Analysis of beat-to-beat ventricular repolarization duration variability from electrocardiogram signal

© 2015 Dr. Mohammad Hasan ImamElectrocardiogram (ECG) signal analysis is a ubiquitous tool for investigating the heart’s function. ECG indicates the cardiac action potential propagation characteristics within the heart chambers (from the atria to the ventricles) and any irregularity in ECG, which can be graphically detected, represents abnormality in the polarization process (i.e. depolarization and repolarization) of the cardiac muscle cell. The lower chambers of the heart termed as ventricles perform the main pumping function by directing blood to the lungs and the peripheral system including the brain and all other body parts. Abnormality in ventricular function is critical, which can cause fatal cardiac diseases, where the heart loses its normal function to maintain proper circulation. Depolarization and repolarization process of the cardiac action potential activates the contraction and relaxation operations of the heart, whose durations can be detected from the temporal distance between different ECG waves (i.e. QRS duration, RR interval, QT interval). Abnormalities in these temporal durations calculated by the different time series variability measures indicate problems in normal cardiac muscle polarization process. Ventricular repolarization (VR) duration contains both the depolarization and repolarization durations, though the duration of depolarization is quite small in comparison to that of repolarization. Prolongation of VR duration from a normal baseline indicates the sign of ventricular dysfunction, which might initiate fatal ventricular arrhythmias (ventricular tachycardia and ventricular fibrillation). VR duration variability represented by QT interval time series variability (QTV) in ECG contains crucial information about the dynamics of VR process, which characterises the function of the ventricles. QTV is affected inherently by heart rate, respiration, autonomic nervous system, age, gender and different genetical disorder of cardiac ion channels. Therefore, variation of VR duration may be affected by several factors, which cannot be analysed properly by using gross time series variability measures (i.e. mean, standard deviation). This thesis investigates different QTV analysis techniques from QT interval time series extracted from ECG, which investigate how different physiological and pathological conditions affect the normal VR process and how this alteration can be used as a subclinical predictive analysis technique of different cardiac diseases. In this thesis, model based QTV analysis techniques were investigated and respiratory information based modelling approach is proposed for analysing dynamic QTV in healthy ageing and stressed condition. ECG derived respiration (EDR) was found a valid surrogate of respiration in modelling QTV, which provide only ECG based modelling technique for QTV by removing the need for collecting respiration signal separately. EDR based modelling was found very effective in describing QTV changes with denervation of ANS branches (parasympathetic and sympathetic) in a prevalent complexity in diabetic patients (Cardiac autonomic neuropathy (CAN)). These findings can describe the effect of ANS modulation on QTV, which is important for validating QTV as a non-invasive measure of sympathetic nervous system modulation on the ventricles. A novel approach describing systolic and diastolic time interval interaction derived from the VR duration (i.e. QT interval) and cardiac cycle duration (i.e. RR interval) in ECG was found very effective in subclinical CAN detection and CAN progression. This finding proves the feasibility of ECG based VR duration based measures in analysing left ventricular function of blood circulation. A novel beat-to-beat QT-RR interaction analysis technique was developed, which was found very useful in analysing age related alteration in the normal VR process. The proposed measure can also be used for determining the QTV component that is not affected directly by the RR intervals (i.e. QTV component independent of heart rate variability), which is more sensitive to the sympathetic modulation of the ventricles. Moreover, this technique showed promising results in the analysis of dynamical QTV changes before arrhythmogenesis, which can be used for predictive analysis of ventricular arrhythmias. Finally, the proposed technique for QTV analysis in this thesis will help to design low-cost and effective ECG based ambulatory care system that can be used for subclinical cardiovascular disease detection