Signal processing methods for characterisation of ventricular repolarisation using the surface electrocardiogram

This thesis investigates the mechanisms underlying drug-induced arrhythmia and pro- poses a new approach for the automated analysis of the electrocardiogram (ECG). The current method of assessing the cardiac safety of new drugs in clinical trials is by the measurement and analysis of the QT interval. However, the sensitivity and specificity of the QT interval has been questioned and alternative biomarkers based on T-wave mor- phology have been proposed in the literature. The mechanisms underlying drug effects on T-wave morphology are not clearly understood. Therefore, a combined approach of for- ward cardiac modelling and inverse ECG analysis is adopted to investigate the effects of sotalol, a compound known to have pro-arrhythmic effects, on ventricular repolarisation.
A computational model of sotalol and IKr, an ion channel that plays a critical role in ventricular repolarisation, was developed. This model was incorporated into a model of the human ventricular myocyte, and subsequently arranged in a 1-D fibre model of 200 cells. The model was used to assess the effect of sotalol on IKr, action potential duration and biomarkers of ventricular repolarisation derived from the simulated ECG.
In parallel, an automated ECG analysis method based on machine learning, signal processing and time-frequency analysis is developed to identify a number of fiducial points in ECG waveforms so that timing intervals and a smooth T-wave segment can be extracted for morphology analysis. The approach is to train a hidden Markov model (HMM) using a data set of ECG waveforms and the corresponding expert annotations. The signal is first encoded using the undecimated wavelet transform (UWT). The UWT coefficients are used for R-peak detection, signal encoding for the HMM and a wavelet de-noising procedure. Using the Viterbi algorithm, the trained HMM is then applied to a subset of the ECG signal to infer the fiducial points for each heart beat. Furthermore, a method for deriving a confidence measure based on the trained HMM is implemented so that a level of confidence can be associated with the automated annotations. Finally, the T-wave segment is extracted from the de-noised ECG signal for morphology characterisation.
This thesis contributes to the literature on automated characterisation of drug ef- fects on ventricular repolarisation in three different ways. Firstly, it investigates the mechanisms underlying the effects of drug inhibition of IKr on ventricular repolarisation as captured by the simulated ECG signal. Secondly, it shows how the combination of UWT encoding and HMM inference can be effectively used to segment 24-hour Holter ECG recordings. Evaluation of the segmentation algorithm on a clinical ECG data set demonstrates the ability of the algorithm to overcome problems associated with existing automated systems, and hence provide a more robust analysis of ECG signals. Finally, the thesis provides insight into the drug effects of sotalol on ventricular repolarisation as captured by biomarkers extracted from the surface ECG.

Keywords: electrocardiogram
signal processing
physiological modelling
Advisor: Tarassenko, L