Control engineering (in the broad sense of the term) has become an important enabling technology in many areas of medicine. Prominent examples include the artificial pancreas, closed-loop anesthesia, and personalized drug dosing strategies in neurology, oncology, endocrinology, and psychiatry. It is a testament to the power of control systems that allow individualizing treatment by providing mechanisms for linking treatment goals to treatment regimens, thus achieving a desired therapeutic effect. Consequently, the arrival of control systems engineering to the clinic enables the visionary concept of "treat the patient, not the disease" technologically and economically feasible

Jayawardhana, Bayu

Medvedev, Alexander

Mercere, Guillaume

Rivera, Daniel E.

Scoglio, Caterina

English

Control engineering (in the broad sense of the term) has become an important enabling technology in many areas of medicine. Prominent examples include the artificial pancreas, closed-loop anesthesia, and personalized drug dosing strategies in neurology, oncology, endocrinology, and psychiatry. It is a testament to the power of control systems that allow individualizing treatment by providing mechanisms for linking treatment goals to treatment regimens, thus achieving a desired therapeutic effect. Consequently, the arrival of control systems engineering to the clinic enables the visionary concept of "treat the patient, not the disease" technologically and economically feasible.</p

ARTS repository - University of Groningen

   University of GroningenForeword Identification and Control in Biomedical ApplicationsMercere, Guillaume; Medvedev, Alexander; Rivera, Daniel E.; Scoglio, Caterina;Jayawardhana, BayuPublished in:IEEE Transactions on Control Systems TechnologyDOI:10.1109/TCST.2019.2957597IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mercere, G., Medvedev, A., Rivera, D. E., Scoglio, C., & Jayawardhana, B. (2020). Foreword Identificationand Control in Biomedical Applications. IEEE Transactions on Control Systems Technology, 28(1), 1-2.Article 8944255. https://doi.org/10.1109/TCST.2019.2957597CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 10-09-2023IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020 1ForewordIdentification and Control inBiomedical ApplicationsCONTROL engineering (in the broad sense of the term)has become an important enabling technology in manyareas of medicine. Prominent examples include the artificialpancreas, closed-loop anesthesia, and personalized drug dosingstrategies in neurology, oncology, endocrinology, and psychia-try. It is a testament to the power of control systems that allowindividualizing treatment by providing mechanisms for linkingtreatment goals to treatment regimens, thus achieving a desiredtherapeutic effect. Consequently, the arrival of control systemsengineering to the clinic enables the visionary concept of “treatthe patient, not the disease” technologically and economicallyfeasible.This Special Issue is a joint effort of the three IEEE ControlSystems Society Technical Committees: System Identificationand Adaptive Control, Systems Biology, and Healthcare andMedical Systems. It addresses the challenges of applying feed-back/feedforward control and related technologies in livingorganisms, with an emphasis on biomedicine. Mathematicalmodels of systems in the human body can be used to uncoverand quantify dynamical contributions to health and disease.The insights obtained are expected to be instrumental in thedesign of innovative and agile tools for modeling, analysis,design, and optimization of biomedical systems.Altogether, 22 articles are included in this Special Issueand cover a wide field within biomedicine and behavioralmedicine, thus reflecting a diversity of topics where controland dynamical systems’ modeling have an important role.Mathematically, describing and controlling metabolic activityis a popular research area motivated by the need to understandthe dynamics of metabolism and thus to improve the treatmentof chronic conditions, such as diabetes. Applications to neu-roscience and neurology are gaining increasing acceptance,given the rise of research initiatives in America and Europetargeting the human brain, as well as the increasing prevalenceof neurological disorders in the world’s population. Articlesin the issue address a diversity of illnesses, conditions, andtreatments (e.g., Type 1 and Type 2 diabetes, Parkinson’s dis-ease, deep brain stimulation, myocardial infarction, influenzaand epidemic disease treatment, tumor therapy, weight control,neuromodulation, muscular rehabilitation, physical activity,and behavioral medicine), control strategies (e.g., feedback,feedforward, model predictive, nonlinear, data-centric/data-driven, and impulsive), and modeling/estimation approachesDigital Object Identifier 10.1109/TCST.2019.2957597(e.g., fractional models, Markov models, prediction-error esti-mation, semiphysical identification, and Kalman filtering).The preponderance of the approaches considered in this Spe-cial Issue is tested either with clinical data or via rigoroussimulation.This Special Issue is spearheaded by articles devoted tometabolic illnesses, artificial pancreas, and glucose control,addressing both Type 1 and Type 2 diabetes. Yu et al.study the problem of glucose prediction using sparse kernelfiltering algorithms. Computationally efficient adaptive andrecursive filtering algorithms are developed to provide onlinepredictions of blood glucose in patients with Type 1 dia-betes. Di Ferdinando et al. describe a nonlinear sampled-datafeedback controller for the glucose–insulin system meaningfulfor Type 2 diabetes. While less severe than Type 1, Type 2diabetes is more prevalent. Data-driven anomaly recognitionfor model-free fault detection is the focus of Meneghetti et al.’swork, while data-driven disturbance estimation and controlapplied to glucose regulation is the primary emphasis of thework by Novara et al. The section is rounded out by thework of Guo et al. in examining the problem of estimatingenergy intake (essential to weight control and understandingmetabolic function), which is studied in the context of weightgain in pregnancy for an overweight/obese population.Three articles address issues with modeling and controlof infectious diseases. The article by Paré et al. presentsa rigorous analysis of spreading models on nontrivial net-works. Several models in the literature are compared, andone is studied analytically, along with two real-world datasets that are used to validate one well-known approximationof the susceptible-infected-susceptible model. A discrete-timeinverse optimal impulsive control scheme is proposed by Her-nandez Mejia et al. in the second article to address the antiviraltreatment scheduling problem. The authors develop a modelwhose parameters are tuned using data from clinical trials andpresent results supporting the effectiveness and robustness ofthe proposed optimal antiviral treatment schedule. The articleby Cacace et al. develops a novel optimal control algorithmapplied to the design of a feedback antiangiogenic tumortherapy. Lack of data is mitigated by the use of a state observerthat exploits recent measurements. The algorithm is validatedon a virtual population through a rigorous set of simulations.Neuroscience and neurological efforts are featured in fourarticles. These include the work of Olsson and Medvedevon nonparametric tremor quantification with smartphones; theapproach leads to significant improvements compared with1063-6536 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.2 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020standard spectral analysis. Fractional-order modeling for aso-called “phantom” electroencephalographic (EEG) systemis examined in the work of Besancon et al., which can beused as a support to analyze dynamical behaviors in standardEEG measurement processes. Shakarami et al. rely on novelformulations of the Kalman filter for intraoperative brain shiftestimation using atlas of brain deformations. Finally, Cuboand Medvedev examine online tissue conductivity estimationin the context of deep brain tissue stimulation, a therapy usedfor alleviating symptoms associated with Parkinson’s disease.Cardiac and respiratory illnesses represent the next group ofarticles. The work of Hunnekens et al. discusses a variable-gain control strategy for mechanical ventilators in respira-tory systems. System identification using fractional models isagain examined in this issue, in the article by Victor et al.,as an approach to modeling thermal transfer in the lungs.Meanwhile, a mechanistic framework sets the underpinningsfor modeling and system identification of biomarkers that arereleased during acute mycardial infarction, as described in thework in Procopio et al.; this work is the first (to the best of ourknowledge) to account for the dynamics of cardiac biomarkerrelease.Two articles form part of the section on systems biology.This includes the work by Bianconi et al. on cancer sys-tems biology, who present an innovative Bayesian methodcalled conditional robust calibration (CRC) for model cal-ibration and identifiability analysis in this problem space.Multiobjective system identification of a feedback syntheticgene circuit, a highly nonlinear, stochastic problem, is exam-ined by Boada et al. Two articles, meanwhile, are associ-ated with muscular applications. van de Ruit et al. rely onboth nonparametric and parametric kernel-based regression tomonitor time-varying joint impedance during a force task;the approaches yield complementary insights. The work byPaz et al. uses stochastic extremum seeking to adapt the gainsof a PID control law for functional neuromuscular electricalstimulation.A final group of articles considers closed-loop anesthesia,physical activity, and behavior change. The article by vanHeusden et al. uses multi-input single-output (MISO) closed-loop identification to take the response to disturbances duringinduction of anesthesia into account. From this approach, theauthors identify and validate several models that describe theblood pressure and the depth-of-hypnosis response to propofolinfusion for patients at risk of cardiovascular suppression.The article by Lopes dos Santos et al. reformulates andadapts a class of identification algorithms known as matchable-observable linear identification (MOLI) to estimate lineartime-invariant models for Just Walk, a behavioral interventiondesigned on the basis of system identification principles toincrease physical activity in sedentary adults. These resultsprovide insights into the individual and the intervention, whichcan be used to improve the design of future studies. The finalarticle in this Special Issue is focused on social cognitivetheory (SCT), an important theory meaningful in many appli-cation settings, including the health behavior. Martin et al.postulate a dynamical systems model for SCT that can beused to predict changes in health behavior over time and canbe validated through “intensive” data streams generated bywearables and related devices that form an increasing partof mobile health (mHealth). These models, in turn, can leadto novel forms of optimized interventions based on controlsystems’ engineering principles.GUILLAUME MERCÈRE, Lead Guest EditorUniversité de Poitiers86073 Poitiers, Francee-mail: guillaume.mercere@univ-poitiers.frALEXANDER MEDVEDEV, Guest EditorUppsala University752 37 Uppsala, Swedene-mail: alexander.medvedev@it.uu.seDANIEL E. RIVERA, Guest EditorSchool for Engineering of Matter, Transport, andEnergy MailArizona State UniversityTempe, AZ 85287-6106 USAe-mail: daniel.rivera@asu.eduCATERINA SCOGLIO, Guest EditorDepartment of Electrical and Computer EngineeringCollege of EngineeringKansas State UniversityManhattan, KS 66506 USAe-mail: caterina@ksu.eduBAYU JAYAWARDHANA, Guest EditorEngineering and Technology Institute GroningenUniversity of Groningen9747AG Groningen, The Netherlandse-mail: b.jayawardhana@rug.nl

Foreword Identification and Control in Biomedical Applications

University of Groningen

   University of GroningenForeword Identification and Control in Biomedical ApplicationsMercere, Guillaume; Medvedev, Alexander; Rivera, Daniel E.; Scoglio, Caterina;Jayawardhana, BayuPublished in:IEEE Transactions on Control Systems TechnologyDOI:10.1109/TCST.2019.2957597IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mercere, G., Medvedev, A., Rivera, D. E., Scoglio, C., & Jayawardhana, B. (2020). Foreword Identificationand Control in Biomedical Applications. IEEE Transactions on Control Systems Technology, 28(1), 1-2.[8944255]. https://doi.org/10.1109/TCST.2019.2957597CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 29-04-2023IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020 1ForewordIdentification and Control inBiomedical ApplicationsCONTROL engineering (in the broad sense of the term)has become an important enabling technology in manyareas of medicine. Prominent examples include the artificialpancreas, closed-loop anesthesia, and personalized drug dosingstrategies in neurology, oncology, endocrinology, and psychia-try. It is a testament to the power of control systems that allowindividualizing treatment by providing mechanisms for linkingtreatment goals to treatment regimens, thus achieving a desiredtherapeutic effect. Consequently, the arrival of control systemsengineering to the clinic enables the visionary concept of “treatthe patient, not the disease” technologically and economicallyfeasible.This Special Issue is a joint effort of the three IEEE ControlSystems Society Technical Committees: System Identificationand Adaptive Control, Systems Biology, and Healthcare andMedical Systems. It addresses the challenges of applying feed-back/feedforward control and related technologies in livingorganisms, with an emphasis on biomedicine. Mathematicalmodels of systems in the human body can be used to uncoverand quantify dynamical contributions to health and disease.The insights obtained are expected to be instrumental in thedesign of innovative and agile tools for modeling, analysis,design, and optimization of biomedical systems.Altogether, 22 articles are included in this Special Issueand cover a wide field within biomedicine and behavioralmedicine, thus reflecting a diversity of topics where controland dynamical systems’ modeling have an important role.Mathematically, describing and controlling metabolic activityis a popular research area motivated by the need to understandthe dynamics of metabolism and thus to improve the treatmentof chronic conditions, such as diabetes. Applications to neu-roscience and neurology are gaining increasing acceptance,given the rise of research initiatives in America and Europetargeting the human brain, as well as the increasing prevalenceof neurological disorders in the world’s population. Articlesin the issue address a diversity of illnesses, conditions, andtreatments (e.g., Type 1 and Type 2 diabetes, Parkinson’s dis-ease, deep brain stimulation, myocardial infarction, influenzaand epidemic disease treatment, tumor therapy, weight control,neuromodulation, muscular rehabilitation, physical activity,and behavioral medicine), control strategies (e.g., feedback,feedforward, model predictive, nonlinear, data-centric/data-driven, and impulsive), and modeling/estimation approachesDigital Object Identifier 10.1109/TCST.2019.2957597(e.g., fractional models, Markov models, prediction-error esti-mation, semiphysical identification, and Kalman filtering).The preponderance of the approaches considered in this Spe-cial Issue is tested either with clinical data or via rigoroussimulation.This Special Issue is spearheaded by articles devoted tometabolic illnesses, artificial pancreas, and glucose control,addressing both Type 1 and Type 2 diabetes. Yu et al.study the problem of glucose prediction using sparse kernelfiltering algorithms. Computationally efficient adaptive andrecursive filtering algorithms are developed to provide onlinepredictions of blood glucose in patients with Type 1 dia-betes. Di Ferdinando et al. describe a nonlinear sampled-datafeedback controller for the glucose–insulin system meaningfulfor Type 2 diabetes. While less severe than Type 1, Type 2diabetes is more prevalent. Data-driven anomaly recognitionfor model-free fault detection is the focus of Meneghetti et al.’swork, while data-driven disturbance estimation and controlapplied to glucose regulation is the primary emphasis of thework by Novara et al. The section is rounded out by thework of Guo et al. in examining the problem of estimatingenergy intake (essential to weight control and understandingmetabolic function), which is studied in the context of weightgain in pregnancy for an overweight/obese population.Three articles address issues with modeling and controlof infectious diseases. The article by Paré et al. presentsa rigorous analysis of spreading models on nontrivial net-works. Several models in the literature are compared, andone is studied analytically, along with two real-world datasets that are used to validate one well-known approximationof the susceptible-infected-susceptible model. A discrete-timeinverse optimal impulsive control scheme is proposed by Her-nandez Mejia et al. in the second article to address the antiviraltreatment scheduling problem. The authors develop a modelwhose parameters are tuned using data from clinical trials andpresent results supporting the effectiveness and robustness ofthe proposed optimal antiviral treatment schedule. The articleby Cacace et al. develops a novel optimal control algorithmapplied to the design of a feedback antiangiogenic tumortherapy. Lack of data is mitigated by the use of a state observerthat exploits recent measurements. The algorithm is validatedon a virtual population through a rigorous set of simulations.Neuroscience and neurological efforts are featured in fourarticles. These include the work of Olsson and Medvedevon nonparametric tremor quantification with smartphones; theapproach leads to significant improvements compared with1063-6536 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.2 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020standard spectral analysis. Fractional-order modeling for aso-called “phantom” electroencephalographic (EEG) systemis examined in the work of Besancon et al., which can beused as a support to analyze dynamical behaviors in standardEEG measurement processes. Shakarami et al. rely on novelformulations of the Kalman filter for intraoperative brain shiftestimation using atlas of brain deformations. Finally, Cuboand Medvedev examine online tissue conductivity estimationin the context of deep brain tissue stimulation, a therapy usedfor alleviating symptoms associated with Parkinson’s disease.Cardiac and respiratory illnesses represent the next group ofarticles. The work of Hunnekens et al. discusses a variable-gain control strategy for mechanical ventilators in respira-tory systems. System identification using fractional models isagain examined in this issue, in the article by Victor et al.,as an approach to modeling thermal transfer in the lungs.Meanwhile, a mechanistic framework sets the underpinningsfor modeling and system identification of biomarkers that arereleased during acute mycardial infarction, as described in thework in Procopio et al.; this work is the first (to the best of ourknowledge) to account for the dynamics of cardiac biomarkerrelease.Two articles form part of the section on systems biology.This includes the work by Bianconi et al. on cancer sys-tems biology, who present an innovative Bayesian methodcalled conditional robust calibration (CRC) for model cal-ibration and identifiability analysis in this problem space.Multiobjective system identification of a feedback syntheticgene circuit, a highly nonlinear, stochastic problem, is exam-ined by Boada et al. Two articles, meanwhile, are associ-ated with muscular applications. van de Ruit et al. rely onboth nonparametric and parametric kernel-based regression tomonitor time-varying joint impedance during a force task;the approaches yield complementary insights. The work byPaz et al. uses stochastic extremum seeking to adapt the gainsof a PID control law for functional neuromuscular electricalstimulation.A final group of articles considers closed-loop anesthesia,physical activity, and behavior change. The article by vanHeusden et al. uses multi-input single-output (MISO) closed-loop identification to take the response to disturbances duringinduction of anesthesia into account. From this approach, theauthors identify and validate several models that describe theblood pressure and the depth-of-hypnosis response to propofolinfusion for patients at risk of cardiovascular suppression.The article by Lopes dos Santos et al. reformulates andadapts a class of identification algorithms known as matchable-observable linear identification (MOLI) to estimate lineartime-invariant models for Just Walk, a behavioral interventiondesigned on the basis of system identification principles toincrease physical activity in sedentary adults. These resultsprovide insights into the individual and the intervention, whichcan be used to improve the design of future studies. The finalarticle in this Special Issue is focused on social cognitivetheory (SCT), an important theory meaningful in many appli-cation settings, including the health behavior. Martin et al.postulate a dynamical systems model for SCT that can beused to predict changes in health behavior over time and canbe validated through “intensive” data streams generated bywearables and related devices that form an increasing partof mobile health (mHealth). These models, in turn, can leadto novel forms of optimized interventions based on controlsystems’ engineering principles.GUILLAUME MERCÈRE, Lead Guest EditorUniversité de Poitiers86073 Poitiers, Francee-mail: guillaume.mercere@univ-poitiers.frALEXANDER MEDVEDEV, Guest EditorUppsala University752 37 Uppsala, Swedene-mail: alexander.medvedev@it.uu.seDANIEL E. RIVERA, Guest EditorSchool for Engineering of Matter, Transport, andEnergy MailArizona State UniversityTempe, AZ 85287-6106 USAe-mail: daniel.rivera@asu.eduCATERINA SCOGLIO, Guest EditorDepartment of Electrical and Computer EngineeringCollege of EngineeringKansas State UniversityManhattan, KS 66506 USAe-mail: caterina@ksu.eduBAYU JAYAWARDHANA, Guest EditorEngineering and Technology Institute GroningenUniversity of Groningen9747AG Groningen, The Netherlandse-mail: b.jayawardhana@rug.nl

Dissertations of the University of Groningen

   University of GroningenForeword Identification and Control in Biomedical ApplicationsMercere, Guillaume; Medvedev, Alexander; Rivera, Daniel E.; Scoglio, Caterina;Jayawardhana, BayuPublished in:IEEE Transactions on Control Systems TechnologyDOI:10.1109/TCST.2019.2957597IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mercere, G., Medvedev, A., Rivera, D. E., Scoglio, C., & Jayawardhana, B. (2020). Foreword Identificationand Control in Biomedical Applications. IEEE Transactions on Control Systems Technology, 28(1), 1-2.Article 8944255. https://doi.org/10.1109/TCST.2019.2957597CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 31-10-2023IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020 1ForewordIdentification and Control inBiomedical ApplicationsCONTROL engineering (in the broad sense of the term)has become an important enabling technology in manyareas of medicine. Prominent examples include the artificialpancreas, closed-loop anesthesia, and personalized drug dosingstrategies in neurology, oncology, endocrinology, and psychia-try. It is a testament to the power of control systems that allowindividualizing treatment by providing mechanisms for linkingtreatment goals to treatment regimens, thus achieving a desiredtherapeutic effect. Consequently, the arrival of control systemsengineering to the clinic enables the visionary concept of “treatthe patient, not the disease” technologically and economicallyfeasible.This Special Issue is a joint effort of the three IEEE ControlSystems Society Technical Committees: System Identificationand Adaptive Control, Systems Biology, and Healthcare andMedical Systems. It addresses the challenges of applying feed-back/feedforward control and related technologies in livingorganisms, with an emphasis on biomedicine. Mathematicalmodels of systems in the human body can be used to uncoverand quantify dynamical contributions to health and disease.The insights obtained are expected to be instrumental in thedesign of innovative and agile tools for modeling, analysis,design, and optimization of biomedical systems.Altogether, 22 articles are included in this Special Issueand cover a wide field within biomedicine and behavioralmedicine, thus reflecting a diversity of topics where controland dynamical systems’ modeling have an important role.Mathematically, describing and controlling metabolic activityis a popular research area motivated by the need to understandthe dynamics of metabolism and thus to improve the treatmentof chronic conditions, such as diabetes. Applications to neu-roscience and neurology are gaining increasing acceptance,given the rise of research initiatives in America and Europetargeting the human brain, as well as the increasing prevalenceof neurological disorders in the world’s population. Articlesin the issue address a diversity of illnesses, conditions, andtreatments (e.g., Type 1 and Type 2 diabetes, Parkinson’s dis-ease, deep brain stimulation, myocardial infarction, influenzaand epidemic disease treatment, tumor therapy, weight control,neuromodulation, muscular rehabilitation, physical activity,and behavioral medicine), control strategies (e.g., feedback,feedforward, model predictive, nonlinear, data-centric/data-driven, and impulsive), and modeling/estimation approachesDigital Object Identifier 10.1109/TCST.2019.2957597(e.g., fractional models, Markov models, prediction-error esti-mation, semiphysical identification, and Kalman filtering).The preponderance of the approaches considered in this Spe-cial Issue is tested either with clinical data or via rigoroussimulation.This Special Issue is spearheaded by articles devoted tometabolic illnesses, artificial pancreas, and glucose control,addressing both Type 1 and Type 2 diabetes. Yu et al.study the problem of glucose prediction using sparse kernelfiltering algorithms. Computationally efficient adaptive andrecursive filtering algorithms are developed to provide onlinepredictions of blood glucose in patients with Type 1 dia-betes. Di Ferdinando et al. describe a nonlinear sampled-datafeedback controller for the glucose–insulin system meaningfulfor Type 2 diabetes. While less severe than Type 1, Type 2diabetes is more prevalent. Data-driven anomaly recognitionfor model-free fault detection is the focus of Meneghetti et al.’swork, while data-driven disturbance estimation and controlapplied to glucose regulation is the primary emphasis of thework by Novara et al. The section is rounded out by thework of Guo et al. in examining the problem of estimatingenergy intake (essential to weight control and understandingmetabolic function), which is studied in the context of weightgain in pregnancy for an overweight/obese population.Three articles address issues with modeling and controlof infectious diseases. The article by Paré et al. presentsa rigorous analysis of spreading models on nontrivial net-works. Several models in the literature are compared, andone is studied analytically, along with two real-world datasets that are used to validate one well-known approximationof the susceptible-infected-susceptible model. A discrete-timeinverse optimal impulsive control scheme is proposed by Her-nandez Mejia et al. in the second article to address the antiviraltreatment scheduling problem. The authors develop a modelwhose parameters are tuned using data from clinical trials andpresent results supporting the effectiveness and robustness ofthe proposed optimal antiviral treatment schedule. The articleby Cacace et al. develops a novel optimal control algorithmapplied to the design of a feedback antiangiogenic tumortherapy. Lack of data is mitigated by the use of a state observerthat exploits recent measurements. The algorithm is validatedon a virtual population through a rigorous set of simulations.Neuroscience and neurological efforts are featured in fourarticles. These include the work of Olsson and Medvedevon nonparametric tremor quantification with smartphones; theapproach leads to significant improvements compared with1063-6536 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.2 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020standard spectral analysis. Fractional-order modeling for aso-called “phantom” electroencephalographic (EEG) systemis examined in the work of Besancon et al., which can beused as a support to analyze dynamical behaviors in standardEEG measurement processes. Shakarami et al. rely on novelformulations of the Kalman filter for intraoperative brain shiftestimation using atlas of brain deformations. Finally, Cuboand Medvedev examine online tissue conductivity estimationin the context of deep brain tissue stimulation, a therapy usedfor alleviating symptoms associated with Parkinson’s disease.Cardiac and respiratory illnesses represent the next group ofarticles. The work of Hunnekens et al. discusses a variable-gain control strategy for mechanical ventilators in respira-tory systems. System identification using fractional models isagain examined in this issue, in the article by Victor et al.,as an approach to modeling thermal transfer in the lungs.Meanwhile, a mechanistic framework sets the underpinningsfor modeling and system identification of biomarkers that arereleased during acute mycardial infarction, as described in thework in Procopio et al.; this work is the first (to the best of ourknowledge) to account for the dynamics of cardiac biomarkerrelease.Two articles form part of the section on systems biology.This includes the work by Bianconi et al. on cancer sys-tems biology, who present an innovative Bayesian methodcalled conditional robust calibration (CRC) for model cal-ibration and identifiability analysis in this problem space.Multiobjective system identification of a feedback syntheticgene circuit, a highly nonlinear, stochastic problem, is exam-ined by Boada et al. Two articles, meanwhile, are associ-ated with muscular applications. van de Ruit et al. rely onboth nonparametric and parametric kernel-based regression tomonitor time-varying joint impedance during a force task;the approaches yield complementary insights. The work byPaz et al. uses stochastic extremum seeking to adapt the gainsof a PID control law for functional neuromuscular electricalstimulation.A final group of articles considers closed-loop anesthesia,physical activity, and behavior change. The article by vanHeusden et al. uses multi-input single-output (MISO) closed-loop identification to take the response to disturbances duringinduction of anesthesia into account. From this approach, theauthors identify and validate several models that describe theblood pressure and the depth-of-hypnosis response to propofolinfusion for patients at risk of cardiovascular suppression.The article by Lopes dos Santos et al. reformulates andadapts a class of identification algorithms known as matchable-observable linear identification (MOLI) to estimate lineartime-invariant models for Just Walk, a behavioral interventiondesigned on the basis of system identification principles toincrease physical activity in sedentary adults. These resultsprovide insights into the individual and the intervention, whichcan be used to improve the design of future studies. The finalarticle in this Special Issue is focused on social cognitivetheory (SCT), an important theory meaningful in many appli-cation settings, including the health behavior. Martin et al.postulate a dynamical systems model for SCT that can beused to predict changes in health behavior over time and canbe validated through “intensive” data streams generated bywearables and related devices that form an increasing partof mobile health (mHealth). These models, in turn, can leadto novel forms of optimized interventions based on controlsystems’ engineering principles.GUILLAUME MERCÈRE, Lead Guest EditorUniversité de Poitiers86073 Poitiers, Francee-mail: guillaume.mercere@univ-poitiers.frALEXANDER MEDVEDEV, Guest EditorUppsala University752 37 Uppsala, Swedene-mail: alexander.medvedev@it.uu.seDANIEL E. RIVERA, Guest EditorSchool for Engineering of Matter, Transport, andEnergy MailArizona State UniversityTempe, AZ 85287-6106 USAe-mail: daniel.rivera@asu.eduCATERINA SCOGLIO, Guest EditorDepartment of Electrical and Computer EngineeringCollege of EngineeringKansas State UniversityManhattan, KS 66506 USAe-mail: caterina@ksu.eduBAYU JAYAWARDHANA, Guest EditorEngineering and Technology Institute GroningenUniversity of Groningen9747AG Groningen, The Netherlandse-mail: b.jayawardhana@rug.nl

NARCIS 

   University of GroningenForeword Identification and Control in Biomedical ApplicationsMercere, Guillaume; Medvedev, Alexander; Rivera, Daniel E.; Scoglio, Caterina;Jayawardhana, BayuPublished in:IEEE Transactions on Control Systems TechnologyDOI:10.1109/TCST.2019.2957597IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mercere, G., Medvedev, A., Rivera, D. E., Scoglio, C., & Jayawardhana, B. (2020). Foreword Identificationand Control in Biomedical Applications. IEEE Transactions on Control Systems Technology, 28(1), 1-2.[8944255]. https://doi.org/10.1109/TCST.2019.2957597CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 19-11-2022IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020 1ForewordIdentification and Control inBiomedical ApplicationsCONTROL engineering (in the broad sense of the term)has become an important enabling technology in manyareas of medicine. Prominent examples include the artificialpancreas, closed-loop anesthesia, and personalized drug dosingstrategies in neurology, oncology, endocrinology, and psychia-try. It is a testament to the power of control systems that allowindividualizing treatment by providing mechanisms for linkingtreatment goals to treatment regimens, thus achieving a desiredtherapeutic effect. Consequently, the arrival of control systemsengineering to the clinic enables the visionary concept of “treatthe patient, not the disease” technologically and economicallyfeasible.This Special Issue is a joint effort of the three IEEE ControlSystems Society Technical Committees: System Identificationand Adaptive Control, Systems Biology, and Healthcare andMedical Systems. It addresses the challenges of applying feed-back/feedforward control and related technologies in livingorganisms, with an emphasis on biomedicine. Mathematicalmodels of systems in the human body can be used to uncoverand quantify dynamical contributions to health and disease.The insights obtained are expected to be instrumental in thedesign of innovative and agile tools for modeling, analysis,design, and optimization of biomedical systems.Altogether, 22 articles are included in this Special Issueand cover a wide field within biomedicine and behavioralmedicine, thus reflecting a diversity of topics where controland dynamical systems’ modeling have an important role.Mathematically, describing and controlling metabolic activityis a popular research area motivated by the need to understandthe dynamics of metabolism and thus to improve the treatmentof chronic conditions, such as diabetes. Applications to neu-roscience and neurology are gaining increasing acceptance,given the rise of research initiatives in America and Europetargeting the human brain, as well as the increasing prevalenceof neurological disorders in the world’s population. Articlesin the issue address a diversity of illnesses, conditions, andtreatments (e.g., Type 1 and Type 2 diabetes, Parkinson’s dis-ease, deep brain stimulation, myocardial infarction, influenzaand epidemic disease treatment, tumor therapy, weight control,neuromodulation, muscular rehabilitation, physical activity,and behavioral medicine), control strategies (e.g., feedback,feedforward, model predictive, nonlinear, data-centric/data-driven, and impulsive), and modeling/estimation approachesDigital Object Identifier 10.1109/TCST.2019.2957597(e.g., fractional models, Markov models, prediction-error esti-mation, semiphysical identification, and Kalman filtering).The preponderance of the approaches considered in this Spe-cial Issue is tested either with clinical data or via rigoroussimulation.This Special Issue is spearheaded by articles devoted tometabolic illnesses, artificial pancreas, and glucose control,addressing both Type 1 and Type 2 diabetes. Yu et al.study the problem of glucose prediction using sparse kernelfiltering algorithms. Computationally efficient adaptive andrecursive filtering algorithms are developed to provide onlinepredictions of blood glucose in patients with Type 1 dia-betes. Di Ferdinando et al. describe a nonlinear sampled-datafeedback controller for the glucose–insulin system meaningfulfor Type 2 diabetes. While less severe than Type 1, Type 2diabetes is more prevalent. Data-driven anomaly recognitionfor model-free fault detection is the focus of Meneghetti et al.’swork, while data-driven disturbance estimation and controlapplied to glucose regulation is the primary emphasis of thework by Novara et al. The section is rounded out by thework of Guo et al. in examining the problem of estimatingenergy intake (essential to weight control and understandingmetabolic function), which is studied in the context of weightgain in pregnancy for an overweight/obese population.Three articles address issues with modeling and controlof infectious diseases. The article by Paré et al. presentsa rigorous analysis of spreading models on nontrivial net-works. Several models in the literature are compared, andone is studied analytically, along with two real-world datasets that are used to validate one well-known approximationof the susceptible-infected-susceptible model. A discrete-timeinverse optimal impulsive control scheme is proposed by Her-nandez Mejia et al. in the second article to address the antiviraltreatment scheduling problem. The authors develop a modelwhose parameters are tuned using data from clinical trials andpresent results supporting the effectiveness and robustness ofthe proposed optimal antiviral treatment schedule. The articleby Cacace et al. develops a novel optimal control algorithmapplied to the design of a feedback antiangiogenic tumortherapy. Lack of data is mitigated by the use of a state observerthat exploits recent measurements. The algorithm is validatedon a virtual population through a rigorous set of simulations.Neuroscience and neurological efforts are featured in fourarticles. These include the work of Olsson and Medvedevon nonparametric tremor quantification with smartphones; theapproach leads to significant improvements compared with1063-6536 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.2 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020standard spectral analysis. Fractional-order modeling for aso-called “phantom” electroencephalographic (EEG) systemis examined in the work of Besancon et al., which can beused as a support to analyze dynamical behaviors in standardEEG measurement processes. Shakarami et al. rely on novelformulations of the Kalman filter for intraoperative brain shiftestimation using atlas of brain deformations. Finally, Cuboand Medvedev examine online tissue conductivity estimationin the context of deep brain tissue stimulation, a therapy usedfor alleviating symptoms associated with Parkinson’s disease.Cardiac and respiratory illnesses represent the next group ofarticles. The work of Hunnekens et al. discusses a variable-gain control strategy for mechanical ventilators in respira-tory systems. System identification using fractional models isagain examined in this issue, in the article by Victor et al.,as an approach to modeling thermal transfer in the lungs.Meanwhile, a mechanistic framework sets the underpinningsfor modeling and system identification of biomarkers that arereleased during acute mycardial infarction, as described in thework in Procopio et al.; this work is the first (to the best of ourknowledge) to account for the dynamics of cardiac biomarkerrelease.Two articles form part of the section on systems biology.This includes the work by Bianconi et al. on cancer sys-tems biology, who present an innovative Bayesian methodcalled conditional robust calibration (CRC) for model cal-ibration and identifiability analysis in this problem space.Multiobjective system identification of a feedback syntheticgene circuit, a highly nonlinear, stochastic problem, is exam-ined by Boada et al. Two articles, meanwhile, are associ-ated with muscular applications. van de Ruit et al. rely onboth nonparametric and parametric kernel-based regression tomonitor time-varying joint impedance during a force task;the approaches yield complementary insights. The work byPaz et al. uses stochastic extremum seeking to adapt the gainsof a PID control law for functional neuromuscular electricalstimulation.A final group of articles considers closed-loop anesthesia,physical activity, and behavior change. The article by vanHeusden et al. uses multi-input single-output (MISO) closed-loop identification to take the response to disturbances duringinduction of anesthesia into account. From this approach, theauthors identify and validate several models that describe theblood pressure and the depth-of-hypnosis response to propofolinfusion for patients at risk of cardiovascular suppression.The article by Lopes dos Santos et al. reformulates andadapts a class of identification algorithms known as matchable-observable linear identification (MOLI) to estimate lineartime-invariant models for Just Walk, a behavioral interventiondesigned on the basis of system identification principles toincrease physical activity in sedentary adults. These resultsprovide insights into the individual and the intervention, whichcan be used to improve the design of future studies. The finalarticle in this Special Issue is focused on social cognitivetheory (SCT), an important theory meaningful in many appli-cation settings, including the health behavior. Martin et al.postulate a dynamical systems model for SCT that can beused to predict changes in health behavior over time and canbe validated through “intensive” data streams generated bywearables and related devices that form an increasing partof mobile health (mHealth). These models, in turn, can leadto novel forms of optimized interventions based on controlsystems’ engineering principles.GUILLAUME MERCÈRE, Lead Guest EditorUniversité de Poitiers86073 Poitiers, Francee-mail: guillaume.mercere@univ-poitiers.frALEXANDER MEDVEDEV, Guest EditorUppsala University752 37 Uppsala, Swedene-mail: alexander.medvedev@it.uu.seDANIEL E. RIVERA, Guest EditorSchool for Engineering of Matter, Transport, andEnergy MailArizona State UniversityTempe, AZ 85287-6106 USAe-mail: daniel.rivera@asu.eduCATERINA SCOGLIO, Guest EditorDepartment of Electrical and Computer EngineeringCollege of EngineeringKansas State UniversityManhattan, KS 66506 USAe-mail: caterina@ksu.eduBAYU JAYAWARDHANA, Guest EditorEngineering and Technology Institute GroningenUniversity of Groningen9747AG Groningen, The Netherlandse-mail: b.jayawardhana@rug.nl

Proceedings - University of Groningen

   University of GroningenForeword Identification and Control in Biomedical ApplicationsMercere, Guillaume; Medvedev, Alexander; Rivera, Daniel E.; Scoglio, Caterina;Jayawardhana, BayuPublished in:IEEE Transactions on Control Systems TechnologyDOI:10.1109/TCST.2019.2957597IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mercere, G., Medvedev, A., Rivera, D. E., Scoglio, C., & Jayawardhana, B. (2020). Foreword Identificationand Control in Biomedical Applications. IEEE Transactions on Control Systems Technology, 28(1), 1-2.Article 8944255. https://doi.org/10.1109/TCST.2019.2957597CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 01-02-2024IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020 1ForewordIdentification and Control inBiomedical ApplicationsCONTROL engineering (in the broad sense of the term)has become an important enabling technology in manyareas of medicine. Prominent examples include the artificialpancreas, closed-loop anesthesia, and personalized drug dosingstrategies in neurology, oncology, endocrinology, and psychia-try. It is a testament to the power of control systems that allowindividualizing treatment by providing mechanisms for linkingtreatment goals to treatment regimens, thus achieving a desiredtherapeutic effect. Consequently, the arrival of control systemsengineering to the clinic enables the visionary concept of “treatthe patient, not the disease” technologically and economicallyfeasible.This Special Issue is a joint effort of the three IEEE ControlSystems Society Technical Committees: System Identificationand Adaptive Control, Systems Biology, and Healthcare andMedical Systems. It addresses the challenges of applying feed-back/feedforward control and related technologies in livingorganisms, with an emphasis on biomedicine. Mathematicalmodels of systems in the human body can be used to uncoverand quantify dynamical contributions to health and disease.The insights obtained are expected to be instrumental in thedesign of innovative and agile tools for modeling, analysis,design, and optimization of biomedical systems.Altogether, 22 articles are included in this Special Issueand cover a wide field within biomedicine and behavioralmedicine, thus reflecting a diversity of topics where controland dynamical systems’ modeling have an important role.Mathematically, describing and controlling metabolic activityis a popular research area motivated by the need to understandthe dynamics of metabolism and thus to improve the treatmentof chronic conditions, such as diabetes. Applications to neu-roscience and neurology are gaining increasing acceptance,given the rise of research initiatives in America and Europetargeting the human brain, as well as the increasing prevalenceof neurological disorders in the world’s population. Articlesin the issue address a diversity of illnesses, conditions, andtreatments (e.g., Type 1 and Type 2 diabetes, Parkinson’s dis-ease, deep brain stimulation, myocardial infarction, influenzaand epidemic disease treatment, tumor therapy, weight control,neuromodulation, muscular rehabilitation, physical activity,and behavioral medicine), control strategies (e.g., feedback,feedforward, model predictive, nonlinear, data-centric/data-driven, and impulsive), and modeling/estimation approachesDigital Object Identifier 10.1109/TCST.2019.2957597(e.g., fractional models, Markov models, prediction-error esti-mation, semiphysical identification, and Kalman filtering).The preponderance of the approaches considered in this Spe-cial Issue is tested either with clinical data or via rigoroussimulation.This Special Issue is spearheaded by articles devoted tometabolic illnesses, artificial pancreas, and glucose control,addressing both Type 1 and Type 2 diabetes. Yu et al.study the problem of glucose prediction using sparse kernelfiltering algorithms. Computationally efficient adaptive andrecursive filtering algorithms are developed to provide onlinepredictions of blood glucose in patients with Type 1 dia-betes. Di Ferdinando et al. describe a nonlinear sampled-datafeedback controller for the glucose–insulin system meaningfulfor Type 2 diabetes. While less severe than Type 1, Type 2diabetes is more prevalent. Data-driven anomaly recognitionfor model-free fault detection is the focus of Meneghetti et al.’swork, while data-driven disturbance estimation and controlapplied to glucose regulation is the primary emphasis of thework by Novara et al. The section is rounded out by thework of Guo et al. in examining the problem of estimatingenergy intake (essential to weight control and understandingmetabolic function), which is studied in the context of weightgain in pregnancy for an overweight/obese population.Three articles address issues with modeling and controlof infectious diseases. The article by Paré et al. presentsa rigorous analysis of spreading models on nontrivial net-works. Several models in the literature are compared, andone is studied analytically, along with two real-world datasets that are used to validate one well-known approximationof the susceptible-infected-susceptible model. A discrete-timeinverse optimal impulsive control scheme is proposed by Her-nandez Mejia et al. in the second article to address the antiviraltreatment scheduling problem. The authors develop a modelwhose parameters are tuned using data from clinical trials andpresent results supporting the effectiveness and robustness ofthe proposed optimal antiviral treatment schedule. The articleby Cacace et al. develops a novel optimal control algorithmapplied to the design of a feedback antiangiogenic tumortherapy. Lack of data is mitigated by the use of a state observerthat exploits recent measurements. The algorithm is validatedon a virtual population through a rigorous set of simulations.Neuroscience and neurological efforts are featured in fourarticles. These include the work of Olsson and Medvedevon nonparametric tremor quantification with smartphones; theapproach leads to significant improvements compared with1063-6536 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.2 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020standard spectral analysis. Fractional-order modeling for aso-called “phantom” electroencephalographic (EEG) systemis examined in the work of Besancon et al., which can beused as a support to analyze dynamical behaviors in standardEEG measurement processes. Shakarami et al. rely on novelformulations of the Kalman filter for intraoperative brain shiftestimation using atlas of brain deformations. Finally, Cuboand Medvedev examine online tissue conductivity estimationin the context of deep brain tissue stimulation, a therapy usedfor alleviating symptoms associated with Parkinson’s disease.Cardiac and respiratory illnesses represent the next group ofarticles. The work of Hunnekens et al. discusses a variable-gain control strategy for mechanical ventilators in respira-tory systems. System identification using fractional models isagain examined in this issue, in the article by Victor et al.,as an approach to modeling thermal transfer in the lungs.Meanwhile, a mechanistic framework sets the underpinningsfor modeling and system identification of biomarkers that arereleased during acute mycardial infarction, as described in thework in Procopio et al.; this work is the first (to the best of ourknowledge) to account for the dynamics of cardiac biomarkerrelease.Two articles form part of the section on systems biology.This includes the work by Bianconi et al. on cancer sys-tems biology, who present an innovative Bayesian methodcalled conditional robust calibration (CRC) for model cal-ibration and identifiability analysis in this problem space.Multiobjective system identification of a feedback syntheticgene circuit, a highly nonlinear, stochastic problem, is exam-ined by Boada et al. Two articles, meanwhile, are associ-ated with muscular applications. van de Ruit et al. rely onboth nonparametric and parametric kernel-based regression tomonitor time-varying joint impedance during a force task;the approaches yield complementary insights. The work byPaz et al. uses stochastic extremum seeking to adapt the gainsof a PID control law for functional neuromuscular electricalstimulation.A final group of articles considers closed-loop anesthesia,physical activity, and behavior change. The article by vanHeusden et al. uses multi-input single-output (MISO) closed-loop identification to take the response to disturbances duringinduction of anesthesia into account. From this approach, theauthors identify and validate several models that describe theblood pressure and the depth-of-hypnosis response to propofolinfusion for patients at risk of cardiovascular suppression.The article by Lopes dos Santos et al. reformulates andadapts a class of identification algorithms known as matchable-observable linear identification (MOLI) to estimate lineartime-invariant models for Just Walk, a behavioral interventiondesigned on the basis of system identification principles toincrease physical activity in sedentary adults. These resultsprovide insights into the individual and the intervention, whichcan be used to improve the design of future studies. The finalarticle in this Special Issue is focused on social cognitivetheory (SCT), an important theory meaningful in many appli-cation settings, including the health behavior. Martin et al.postulate a dynamical systems model for SCT that can beused to predict changes in health behavior over time and canbe validated through “intensive” data streams generated bywearables and related devices that form an increasing partof mobile health (mHealth). These models, in turn, can leadto novel forms of optimized interventions based on controlsystems’ engineering principles.GUILLAUME MERCÈRE, Lead Guest EditorUniversité de Poitiers86073 Poitiers, Francee-mail: guillaume.mercere@univ-poitiers.frALEXANDER MEDVEDEV, Guest EditorUppsala University752 37 Uppsala, Swedene-mail: alexander.medvedev@it.uu.seDANIEL E. RIVERA, Guest EditorSchool for Engineering of Matter, Transport, andEnergy MailArizona State UniversityTempe, AZ 85287-6106 USAe-mail: daniel.rivera@asu.eduCATERINA SCOGLIO, Guest EditorDepartment of Electrical and Computer EngineeringCollege of EngineeringKansas State UniversityManhattan, KS 66506 USAe-mail: caterina@ksu.eduBAYU JAYAWARDHANA, Guest EditorEngineering and Technology Institute GroningenUniversity of Groningen9747AG Groningen, The Netherlandse-mail: b.jayawardhana@rug.nl

   University of GroningenForeword Identification and Control in Biomedical ApplicationsMercere, Guillaume; Medvedev, Alexander; Rivera, Daniel E.; Scoglio, Caterina;Jayawardhana, BayuPublished in:IEEE Transactions on Control Systems TechnologyDOI:10.1109/TCST.2019.2957597IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mercere, G., Medvedev, A., Rivera, D. E., Scoglio, C., & Jayawardhana, B. (2020). Foreword Identificationand Control in Biomedical Applications. IEEE Transactions on Control Systems Technology, 28(1), 1-2.[8944255]. https://doi.org/10.1109/TCST.2019.2957597CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 04-06-2022IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020 1ForewordIdentification and Control inBiomedical ApplicationsCONTROL engineering (in the broad sense of the term)has become an important enabling technology in manyareas of medicine. Prominent examples include the artificialpancreas, closed-loop anesthesia, and personalized drug dosingstrategies in neurology, oncology, endocrinology, and psychia-try. It is a testament to the power of control systems that allowindividualizing treatment by providing mechanisms for linkingtreatment goals to treatment regimens, thus achieving a desiredtherapeutic effect. Consequently, the arrival of control systemsengineering to the clinic enables the visionary concept of “treatthe patient, not the disease” technologically and economicallyfeasible.This Special Issue is a joint effort of the three IEEE ControlSystems Society Technical Committees: System Identificationand Adaptive Control, Systems Biology, and Healthcare andMedical Systems. It addresses the challenges of applying feed-back/feedforward control and related technologies in livingorganisms, with an emphasis on biomedicine. Mathematicalmodels of systems in the human body can be used to uncoverand quantify dynamical contributions to health and disease.The insights obtained are expected to be instrumental in thedesign of innovative and agile tools for modeling, analysis,design, and optimization of biomedical systems.Altogether, 22 articles are included in this Special Issueand cover a wide field within biomedicine and behavioralmedicine, thus reflecting a diversity of topics where controland dynamical systems’ modeling have an important role.Mathematically, describing and controlling metabolic activityis a popular research area motivated by the need to understandthe dynamics of metabolism and thus to improve the treatmentof chronic conditions, such as diabetes. Applications to neu-roscience and neurology are gaining increasing acceptance,given the rise of research initiatives in America and Europetargeting the human brain, as well as the increasing prevalenceof neurological disorders in the world’s population. Articlesin the issue address a diversity of illnesses, conditions, andtreatments (e.g., Type 1 and Type 2 diabetes, Parkinson’s dis-ease, deep brain stimulation, myocardial infarction, influenzaand epidemic disease treatment, tumor therapy, weight control,neuromodulation, muscular rehabilitation, physical activity,and behavioral medicine), control strategies (e.g., feedback,feedforward, model predictive, nonlinear, data-centric/data-driven, and impulsive), and modeling/estimation approachesDigital Object Identifier 10.1109/TCST.2019.2957597(e.g., fractional models, Markov models, prediction-error esti-mation, semiphysical identification, and Kalman filtering).The preponderance of the approaches considered in this Spe-cial Issue is tested either with clinical data or via rigoroussimulation.This Special Issue is spearheaded by articles devoted tometabolic illnesses, artificial pancreas, and glucose control,addressing both Type 1 and Type 2 diabetes. Yu et al.study the problem of glucose prediction using sparse kernelfiltering algorithms. Computationally efficient adaptive andrecursive filtering algorithms are developed to provide onlinepredictions of blood glucose in patients with Type 1 dia-betes. Di Ferdinando et al. describe a nonlinear sampled-datafeedback controller for the glucose–insulin system meaningfulfor Type 2 diabetes. While less severe than Type 1, Type 2diabetes is more prevalent. Data-driven anomaly recognitionfor model-free fault detection is the focus of Meneghetti et al.’swork, while data-driven disturbance estimation and controlapplied to glucose regulation is the primary emphasis of thework by Novara et al. The section is rounded out by thework of Guo et al. in examining the problem of estimatingenergy intake (essential to weight control and understandingmetabolic function), which is studied in the context of weightgain in pregnancy for an overweight/obese population.Three articles address issues with modeling and controlof infectious diseases. The article by Paré et al. presentsa rigorous analysis of spreading models on nontrivial net-works. Several models in the literature are compared, andone is studied analytically, along with two real-world datasets that are used to validate one well-known approximationof the susceptible-infected-susceptible model. A discrete-timeinverse optimal impulsive control scheme is proposed by Her-nandez Mejia et al. in the second article to address the antiviraltreatment scheduling problem. The authors develop a modelwhose parameters are tuned using data from clinical trials andpresent results supporting the effectiveness and robustness ofthe proposed optimal antiviral treatment schedule. The articleby Cacace et al. develops a novel optimal control algorithmapplied to the design of a feedback antiangiogenic tumortherapy. Lack of data is mitigated by the use of a state observerthat exploits recent measurements. The algorithm is validatedon a virtual population through a rigorous set of simulations.Neuroscience and neurological efforts are featured in fourarticles. These include the work of Olsson and Medvedevon nonparametric tremor quantification with smartphones; theapproach leads to significant improvements compared with1063-6536 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.2 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020standard spectral analysis. Fractional-order modeling for aso-called “phantom” electroencephalographic (EEG) systemis examined in the work of Besancon et al., which can beused as a support to analyze dynamical behaviors in standardEEG measurement processes. Shakarami et al. rely on novelformulations of the Kalman filter for intraoperative brain shiftestimation using atlas of brain deformations. Finally, Cuboand Medvedev examine online tissue conductivity estimationin the context of deep brain tissue stimulation, a therapy usedfor alleviating symptoms associated with Parkinson’s disease.Cardiac and respiratory illnesses represent the next group ofarticles. The work of Hunnekens et al. discusses a variable-gain control strategy for mechanical ventilators in respira-tory systems. System identification using fractional models isagain examined in this issue, in the article by Victor et al.,as an approach to modeling thermal transfer in the lungs.Meanwhile, a mechanistic framework sets the underpinningsfor modeling and system identification of biomarkers that arereleased during acute mycardial infarction, as described in thework in Procopio et al.; this work is the first (to the best of ourknowledge) to account for the dynamics of cardiac biomarkerrelease.Two articles form part of the section on systems biology.This includes the work by Bianconi et al. on cancer sys-tems biology, who present an innovative Bayesian methodcalled conditional robust calibration (CRC) for model cal-ibration and identifiability analysis in this problem space.Multiobjective system identification of a feedback syntheticgene circuit, a highly nonlinear, stochastic problem, is exam-ined by Boada et al. Two articles, meanwhile, are associ-ated with muscular applications. van de Ruit et al. rely onboth nonparametric and parametric kernel-based regression tomonitor time-varying joint impedance during a force task;the approaches yield complementary insights. The work byPaz et al. uses stochastic extremum seeking to adapt the gainsof a PID control law for functional neuromuscular electricalstimulation.A final group of articles considers closed-loop anesthesia,physical activity, and behavior change. The article by vanHeusden et al. uses multi-input single-output (MISO) closed-loop identification to take the response to disturbances duringinduction of anesthesia into account. From this approach, theauthors identify and validate several models that describe theblood pressure and the depth-of-hypnosis response to propofolinfusion for patients at risk of cardiovascular suppression.The article by Lopes dos Santos et al. reformulates andadapts a class of identification algorithms known as matchable-observable linear identification (MOLI) to estimate lineartime-invariant models for Just Walk, a behavioral interventiondesigned on the basis of system identification principles toincrease physical activity in sedentary adults. These resultsprovide insights into the individual and the intervention, whichcan be used to improve the design of future studies. The finalarticle in this Special Issue is focused on social cognitivetheory (SCT), an important theory meaningful in many appli-cation settings, including the health behavior. Martin et al.postulate a dynamical systems model for SCT that can beused to predict changes in health behavior over time and canbe validated through “intensive” data streams generated bywearables and related devices that form an increasing partof mobile health (mHealth). These models, in turn, can leadto novel forms of optimized interventions based on controlsystems’ engineering principles.GUILLAUME MERCÈRE, Lead Guest EditorUniversité de Poitiers86073 Poitiers, Francee-mail: guillaume.mercere@univ-poitiers.frALEXANDER MEDVEDEV, Guest EditorUppsala University752 37 Uppsala, Swedene-mail: alexander.medvedev@it.uu.seDANIEL E. RIVERA, Guest EditorSchool for Engineering of Matter, Transport, andEnergy MailArizona State UniversityTempe, AZ 85287-6106 USAe-mail: daniel.rivera@asu.eduCATERINA SCOGLIO, Guest EditorDepartment of Electrical and Computer EngineeringCollege of EngineeringKansas State UniversityManhattan, KS 66506 USAe-mail: caterina@ksu.eduBAYU JAYAWARDHANA, Guest EditorEngineering and Technology Institute GroningenUniversity of Groningen9747AG Groningen, The Netherlandse-mail: b.jayawardhana@rug.nl

   University of GroningenForeword Identification and Control in Biomedical ApplicationsMercere, Guillaume; Medvedev, Alexander; Rivera, Daniel E.; Scoglio, Caterina;Jayawardhana, BayuPublished in:IEEE Transactions on Control Systems TechnologyDOI:10.1109/TCST.2019.2957597IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mercere, G., Medvedev, A., Rivera, D. E., Scoglio, C., & Jayawardhana, B. (2020). Foreword Identificationand Control in Biomedical Applications. IEEE Transactions on Control Systems Technology, 28(1), 1-2.[8944255]. https://doi.org/10.1109/TCST.2019.2957597CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 05-06-2022IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020 1ForewordIdentification and Control inBiomedical ApplicationsCONTROL engineering (in the broad sense of the term)has become an important enabling technology in manyareas of medicine. Prominent examples include the artificialpancreas, closed-loop anesthesia, and personalized drug dosingstrategies in neurology, oncology, endocrinology, and psychia-try. It is a testament to the power of control systems that allowindividualizing treatment by providing mechanisms for linkingtreatment goals to treatment regimens, thus achieving a desiredtherapeutic effect. Consequently, the arrival of control systemsengineering to the clinic enables the visionary concept of “treatthe patient, not the disease” technologically and economicallyfeasible.This Special Issue is a joint effort of the three IEEE ControlSystems Society Technical Committees: System Identificationand Adaptive Control, Systems Biology, and Healthcare andMedical Systems. It addresses the challenges of applying feed-back/feedforward control and related technologies in livingorganisms, with an emphasis on biomedicine. Mathematicalmodels of systems in the human body can be used to uncoverand quantify dynamical contributions to health and disease.The insights obtained are expected to be instrumental in thedesign of innovative and agile tools for modeling, analysis,design, and optimization of biomedical systems.Altogether, 22 articles are included in this Special Issueand cover a wide field within biomedicine and behavioralmedicine, thus reflecting a diversity of topics where controland dynamical systems’ modeling have an important role.Mathematically, describing and controlling metabolic activityis a popular research area motivated by the need to understandthe dynamics of metabolism and thus to improve the treatmentof chronic conditions, such as diabetes. Applications to neu-roscience and neurology are gaining increasing acceptance,given the rise of research initiatives in America and Europetargeting the human brain, as well as the increasing prevalenceof neurological disorders in the world’s population. Articlesin the issue address a diversity of illnesses, conditions, andtreatments (e.g., Type 1 and Type 2 diabetes, Parkinson’s dis-ease, deep brain stimulation, myocardial infarction, influenzaand epidemic disease treatment, tumor therapy, weight control,neuromodulation, muscular rehabilitation, physical activity,and behavioral medicine), control strategies (e.g., feedback,feedforward, model predictive, nonlinear, data-centric/data-driven, and impulsive), and modeling/estimation approachesDigital Object Identifier 10.1109/TCST.2019.2957597(e.g., fractional models, Markov models, prediction-error esti-mation, semiphysical identification, and Kalman filtering).The preponderance of the approaches considered in this Spe-cial Issue is tested either with clinical data or via rigoroussimulation.This Special Issue is spearheaded by articles devoted tometabolic illnesses, artificial pancreas, and glucose control,addressing both Type 1 and Type 2 diabetes. Yu et al.study the problem of glucose prediction using sparse kernelfiltering algorithms. Computationally efficient adaptive andrecursive filtering algorithms are developed to provide onlinepredictions of blood glucose in patients with Type 1 dia-betes. Di Ferdinando et al. describe a nonlinear sampled-datafeedback controller for the glucose–insulin system meaningfulfor Type 2 diabetes. While less severe than Type 1, Type 2diabetes is more prevalent. Data-driven anomaly recognitionfor model-free fault detection is the focus of Meneghetti et al.’swork, while data-driven disturbance estimation and controlapplied to glucose regulation is the primary emphasis of thework by Novara et al. The section is rounded out by thework of Guo et al. in examining the problem of estimatingenergy intake (essential to weight control and understandingmetabolic function), which is studied in the context of weightgain in pregnancy for an overweight/obese population.Three articles address issues with modeling and controlof infectious diseases. The article by Paré et al. presentsa rigorous analysis of spreading models on nontrivial net-works. Several models in the literature are compared, andone is studied analytically, along with two real-world datasets that are used to validate one well-known approximationof the susceptible-infected-susceptible model. A discrete-timeinverse optimal impulsive control scheme is proposed by Her-nandez Mejia et al. in the second article to address the antiviraltreatment scheduling problem. The authors develop a modelwhose parameters are tuned using data from clinical trials andpresent results supporting the effectiveness and robustness ofthe proposed optimal antiviral treatment schedule. The articleby Cacace et al. develops a novel optimal control algorithmapplied to the design of a feedback antiangiogenic tumortherapy. Lack of data is mitigated by the use of a state observerthat exploits recent measurements. The algorithm is validatedon a virtual population through a rigorous set of simulations.Neuroscience and neurological efforts are featured in fourarticles. These include the work of Olsson and Medvedevon nonparametric tremor quantification with smartphones; theapproach leads to significant improvements compared with1063-6536 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.2 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020standard spectral analysis. Fractional-order modeling for aso-called “phantom” electroencephalographic (EEG) systemis examined in the work of Besancon et al., which can beused as a support to analyze dynamical behaviors in standardEEG measurement processes. Shakarami et al. rely on novelformulations of the Kalman filter for intraoperative brain shiftestimation using atlas of brain deformations. Finally, Cuboand Medvedev examine online tissue conductivity estimationin the context of deep brain tissue stimulation, a therapy usedfor alleviating symptoms associated with Parkinson’s disease.Cardiac and respiratory illnesses represent the next group ofarticles. The work of Hunnekens et al. discusses a variable-gain control strategy for mechanical ventilators in respira-tory systems. System identification using fractional models isagain examined in this issue, in the article by Victor et al.,as an approach to modeling thermal transfer in the lungs.Meanwhile, a mechanistic framework sets the underpinningsfor modeling and system identification of biomarkers that arereleased during acute mycardial infarction, as described in thework in Procopio et al.; this work is the first (to the best of ourknowledge) to account for the dynamics of cardiac biomarkerrelease.Two articles form part of the section on systems biology.This includes the work by Bianconi et al. on cancer sys-tems biology, who present an innovative Bayesian methodcalled conditional robust calibration (CRC) for model cal-ibration and identifiability analysis in this problem space.Multiobjective system identification of a feedback syntheticgene circuit, a highly nonlinear, stochastic problem, is exam-ined by Boada et al. Two articles, meanwhile, are associ-ated with muscular applications. van de Ruit et al. rely onboth nonparametric and parametric kernel-based regression tomonitor time-varying joint impedance during a force task;the approaches yield complementary insights. The work byPaz et al. uses stochastic extremum seeking to adapt the gainsof a PID control law for functional neuromuscular electricalstimulation.A final group of articles considers closed-loop anesthesia,physical activity, and behavior change. The article by vanHeusden et al. uses multi-input single-output (MISO) closed-loop identification to take the response to disturbances duringinduction of anesthesia into account. From this approach, theauthors identify and validate several models that describe theblood pressure and the depth-of-hypnosis response to propofolinfusion for patients at risk of cardiovascular suppression.The article by Lopes dos Santos et al. reformulates andadapts a class of identification algorithms known as matchable-observable linear identification (MOLI) to estimate lineartime-invariant models for Just Walk, a behavioral interventiondesigned on the basis of system identification principles toincrease physical activity in sedentary adults. These resultsprovide insights into the individual and the intervention, whichcan be used to improve the design of future studies. The finalarticle in this Special Issue is focused on social cognitivetheory (SCT), an important theory meaningful in many appli-cation settings, including the health behavior. Martin et al.postulate a dynamical systems model for SCT that can beused to predict changes in health behavior over time and canbe validated through “intensive” data streams generated bywearables and related devices that form an increasing partof mobile health (mHealth). These models, in turn, can leadto novel forms of optimized interventions based on controlsystems’ engineering principles.GUILLAUME MERCÈRE, Lead Guest EditorUniversité de Poitiers86073 Poitiers, Francee-mail: guillaume.mercere@univ-poitiers.frALEXANDER MEDVEDEV, Guest EditorUppsala University752 37 Uppsala, Swedene-mail: alexander.medvedev@it.uu.seDANIEL E. RIVERA, Guest EditorSchool for Engineering of Matter, Transport, andEnergy MailArizona State UniversityTempe, AZ 85287-6106 USAe-mail: daniel.rivera@asu.eduCATERINA SCOGLIO, Guest EditorDepartment of Electrical and Computer EngineeringCollege of EngineeringKansas State UniversityManhattan, KS 66506 USAe-mail: caterina@ksu.eduBAYU JAYAWARDHANA, Guest EditorEngineering and Technology Institute GroningenUniversity of Groningen9747AG Groningen, The Netherlandse-mail: b.jayawardhana@rug.nl

   University of GroningenForeword Identification and Control in Biomedical ApplicationsMercere, Guillaume; Medvedev, Alexander; Rivera, Daniel E.; Scoglio, Caterina;Jayawardhana, BayuPublished in:IEEE Transactions on Control Systems TechnologyDOI:10.1109/TCST.2019.2957597IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Mercere, G., Medvedev, A., Rivera, D. E., Scoglio, C., & Jayawardhana, B. (2020). Foreword Identificationand Control in Biomedical Applications. IEEE Transactions on Control Systems Technology, 28(1), 1-2.[8944255]. https://doi.org/10.1109/TCST.2019.2957597CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 21-06-2022IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020 1ForewordIdentification and Control inBiomedical ApplicationsCONTROL engineering (in the broad sense of the term)has become an important enabling technology in manyareas of medicine. Prominent examples include the artificialpancreas, closed-loop anesthesia, and personalized drug dosingstrategies in neurology, oncology, endocrinology, and psychia-try. It is a testament to the power of control systems that allowindividualizing treatment by providing mechanisms for linkingtreatment goals to treatment regimens, thus achieving a desiredtherapeutic effect. Consequently, the arrival of control systemsengineering to the clinic enables the visionary concept of “treatthe patient, not the disease” technologically and economicallyfeasible.This Special Issue is a joint effort of the three IEEE ControlSystems Society Technical Committees: System Identificationand Adaptive Control, Systems Biology, and Healthcare andMedical Systems. It addresses the challenges of applying feed-back/feedforward control and related technologies in livingorganisms, with an emphasis on biomedicine. Mathematicalmodels of systems in the human body can be used to uncoverand quantify dynamical contributions to health and disease.The insights obtained are expected to be instrumental in thedesign of innovative and agile tools for modeling, analysis,design, and optimization of biomedical systems.Altogether, 22 articles are included in this Special Issueand cover a wide field within biomedicine and behavioralmedicine, thus reflecting a diversity of topics where controland dynamical systems’ modeling have an important role.Mathematically, describing and controlling metabolic activityis a popular research area motivated by the need to understandthe dynamics of metabolism and thus to improve the treatmentof chronic conditions, such as diabetes. Applications to neu-roscience and neurology are gaining increasing acceptance,given the rise of research initiatives in America and Europetargeting the human brain, as well as the increasing prevalenceof neurological disorders in the world’s population. Articlesin the issue address a diversity of illnesses, conditions, andtreatments (e.g., Type 1 and Type 2 diabetes, Parkinson’s dis-ease, deep brain stimulation, myocardial infarction, influenzaand epidemic disease treatment, tumor therapy, weight control,neuromodulation, muscular rehabilitation, physical activity,and behavioral medicine), control strategies (e.g., feedback,feedforward, model predictive, nonlinear, data-centric/data-driven, and impulsive), and modeling/estimation approachesDigital Object Identifier 10.1109/TCST.2019.2957597(e.g., fractional models, Markov models, prediction-error esti-mation, semiphysical identification, and Kalman filtering).The preponderance of the approaches considered in this Spe-cial Issue is tested either with clinical data or via rigoroussimulation.This Special Issue is spearheaded by articles devoted tometabolic illnesses, artificial pancreas, and glucose control,addressing both Type 1 and Type 2 diabetes. Yu et al.study the problem of glucose prediction using sparse kernelfiltering algorithms. Computationally efficient adaptive andrecursive filtering algorithms are developed to provide onlinepredictions of blood glucose in patients with Type 1 dia-betes. Di Ferdinando et al. describe a nonlinear sampled-datafeedback controller for the glucose–insulin system meaningfulfor Type 2 diabetes. While less severe than Type 1, Type 2diabetes is more prevalent. Data-driven anomaly recognitionfor model-free fault detection is the focus of Meneghetti et al.’swork, while data-driven disturbance estimation and controlapplied to glucose regulation is the primary emphasis of thework by Novara et al. The section is rounded out by thework of Guo et al. in examining the problem of estimatingenergy intake (essential to weight control and understandingmetabolic function), which is studied in the context of weightgain in pregnancy for an overweight/obese population.Three articles address issues with modeling and controlof infectious diseases. The article by Paré et al. presentsa rigorous analysis of spreading models on nontrivial net-works. Several models in the literature are compared, andone is studied analytically, along with two real-world datasets that are used to validate one well-known approximationof the susceptible-infected-susceptible model. A discrete-timeinverse optimal impulsive control scheme is proposed by Her-nandez Mejia et al. in the second article to address the antiviraltreatment scheduling problem. The authors develop a modelwhose parameters are tuned using data from clinical trials andpresent results supporting the effectiveness and robustness ofthe proposed optimal antiviral treatment schedule. The articleby Cacace et al. develops a novel optimal control algorithmapplied to the design of a feedback antiangiogenic tumortherapy. Lack of data is mitigated by the use of a state observerthat exploits recent measurements. The algorithm is validatedon a virtual population through a rigorous set of simulations.Neuroscience and neurological efforts are featured in fourarticles. These include the work of Olsson and Medvedevon nonparametric tremor quantification with smartphones; theapproach leads to significant improvements compared with1063-6536 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.2 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 28, NO. 1, JANUARY 2020standard spectral analysis. Fractional-order modeling for aso-called “phantom” electroencephalographic (EEG) systemis examined in the work of Besancon et al., which can beused as a support to analyze dynamical behaviors in standardEEG measurement processes. Shakarami et al. rely on novelformulations of the Kalman filter for intraoperative brain shiftestimation using atlas of brain deformations. Finally, Cuboand Medvedev examine online tissue conductivity estimationin the context of deep brain tissue stimulation, a therapy usedfor alleviating symptoms associated with Parkinson’s disease.Cardiac and respiratory illnesses represent the next group ofarticles. The work of Hunnekens et al. discusses a variable-gain control strategy for mechanical ventilators in respira-tory systems. System identification using fractional models isagain examined in this issue, in the article by Victor et al.,as an approach to modeling thermal transfer in the lungs.Meanwhile, a mechanistic framework sets the underpinningsfor modeling and system identification of biomarkers that arereleased during acute mycardial infarction, as described in thework in Procopio et al.; this work is the first (to the best of ourknowledge) to account for the dynamics of cardiac biomarkerrelease.Two articles form part of the section on systems biology.This includes the work by Bianconi et al. on cancer sys-tems biology, who present an innovative Bayesian methodcalled conditional robust calibration (CRC) for model cal-ibration and identifiability analysis in this problem space.Multiobjective system identification of a feedback syntheticgene circuit, a highly nonlinear, stochastic problem, is exam-ined by Boada et al. Two articles, meanwhile, are associ-ated with muscular applications. van de Ruit et al. rely onboth nonparametric and parametric kernel-based regression tomonitor time-varying joint impedance during a force task;the approaches yield complementary insights. The work byPaz et al. uses stochastic extremum seeking to adapt the gainsof a PID control law for functional neuromuscular electricalstimulation.A final group of articles considers closed-loop anesthesia,physical activity, and behavior change. The article by vanHeusden et al. uses multi-input single-output (MISO) closed-loop identification to take the response to disturbances duringinduction of anesthesia into account. From this approach, theauthors identify and validate several models that describe theblood pressure and the depth-of-hypnosis response to propofolinfusion for patients at risk of cardiovascular suppression.The article by Lopes dos Santos et al. reformulates andadapts a class of identification algorithms known as matchable-observable linear identification (MOLI) to estimate lineartime-invariant models for Just Walk, a behavioral interventiondesigned on the basis of system identification principles toincrease physical activity in sedentary adults. These resultsprovide insights into the individual and the intervention, whichcan be used to improve the design of future studies. The finalarticle in this Special Issue is focused on social cognitivetheory (SCT), an important theory meaningful in many appli-cation settings, including the health behavior. Martin et al.postulate a dynamical systems model for SCT that can beused to predict changes in health behavior over time and canbe validated through “intensive” data streams generated bywearables and related devices that form an increasing partof mobile health (mHealth). These models, in turn, can leadto novel forms of optimized interventions based on controlsystems’ engineering principles.GUILLAUME MERCÈRE, Lead Guest EditorUniversité de Poitiers86073 Poitiers, Francee-mail: guillaume.mercere@univ-poitiers.frALEXANDER MEDVEDEV, Guest EditorUppsala University752 37 Uppsala, Swedene-mail: alexander.medvedev@it.uu.seDANIEL E. RIVERA, Guest EditorSchool for Engineering of Matter, Transport, andEnergy MailArizona State UniversityTempe, AZ 85287-6106 USAe-mail: daniel.rivera@asu.eduCATERINA SCOGLIO, Guest EditorDepartment of Electrical and Computer EngineeringCollege of EngineeringKansas State UniversityManhattan, KS 66506 USAe-mail: caterina@ksu.eduBAYU JAYAWARDHANA, Guest EditorEngineering and Technology Institute GroningenUniversity of Groningen9747AG Groningen, The Netherlandse-mail: b.jayawardhana@rug.nl

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Foreword Identification and Control in Biomedical Applications

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