Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

Experimental and computational biomedicine : Russian Conference with International Participation in memory of Professor Vladimir S. Markhasin : abstract book

Toward 100 Anniversary of I. P. Pavlov's Physiological Society.The volume contains the presentations that were made during Russian conference with international participation "Experimental and Computational Biomedicine" dedicated to corresponding member of RAS V.S. Markhasin (Ekaterinburg, April 10‒12, 2016). The main purpose of the conference is the discussion of the current state of experimental and theoretical research in biomedicine. For a wide range of scientists, as well as for lecturers, students of the biological and medical high schools.Сборник содержит тезисы докладов, представленных на российской конференции с международным участием «Экспериментальная и компьютерная биомедицина», посвященной памяти члена‐корреспондента РАН В. С. Мархасина (г. Екатеринбург, 10‒12 апреля 2016 г.). Основной целью конференции является обсуждение современного состояния экспериментальных и теоретических исследований в области биомедицины. Сборник предназначен для ученых, преподавателей, студентов и аспирантов биологического и медицинского профиля.МАРХАСИН ВЛАДИМИР СЕМЕНОВИЧ (1941-2015)/ MARKHASIN VLADIMIR SEMENOVICH (1941-2015). [3] PROGRAMM COMMITTEE. [5] ORGANIZING COMMITTEE. [6] KEYNOTE SPEAKERS. [7] CONTENTS. [9] PLENARY LECTURES. [10] Fedotov S. Non-Markovian random walks and anomalous transport in biology. [10] Hoekstra A. Multiscale modelling in vascular disease. [10] Kohl P. Systems biology of the heart: why bother? [10] Meyerhans A. On the regulation of virus infection fates. [11] Panfilov A.V., Dierckx H., Kazbanov I., Vandersickel N. Systems approach to studying mechanisms of ventricular fibrillationusing anatomically accurate modeling. [11] Revishvili A.S. Atrial fibrillation. Noninvasive diagnostic and treatment:from fundamental studies to clinical practice. [12] Rice J. Life sciences research at IBM. [12] Roshchevskaya I.M., Smirnova S., Roshchevsky M.P. Regularities of the depolarization of an atria:an experimental comparative-physiological study. [12] Rusinov V.L., Chupahin O.N., Charushin V.N Scientific basis for development of antiviral drugs. [13] Solovyova O.E. Tribute Lecture. Mechano-electric heterogeneity of the myocardiumas a paradigm of its function. [13] Veksler V. Myocardial energy starvation in chronic heart failure:perspectives for metabolic therapy. [13] Wladimiroff J.W. Fetal cardiac assessment using new methodsof ultrasound examination. [14] Yushkov B.G., Chereshnev V.A. The important questions of regeneration theory. [14] EXPERIMENTAL AND COMPUTATIONAL MODELS IN CARDIOVASCULARPHYSIOLOGY AND CARDIOLOGY. [15] EXPERIMENTAL AND COMPUTATIONAL MODELS IN CARDIOVASCULARPHYSIOLOGY AND CARDIOLOGY. [15] Arteyeva N. T-wave area along with Tpeak-Tend interval is the most accurateindex of the dispersion of repolarization. [15] Borodin N., Iaparov B.Y., Moskvin A. Mathematical modeling of the calmodulin effect on the RyR2 gating. [15] Dokuchaev A., Katsnelson L.B., Sulman T.B., Shikhaleva E.V., Vikulova N.A. Contribution of cooperativity to the mechano-calcium feedbacksin myocardium. Experimental discrepancy and mathematicalapproach to overcome it. [16] Elman K.A., Filatova D.Y., Bashkatova Y.V., Beloschenko D.V. The stochastic and chaotic estimation of parametersof cardiorespiratory system of students of Ugra. [16] Erkudov V.O., Pugovkin A.P., Verlov N.A., Sergeev I.V., Ievkov S.A., Mashood S., Bagrina J.V. Characteristics of the accuracy of calculation of values of systemic blood pressure using transfer functions in experimental blood loss and its compensation. [16] Ermolaev P., Khramykh T.Mechanisms of cardiodepression after 80% liver resection in rats. [17] Filatova O.E., Rusak S.N., Maystrenko E.V., Dobrynina I.Y. Aging dynamics of cardio-vascular parameters аboriginal systemand alien population of the Russian North. [17] Frolova S., Agladze K.I., Tsvelaya V., Gaiko O. Photocontrol of voltage-gated ion channel activity by azobenzenetrimethylammonium bromide in neonatal rat cardiomyocytes. [18] Gorbunov V.S., Agladze K.I., Erofeev I.S. The application of C-TAB for excitation propagation photocontrolin cardiac tissue. [18] Iribe G. Localization of TRPC3 channels estimated by in-silicoand cellular functional experiments. [19] Kachalov V.N., Tsvelaya V., Agladze K.I. Conditions of the spiral wave unpinning from the heterogeneitywith different boundary conditions in a model of cardiac tissue. [19] Kalita I., Nizamieva A.A., Tsvelaya V., Kudryashova N., Agladze K.I. The influence of anisotropy on excitation wave propagationin neonatal rat cardiomyocytes monolayer. [19] Kamalova Y. The designing of vectorcardiograph prototype. [20] Kapelko V., Shirinsky V.P., Lakomkin V., Lukoshkova E., Gramovich V.,Vyborov O., Abramov A., Undrovinas N., Ermishkin V. Models of chronic heart failure with acute and gradual onset. [20] Khassanov I., Lomidze N.N., Revishvili A.S. Remote Patient Monitoring and Integration of Medical Data. [20] Kislukhin V. Markov chain for an indicator passing throughoutcardio-vascular system (CVS). [21] Konovalov P.V., Pravdin S., Solovyova O.E., Panfilov A.V. Influence of myocardial heterogeneity on scroll wave dynamicsin an axisymmetrical anatomical model of the left ventricle of thehuman heart. [21] Koshelev A., Pravdin S., Ushenin K.S., Bazhutina A.E. An improved analytical model of the cardiac left ventricle. [22] Lookin O., Protsenko Y.L. Sex-related effects of stretch on isometric twitch and Ca2+ transientin healthy and failing right ventricular myocardiumof adult and impuberal rats. [22] Moskvin A. Electron-conformational model of the ligand-activated ion channels. [22] Nezlobinsky T., Pravdin S., Katsnelson L.B. In silico comparison of the electrical propagation wave alongmyocardium fibers in the left ventricle wall vs. isolation. [23] Nigmatullina R.R., Zemskova S.N., Bilalova D.F., Mustafin A.A., Kuzmina O.I., Chibireva M.D., Nedorezova R.S. Valid method for estimation of pulmonary hypertention degreein children. [23] Parfenov A. Mathematical modeling of the cardiovascular systemunder the influence of environmental factors. [24] Pimenov V.G., Hendy A. Adaptivity of the alternating direction method for fractional reactiondiffusion equation with delay effects in electrocardiology. [24] Podgurskaya A.D., Krasheninnikova A., Tsvelaya V., Kudryashova N., Agladze K.I. Influence of alcohols on excitation wave propagationin neonatal rat ventricular cardiomyocyte monolayer. [24] Pravdin S. A mathematical model of the cardiac left ventricle anatomy and morphology. [24] Seemann G. Cause and effects of cardiac heterogeneity:insights from experimental and computational models. [25] Seryapina A.A., Shevelev O.B. Basic metabolomic patterns in early hypertensive rats: MRI study. [25] Shestakov A.P., Vasserman I.N., Shardakov I.N. Modeling of cardiac arrhythmia generation caused bypathological distribution of myocardial conductivity. [26] Shutko A.V., Gorbunov V.S., Nizamieva A.A., Guriya K.G., Agladze K.I. Contractile micro-constructs from cardiac tissue culturefor the research of autowave propagation in excitable systems. [26] Simakov S., Gamilov T., Kopylov Ph. Computational study of the haemodynamic significanceof the stenosis during multivessel coronary disease. [27] Syomin F., Zberiya M.V. A numerical simulation of changes in the performance of the leftventricle of the heart under various hemodynamic conditions. [27] Tsaturyan A. A simple model of cardiac muscle:mechanics, actin-myosin interaction and Ca-activation. [27] Tsvelaya V., Krasheninnikova A., Kudryashova N., Agladze K.I. Calcium-current dominated upstroke in severe hyperkalemia. [28] Ushenin K.S., Pravdin S., Chumarnaya T.V., Alueva Y.S., Solovyova O.E. Dynamics of scroll wave filaments in personalized modelsof the left ventricle of the human heart. [28] Vasserman I.N., Shardakov I.N., Shestakov A.P. Deriving of macroscopic intracellular conductivity of deformedmyocardium based on its microstructure. [28] Vassilevski Y.V., Pryamonosov R., Gamilov T. Personalized 3D models and applications. [29] Zun P.S., Hoekstra A., Anikina T.S. First results of fully coupled 3D models of in-stent restenosis. [29] BIOMECHANICS. EXPERIMENTAL AND MATHEMATICAL MODELSSBIOMECHANICS. EXPERIMENTAL AND MATHEMATICAL MODELS. EXPERIMENTAL AND MATHEMATICAL MODELS. [30] Balakin A., Kuznetsov D., Protsenko Y.L. The ‘length-tension’ loop in isolated myocardial preparations of theright ventricle of normal and hypertrophied hearts of male rats. [30] Belousova M.D., Kruchinina A.P., Chertopolokhov V.A. Automatic control model of the three-tier arm type manipulatorin the aimed-movement task. [30] Berestin D.K., Bazhenova A.E., Chernikov N.A., Vokhmina Y.V. Mathematical modeling of dynamics of development of Parkinson'sdisease on the tremor parameters. [31] Dubinin A.L., Nyashin Y.I., Osipenko M.A. Development of the biomechanical approach to tooth movementunder the orthodontic treatment. [31] Galochkina T., Volpert V. Reaction-diffusion waves in mathematical model of bloodcoagulation. [31] Golov A.V., Simakov S., Timme E.A. Mathematical modeling of alveolar ventilationand gas exchange during treadmill stress tests. [32] Gurev V., Rice J. Strain prediction in 3D finite element models of cardiac mechanics. [32] Kamaltdinov M.R. Simulation of digestion processes in antroduodenum:food particles dissolution in consideration of functional disorders. [33] Khamzin S., Kursanov A., Solovyova O.E. Load-dependence of the electromechanical function of myocardiumin a 1D tissue model. [33] Khokhlova A., Iribe G., Solovyova O.E Transmural gradient in mechanical properties of isolatedsubendocardial and subepicardial cardiomyocytes. [33] Kruchinin P.A. Optimal control problem and indexesof stabilometric "test with the visual step input". [34] Kruchinina A.P., Yakushev A.G. A study of the edge segments of saccadic eye trajectory. [34] Kursanov A., Khamzin S., Solovyova O.E. Load-dependence of intramyocardial slow force responsein heterogeneous myocardium. [35] Lisin R.V., Balakin A., Protsenko Y.L. Experimental study of the intramyocardial slow force response. [35] Melnikova N.B., Hoekstra A. The mechanics of a discrete multi-cellular model of arterial in‐stent restenosis. [35] Murashova D.S., Murashov S.A., Bogdan O.P., Muravieva O.V., Yugova S.O. Modelling of soft tissue deformation for static elastometry. [36] Nikitin V.N., Tverier V.M., Krotkikh A.A. Occlusion correction based on biomechanical modelling. [36] Nyashin Y.I., Lokhov V.A. Development of the “Virtual physiological human” concept. [37] Shulyatev A.F., Akulich Y.V., Akulich A.Y., Denisov A.S. 3D FEA simulation of the proximal human femur. [37] Smoluk A.T., Smoluk L.T., Balakin A., Protsenko Y.L., Lisin R.V. Modelling viscoelastic hysteresis of passive myocardial sample. [37] Svirepov P.I. Mathematical modeling of the left atria mechanical actionwith mitral regurgitation. [38] Svitenkov A., Rekin O., Hoekstra A. Accuracy of 1D blood flow simulations in relation to level of detailof the arterial tree model. [38] Tsinker M. Mathematical modelling of airflow in human respiratory tract. [39] Wilde M.V. Influence of artificial initial and boundary conditionsin biomechanical models of blood vessels. [39] ELECTROPHYSIOLOGY. EXPERIMENTAL AND COMPUTATIONAL MODELS. CLINICAL STUDIES. [40] Agladze K.I., Agladze N.N. Arrhythmia modelling in tissue culture. [40] Golovko V., Gonotkov M.A. Pharmacological analysis of transmembrane action potential'smorphology of myoepitelial cells in the spontaneously beating heartof ascidia Styela rustica. [40] Gonotkov M.A., Golovko V. The crucial role of the rapidly activating component of outwarddelayed rectifier K-current (IKr) in pig sinoauricular node (SAN). [40] Danilov A.A. Numerical methods for electrocardiography modelling. [41] Kolomeyets N.L., Roshchevskaya I.M. The electrical resistivity of a segment of the tail, lungs, liver,intercostal muscles of grass snakes during cooling. [41] Kharkovskaia E., Zhidkova N., Mukhina I.V., Osipov G.V. Role of TRPC1 channels in the propagation of electrical excitationin the isolated rat heart. [42] Lubimceva T.A., Lebedeva V.K., Trukshina M.A., Lyasnikova E.A., Lebedev D.S. Ventricular lead position and mechanical dyssynchronyin response to cardiac resynchronization therapy. [42] Poskina T.Y., Shakirova L.S., Klyus L.G., Eskov V.V. Stochastics and chaotic analysis of electromyogramand electroencefalogramm. [42] Prosheva V.I. New insights into the pacemaker and conduction systemcells organization in the adult avian heart. [43] Suslonova O., Smirnova S., Roshchevskaya I.M. Cardioelectric field in rats with experimental pulmonaryhypertension during ventricular depolarization. [43] Syunyaev R.A., Karpaev A.A., Aliev R.R. Simulation of the fibroblasts effect on synchronizationand rhythmogenesis in the sinoatrial node. [44] Zorin N.M., Ryvkin A.М., Moskvin A. Cooperation of membrane and calcium oscillatorsin sinoatrial node cells. [44] EXPERIMENTAL AND COMPUTATIONAL MODELS IN IMMUNOLOGY. [45] Bocharov G. Systems approach to modelling the "virus-host organism" interactionin infectious diseases. [45] Brilliant S.A. Impact of immobilization stress on change of protein fractionshemoglobin of bone marrow in rats. [45] Bykova M. The features of biochemical properties of extracellular matrix of bonemarrow in rats in conditions which stimulate granulocytopoiesis. [45] Chigvintsev V.M. A mathematical model of the functioning and mutual regulation ofthe immune and neuroendocrine systems in response to viralexposure under the impact of environmental factors, taking intoaccount the evolution of synthetic function impairment. [46] Khramtsova Y. The role of mast cells in the regulation of repair testicles. [46] Novikov M.Y., Kim A.V. Simulation of immune processes using Bio-Medical Software Package. [47] Polevshchikov A.V., Bondar A.V., Gumovskaya J.P. Modelling of t cell extravasation into a lymph node:from morphological basics towards clonal selection theory. [47] Tuzankina I.A., Sarkisyan N., Bolkov M., Tihomirov L.B., Bass E.A. Oral and maxillofacial manifestationsof primary immunodeficiency syndroms. [47] Zaitsev S.V., Polevshchikov A.V. Evaluation of probabilities of antigen recognition by T-lymphocytesin the lymph node: a mathematical model. [48] MOLECULAR BASIS OF BIOLOGICAL MOTILITY. [49] Bershitsky S.Y., Nabiev S., Kopylova G., Shchepkin D., Matyushenko A.M., Koubassova N.A., Levitsky D.I., Tsaturyan A. Mutations in the central part of tropomyosin molecule affectthe actomyosin interaction. [49] Borovkov D.I., Kopylova G., Shchepkin D., Nabiev S., Matyushenko A.M., Levitsky D.I. Functional studies of tropomyosin mutations associatedwith dilated and hypertrophic cardiomyopathy. [49] Fatkhrakhmanova M.R., Mukhutdinova K.A., Kasimov M.R., Petrov A.M. The role of glutamate NMDA-receptor-NO synthase axis in the effectof 24-hydroxycholesterolon synaptic vesicle exocytosis at the mouseneuromuscular junctions. [50] Gritsyna Y., Vikhlyantsev I.M., Salmov N., Bobylev A.G., Podlubnaya Z.A. Increasing μ-calpain activity in striated muscles of alcohol-fed rats. [50] Kochubey P.V., Bershitsky S.Y. Study of biphasic tension rise in contracting muscle fiberduring ramp stretch. [51] Kopylova G., Shchepkin D., Nabiev S., Nikitina L., Bershitsky S.Y. 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An interactive, real-time, high precision and portable monitoring system of obstructive sleep apnea

Obstructive sleep apnea (OSA) is the most common type of sleep apnea which is defined as the suspension of breathing. OSA is generally caused by complete or partial obstruction of airway during sleep, making the breathing pattern irregular and abnormal for prolonged periods of time. Apnea can contribute to a variety of life threatening medical conditions, and can be deadly if left untreated. Nowadays, out of 18 to 50 million people in the US, most cases remain undiagnosed due to the cost, cumbersome and resource limitations of overnight polysomnography (PSG) at sleep labs. Currently PSG relies on a doctor's experience. In order to improve the medical service efficiency, reduce diagnosis time and ensure a more accurate diagnosis, a quantitative and objective method is needed. In this dissertation, an innovative method in characterizing bio-signals for detecting epochs of sleep apnea with high accuracy is presented. Three data channels that are related to breath defect; respiratory sound, ECG and SpO2 are investigated, in order to extract physiological indicators that characterize sleep apnea. An automated method was used to analyze the respiratory sound to find pauses in breathing. Furthermore, the automated method analyzed ECG to find irregular heartbeats and SpO 2 to find rises and drops. The system consists of three main parts which are signal segmentation, features extraction and features classification. Feature extractions process is based on statistical measures. Features classification process is learned through Support Vector Machines (SVMs) and Neural Network (NN) classifiers. Moreover, a preprocessing technique is carried out to distinguish the R-wave from the other waves of the ECG signal. The approach presented in this dissertation was tested using downloaded polysomnographic ECG and SpO2 data from the Physionet database. In addition, to identifying sleep apnea using the acoustic signal of respiration; the characterization of breathing sound was carried by Voice Activity Detection (VAD) algorithm. VAD was used to measure the energy of the acoustic respiratory signal during breath and silence segments. From the experimental results for the three signals, it was concluded that the precision of classifying sleep apnea has an accuracy of 97%. This result offers a clinical reference value for identifying OSA instead of expensive PSG visual scoring method which is commonly used to asses sleep apnea, and could reduce diagnostic time and improve medical service efficiency

Hypotheses engine (HypE): exploring structured biomedical datasets in search for predictive patterns

Nowadays, healthcare facilities constantly collect an immense amount of data as part of their daily-management systems, which include diverse type of information, such as patient admission details, drugs administered or clinical examinations’ results. Even though medical research has been traditionally condition-oriented, researchers oftentimes use similar analysis methodologies, with very little context customization, making them computationally redundant. This project proposes an analysis pipeline capable of automatically mine big and diverse biomedical datasets, and identify potentially interesting patterns in the data, despite of the medical conditions the data might relate to. Such system is called an hypotheses engine, as its purpose is to output patterns that seem to be medically predictive, which we call hypotheses. HypE’s novelty is two-fold: on one hand, a tailored data processing method was developed for analyzing inconsistent and chaotic temporal data (i.e. a patient has laboratory measurements, that usually are only partially repeated over time); and on the other hand, the hypotheses found are to be outputted in a physician-friendly way, to allow fast understanding of the patterns found, in case medical intervention is recommended. Given HypE’s functionality, results cannot be straightforwardly classified as good or bad, as certain data subsets might actually not contain any patterns, at all. However, methodologically, it is to expect that some hypotheses found will be known medical patterns. Thus, HypE’s outputs are presented and discussed on a high level, considering no manual check for their medical validity was performed by medical experts. The prototype implemented was ran on MIMIC-III data and the results exceeded the initial expectations as they did include common medical scenarios

Retinal Vascular Measurement Tools for Diagnostic Feature Extraction

The contributions of this work are in the development of new and state of the art algorithms for retinal image analysis including optic disc detection, tortuosity estimation, and cross-over abnormality detection. The retina is one of the only areas of the human body that blood vessels can be visualized noninvasively. Retinal imaging has become a standard in the ophthalmologist’s office because it is an easy and inexpensive way to monitor not just eye health, but also systemic vascular diseases. Changes to the retinal vasculature can be the early signs of diseases such as diabetic and hypertensive retinopathy, of which early detection can save vision, money, and improve overall health for the patient. When looking at the retinal vasculature, ophthalmologists generally rely on a qualitative assessment which can make comparisons over time or between different ophthalmologists difficult. Computer aided systems are now able to quantify what the ophthalmologist is qualitatively measuring in what they consider to be the most important features of the vasculature. These include, but are not limited to, tortuosity, arteriolar narrowing, cross-over abnormalities, and artery-vein (AV) ratio. The University of Padova has created a semi-automatic system for detecting and quantifying retinal vessels starting from optic disc detection, vessel segmentation, width estimation, tortuosity calculation, AV classification, and AV ratio. We propose a new method for optic disc detection that converts the retinal image into a graph and exploits vessel enhancement methods to calculate edge weights in finding the shortest path between pairs of points on the periphery of the image. The line segment with the maximum number of shortest paths is considered the optic disc location. The method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images and achieved an accuracy of 100, 98.88, and 99.42% respectively. The second contribution is a new algorithm for calculating abnormalities at AV crossing points. In retinal images, Gunn’s sign appears as a tapering of the vein at a crossing point, while Salus’s sign presents as an S-shaped curving. This work presents a method for the automatic quantification of these two signs once a crossover has been detected; combining segmentation, artery vein classification, and morphological feature extraction techniques to calculate vein widths and angles entering and exiting the crossover. Results on two datasets show separation between the two classes and that we can reliably detect and quantify these signs under the right conditions. The last contribution in tortuosity consists of two parts. A comparative study was performed on several of the most popular methods for tortuosity estimation on a new vessel dataset. Results show that several methods have good Cohen’s kappa agreement with both graders, while the tortuosity density metric has the highest single metric average agreement across vessel type and grader. The second is a new way to enhance curvature in segmented vessels based on a difference of Gabor filters to create a curvature enhanced image. The proposed method was tested on the RET-TORT database using several methods to calculate tortuosity, and had best Pearson’s correlation of .94 for arteries and .882 for veins, outperforming single mathematical formulations on the data. This held true after testing the method on the propose dataset as well, having higher correlation values across grader and vessel type compared with other tortuosity metrics. Summary of Results: The optic disc detection method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images and achieved an accuracy of 100, 98.88, and 99.42% respectively. The AV nicking quantification method was tested on a small dataset of 10 crossing provided by doctors at Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece. Results showed separation between the normal and abnormal classes for both the Gunn and Salus sign. The method was then tested on a larger, publicly available dataset which showed good separation for the Gunn sign. The proposed tortuosity method was tested on the RET-TORT database using several methods to calculate tortuosity, and had best Pearson’s correlation of .94 for arteries and .882 for veins, outperforming single mathematical formulations on the data. It was then tested on the dataset proposed in this thesis, further corroborating the effectiveness of the method

Identifying evolving multivariate dynamics in individual and cohort time series, with application to physiological control systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 117-125).Physiological control systems involve multiple interacting variables operating in feedback loops that enhance an organism's ability to self-regulate and respond to internal and external disturbances. The resulting multivariate time-series often exhibit rich dynamical patterns, which are altered under pathological conditions. However, model identification for physiological systems is complicated by measurement artifacts and changes between operating regimes. The overall aim of this thesis is to develop and validate computational tools for identification and analysis of structured multivariate models of physiological dynamics in individual and cohort time-series. We first address the identification and stability of the respiratory chemoreflex system, which is key to the pathogenesis of sleep-induced periodic breathing and apnea. Using data from both an animal model of periodic breathing, as well as human recordings from clinical sleep studies, we demonstrate that model-based analysis of the interactions involved in spontaneous breathing can characterize the dynamics of the respiratory control system, and provide a useful tool for quantifying the contribution of various dynamic factors to ventilatory instability. The techniques have suggested novel approaches to titration of combination therapies, and clinical evaluations are now underway. We then study shared multivariate dynamics in physiological cohort time-series, assuming that the time-series are generated by switching among a finite collection of physiologically constrained dynamical models. Patients whose time-series exhibit similar dynamics may be grouped for monitoring and outcome prediction. We develop a novel parallelizable machine-learning algorithm for outcome-discriminative identification of the switching dynamics, using a probabilistic dynamic Bayesian network to initialize a deterministic neural network classifier. In validation studies involving simulated data and human laboratory recordings, the new technique significantly outperforms the standard expectation-maximization approach for identification of switching dynamics. In a clinical application, we show the prognostic value of assessing evolving dynamics in blood pressure time-series to predict mortality in a cohort of intensive care unit patients. A better understanding of the dynamics of physiological systems in both health and disease may enable clinicians to direct therapeutic interventions targeted to specific underlying mechanisms. The techniques developed in this thesis are general, and can be extended to other domains involving multi-dimensional cohort time-series.by Shamim Nemati.Ph.D

DEVELOPING INTEGRATED MACHINE LEARNING MODELS FOR AUTOMATIC COMPUTER-AIDED DIAGNOSIS IN ISCHEMIC ACUTE STROKE MRI

Fast detection and quantification of lesion cores in diffusion weighted images (DWIs) has been highly anticipated in clinical and research communities for planning treatment of acute stroke. The recent emergence of successful machine learning (ML) methods, especially Deep Learning (DL), enables automatic Computer Aided Diagnosis (CAD) of stroke in DWIs. However, the lack of publicly available large-scale data and ML models in clinical acute stroke DWI application are still the bottlenecks. In this work, we established the first large annotated open-source database of 2,888 clinical acute stroke MRIs (Chapter 2) to train and develop ML models for automatic stroke lesion detection and segmentation in clinical acute stroke MRI (Chapter 3). For automatic measurement of infarcted arterial territories, the first digital 3D deformable brain arterial territory atlas was created (Chapter 4). In addition, a fully automatic ML system is created to generate automatic radiological reports (Chapter 5 and 6) for calculation of ASPECTS, prediction and quantification of infarcted arterial and anatomical regions, and estimation of hydrocephalus presented in acute stroke MRI. The complete ML system in this work runs locally in real time with minimal computational requirements. It is publicly available and readily useful for non-expert users

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Methods and Applications for Summarising Free-Text Narratives in Electronic Health Records

As medical services move towards electronic health record (EHR) systems the breadth and depth of data stored at each patient encounter has increased. This growing wealth of data and investment in care systems has arguably put greater strain on services, as those at the forefront are pushed towards greater time spent in front of computers over their patients. To minimise the use of EHR systems clinicians often revert to using free-text data entry to circumvent the structured input fields. It has been estimated that approximately 80% of EHR data is within the free-text portion. Outside of their primary use, that is facilitating the direct care of the patient, secondary use of EHR data includes clinical research, clinical audits, service improvement research, population health analysis, disease and patient phenotyping, clinical trial recruitment to name but a few.This thesis presents a number of projects, previously published and original work in the development, assessment and application of summarisation methods for EHR free-text. Firstly, I introduce, define and motivate EHR free-text analysis and summarisation methods of open-domain text and how this compares to EHR free-text. I then introduce a subproblem in natural language processing (NLP) that is the recognition of named entities and linking of the entities to pre-existing clinical knowledge bases (NER+L). This leads to the first novel contribution the Medical Concept Annotation Toolkit (MedCAT) that provides a software library workflow for clinical NER+L problems. I frame the outputs of MedCAT as a form of summarisation by showing the tools contributing to published clinical research and the application of this to another clinical summarisation use-case ‘clinical coding’. I then consider methods for the textual summarisation of portions of clinical free-text. I show how redundancy in clinical text is empirically different to open-domain text discussing how this impacts text-to-text summarisation. I then compare methods to generate discharge summary sections from previous clinical notes using methods presented in prior chapters via a novel ‘guidance’ approach.I close the thesis by discussing my contributions in the context of state-of-the-art and how my work fits into the wider body of clinical NLP research. I briefly describe the challenges encountered throughout, offer my perspectives on the key enablers of clinical informatics research, and finally the potential future work that will go towards translating research impact to real-world benefits to healthcare systems, workers and patients alike