Computer modeling and signal analysis of cardiovascular physiology

This dissertation aims to study cardiovascular physiology from the cellular level to the whole heart level to the body level using numerical approaches. A mathematical model was developed to describe electromechanical interaction in the heart. The model integrates cardio-electrophysiology and cardiac mechanics through excitation-induced contraction and deformation-induced currents. A finite element based parallel simulation scheme was developed to investigate coupled electrical and mechanical functions. The developed model and numerical scheme were utilized to study cardiovascular dynamics at cellular, tissue and organ levels. The influence of ion channel blockade on cardiac alternans was investigated. It was found that the channel blocker may significantly change the critical pacing period corresponding to the onset of alternans as well as the alternans’ amplitude. The influence of electro-mechanical coupling on cardiac alternans was also investigated. The study supported the earlier assumptions that discordant alternans is induced by the interaction of conduction velocity and action potential duration restitution at high pacing rates. However, mechanical contraction may influence the spatial pattern and onset of discordant alternans. Computer algorithms were developed for analysis of human physiology. The 12-lead electrocardiography (ECG) is the gold standard for diagnosis of various cardiac abnormalities. However, disturbances and mistakes may modify physiological waves in ECG and lead to wrong diagnoses. This dissertation developed advanced signal analysis techniques and computer software to detect and suppress artifacts and errors in ECG. These algorithms can help to improve the quality of health care when integrated into medical devices or services. Moreover, computer algorithms were developed to predict patient mortality in intensive care units using various physiological measures. Models and analysis techniques developed here may help to improve the quality of health care

A Neural Network Model for Mortality Prediction in ICU

Abstrac

NUMERICAL SIMULATION OF ELECTROMECHANICAL DYNAMICS IN PACED CARDIAC TISSUE

ABSTRACT We INTRODUCTION Sudden cardiac arrest (SCA), a leading cause of death in the industrialized world, kills over 350,000 Americans each year. SCA can often result from fatal arrhythmias such as ventricular fibrillation. Much research has demonstrated the connections between the fatal arrhythmias and the instabilities of the cardiac electrophysiology. Alternans of action potential duration (APD) is a well-known indicator of the instabilities. Discordant alternans, a phenomenon of two spatially distinct regions exhibiting APD alternans of opposite phases, amplifies dispersion of repolarization and can lead to conduction block and reentr

A Fully Coupled Model for Electromechanics of the Heart

We present a fully coupled electromechanical model of the heart. The model integrates cardiac electrophysiology and cardiac mechanics through excitation-induced contraction and deformation-induced current. Numerical schemes based on finite element were implemented in a supercomputer. Numerical examples were presented using a thin cardiac tissue and a dog ventricle with realistic geometry. Performance of the parallel simulation scheme was studied. The model provides a useful tool to understand cardiovascular dynamics

Reconstruction of physiological signals using iterative retraining and accumulated averaging of neural network models

Abstract. Real-time monitoring of vital physiological signals is of significant clinical relevance. Disruptions in the signals are frequently encountered and make it difficult for precise diagnosis. Thus, the ability to accurately predict/recover the lost signals could greatly impact medical research and application. We have developed new techniques of signal reconstructions based on iterative retraining and accumulated averaging of neural networks. The effectiveness and robustness of these techniques are demonstrated using data records from the Computing in Cardiology/PhysioNet Challenge 2010. The average correlation coefficient between prediction and target for 100 records of various target signals is about 0.9. We have also explored influences of a few important parameters on the accuracy of reconstructions. The developed techniques may be used to detect changes in patient state and to recognize intervals of signal corruption