Markov Models for Automated ECG Interval Analysis
Nicholas Peter Hughes, Lionel Tarassenko, Stephen John Roberts · 2003
This thesis proposes a new approach for the automated analysis of electrocardiogram (ECG) signals, based on the framework of probabilistic modelling. The approach makes use of a number of techniques from machine learning, speech recognition and time-frequency analysis. The problem is to estimate from an ECG signal a number of timing intervals which occur in every heartbeat. The accurate measurement and assessment of these intervals, and in particular the QT interval, is currently the gold standard for evaluating the cardiac safety of new drugs in clinical trials. The approach adopted in this thesis is to train a hidden Markov model (HMM) using a data set of ECG waveforms and the corresponding expert interval measurements. The trained model then serves two complementary purposes. Firstly, it enables the segmentation of test ECG signals through the use of the Viterbi algorithm. Secondly, it serves as a statistical description of ECG waveform normality. This allows the derivation of a confidence measure in the automated ECG interval values