Stochastic Cardiac Pacing Increases Ventricular Electrical Stability—A Computational Study

AbstractThe ventricular tissue is activated in a stochastic rather than in a deterministic rhythm due to the inherent heart rate variability (HRV). Low HRV is a known predictor for arrhythmia events and traditionally is attributed to autonomic nervous system tone damage. Yet, there is no model that directly assesses the antiarrhythmic effect of pacing stochasticity per se. One-dimensional (1D) and two-dimensional (2D) human ventricular tissues were modeled, and both deterministic and stochastic pacing protocols were applied. Action potential duration restitution (APDR) and conduction velocity restitution (CVR) curves were generated and analyzed, and the propensity and characteristics of action potential duration (APD) alternans were investigated. In the 1D model, pacing stochasticity was found to sustain a moderating effect on the APDR curve by reducing its slope, rendering the tissue less arrhythmogenic. Moreover, stochasticity was found to be a significant antagonist to the development of concordant APD alternans. These effects were generally amplified with increased variability in the pacing cycle intervals. In addition, in the 2D tissue configuration, stochastic pacing exerted a protective antiarrhythmic effect by reducing the spatial APD heterogeneity and converting discordant APD alternans to concordant ones. These results suggest that high cardiac pacing stochasticity is likely to reduce the risk of cardiac arrhythmias in patients

Inhibition of G-protein signalling in cardiac dysfunction of intellectual developmental disorder with cardiac arrhythmia (IDDCA) syndrome

Background: Pathogenic variants of GNB5 encoding the β5 subunit of the guanine nucleotide-binding protein cause IDDCA syndrome, an autosomal recessive neurodevelopmental disorder associated with cognitive disability and cardiac arrhythmia, particularly severe bradycardia. Methods: We used echocardiography and telemetric ECG recordings to investigate consequences of Gnb5 loss in mouse. Results: We delineated a key role of Gnb5 in heart sinus conduction and showed that Gnb5-inhibitory signalling is essential for parasympathetic control of heart rate (HR) and maintenance of the sympathovagal balance. Gnb5-/- mice were smaller and had a smaller heart than Gnb5+/+ and Gnb5+/-, but exhibited better cardiac function. Lower autonomic nervous system modulation through diminished parasympathetic control and greater sympathetic regulation resulted in a higher baseline HR in Gnb5-/- mice. In contrast, Gnb5-/- mice exhibited profound bradycardia on treatment with carbachol, while sympathetic modulation of the cardiac stimulation was not altered. Concordantly, transcriptome study pinpointed altered expression of genes involved in cardiac muscle contractility in atria and ventricles of knocked-out mice. Homozygous Gnb5 loss resulted in significantly higher frequencies of sinus arrhythmias. Moreover, we described 13 affected individuals, increasing the IDDCA cohort to 44 patients. Conclusions: Our data demonstrate that loss of negative regulation of the inhibitory G-protein signalling causes HR perturbations in Gnb5-/- mice, an effect mainly driven by impaired parasympathetic activity. We anticipate that unravelling the mechanism of Gnb5 signalling in the autonomic control of the heart will pave the way for future drug screening

Digital Biomarker Models for Prediction of Infectious Disease Susceptibility

Acute respiratory viral infection (ARVI) represents one of the most prevalent infectious diseases affecting mankind. With the threat of COVID-19 still looming over us, we have witnessed the substantial threat ARVI poses to world health and economy, extinguishing millions of lives and costing trillions of dollars. This sets the context for the research of this thesis: using digital biomarkers to distinguish between individuals who are susceptible to becoming severely infected and/or infectious before an infection is clinically detectable. The development of such biomarkers can have both clinical and epidemiological impact in terms of identifying individuals who are either vulnerable to severe infection or those who may become highly infectious. The digital biomarkers and associated analysis methods are developed and validated on longitudinal data collected by our clinical collaborators from two different ARVI challenge studies. The first study provides data on healthy human volunteers who were inoculated with the common cold and the second study provides data on volunteers inoculated with the flu. Digital biomarkers include molecular, physiological and cognitive data continuously collected from blood, wearable devices and cognitive testing of the study participants. The findings of our research on digitally measurable susceptibility factors are wide-ranging. We find that circadian rhythm at the molecular scale (biochronicity) plays an important role in mediating both the susceptibility and the response to severe infection, revealing groups of gene expression markers that differentiate the responses of low infected and high infected individuals. Using a high dimensional representation of physiological signals from a wearable device, we find that an infection response and its onset time can be reliably predicted at least 24 hours before peak infection time. We find that a certain measure of variability in pre-exposure cognitive function is highly associated with the post-exposure severity of infection.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169966/1/yayazhai_1.pd

Detection of QRS complex in experimental ECG data

V rámci mé bakalářské práce jsem realizoval QRS detektor pomocí vlnkové transformace za použití biortogonální vlnky bior1.5 v prostředí Matlab. Detektor využívá antisymetrie této vlnky k rozpoznávání páru minimum/maximum nebo maximum/minimum a singularity mezi nimi. Tento detektor byl testován proti databázi CSE. Dále byl detektor upraven pro detekci QRS komplexu na experimentálních datech.In this paper, called Bachelor thesis, I proposed a QRS complex detector based on wavelet transformation where biorthogonal wavelet bior1.5 was applied with use of Matlab. The detector works with antisymmetry of the wavelet to detect minimum/maximum or maximum/minimum pairs and singularity between them. Results of this detector were evaluated on the CSE database. The detector was reworked to be able to detect experimental QRS complexes.

Imaging photoplethysmography: towards effective physiological measurements

Since its conception decades ago, Photoplethysmography (PPG) the non-invasive opto-electronic technique that measures arterial pulsations in-vivo has proven its worth by achieving and maintaining its rank as a compulsory standard of patient monitoring. However successful, conventional contact monitoring mode is not suitable in certain clinical and biomedical situations, e.g., in the case of skin damage, or when unconstrained movement is required. With the advance of computer and photonics technologies, there has been a resurgence of interest in PPG and one potential route to overcome the abovementioned issues has been increasingly explored, i.e., imaging photoplethysmography (iPPG). The emerging field of iPPG offers some nascent opportunities in effective and comprehensive interpretation of the physiological phenomena, indicating a promising alternative to conventional PPG. Heart and respiration rate, perfusion mapping, and pulse rate variability have been accessed using iPPG. To effectively and remotely access physiological information through this emerging technique, a number of key issues are still to be addressed. The engineering issues of iPPG, particularly the influence of motion artefacts on signal quality, are addressed in this thesis, where an engineering model based on the revised Beer-Lambert law was developed and used to describe opto-physiological phenomena relevant to iPPG. An iPPG setup consisting of both hardware and software elements was developed to investigate its reliability and reproducibility in the context of effective remote physiological assessment. Specifically, a first study was conducted for the acquisition of vital physiological signs under various exercise conditions, i.e. resting, light and heavy cardiovascular exercise, in ten healthy subjects. The physiological parameters derived from the images captured by the iPPG system exhibited functional characteristics comparable to conventional contact PPG, i.e., maximum heart rate difference was <3 bpm and a significant (p < 0.05) correlation between both measurements were also revealed. Using a method for attenuation of motion artefacts, the heart rate and respiration rate information was successfully assessed from different anatomical locations even in high-intensity physical exercise situations. This study thereby leads to a new avenue for noncontact sensing of vital signs and remote physiological assessment, showing clear and promising applications in clinical triage and sports training. A second study was conducted to remotely assess pulse rate variability (PRV), which has been considered a valuable indicator of autonomic nervous system (ANS) status. The PRV information was obtained using the iPPG setup to appraise the ANS in ten normal subjects. The performance of the iPPG system in accessing PRV was evaluated via comparison with the readings from a contact PPG sensor. Strong correlation and good agreement between these two techniques verify the effectiveness of iPPG in the remote monitoring of PRV, thereby promoting iPPG as a potential alternative to the interpretation of physiological dynamics related to the ANS. The outcomes revealed in the thesis could present the trend of a robust non-contact technique for cardiovascular monitoring and evaluation

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

Perception and Orientation in Minimally Invasive Surgery

During the last two decades, we have seen a revolution in the way that we perform abdominal surgery with increased reliance on minimally invasive techniques. This paradigm shift has come at a rapid pace, with laparoscopic surgery now representing the gold standard for many surgical procedures and further minimisation of invasiveness being seen with the recent clinical introduction of novel techniques such as single-incision laparoscopic surgery and natural orifice translumenal endoscopic surgery. Despite the obvious benefits conferred on the patient in terms of morbidity, length of hospital stay and post-operative pain, this paradigm shift comes at a significantly higher demand on the surgeon, in terms of both perception and manual dexterity. The issues involved include degradation of sensory input to the operator compared to conventional open surgery owing to a loss of three-dimensional vision through the use of the two-dimensional operative interface, and decreased haptic feedback from the instruments. These changes have led to a much higher cognitive load on the surgeon and a greater risk of operator disorientation leading to potential surgical errors. This thesis represents a detailed investigation of disorientation in minimally invasive surgery. In this thesis, eye tracking methodology is identified as the method of choice for evaluating behavioural patterns during orientation. An analysis framework is proposed to profile orientation behaviour using eye tracking data validated in a laboratory model. This framework is used to characterise and quantify successful orientation strategies at critical stages of laparoscopic cholecystectomy and furthermore use these strategies to prove that focused teaching of this behaviour in novices can significantly increase performance in this task. Orientation strategies are then characterised for common clinical scenarios in natural orifice translumenal endoscopic surgery and the concept of image saliency is introduced to further investigate the importance of specific visual cues associated with effective orientation. Profiling of behavioural patterns is related to performance in orientation and implications on education and construction of smart surgical robots are drawn. Finally, a method for potentially decreasing operator disorientation is investigated in the form of endoscopic horizon stabilization in a simulated operative model for transgastric surgery. The major original contributions of this thesis include: Validation of a profiling methodology/framework to characterise orientation behaviour Identification of high performance orientation strategies in specific clinical scenarios including laparoscopic cholecystectomy and natural orifice translumenal endoscopic surgery Evaluation of the efficacy of teaching orientation strategies Evaluation of automatic endoscopic horizon stabilization in natural orifice translumenal endoscopic surgery The impact of the results presented in this thesis, as well as the potential for further high impact research is discussed in the context of both eye tracking as an evaluation tool in minimally invasive surgery as well as implementation of means to combat operator disorientation in a surgical platform. The work also provides further insight into the practical implementation of computer-assistance and technological innovation in future flexible access surgical platforms

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing