An approach to cardiac arrhythmia analysis using hidden Markov models

A new approach to ECG arrhythmia analysis is described. It is based on hidden Markov modeling (HMM), a technique successfully used since the mid 1970s to model speech waveforms for automatic speech recognition. Many ventricular arrhythmias can be classified by detecting and analyzing QRS complexes and determining R-R intervals. Classification of supraventricular arrhythmias, however, often requires detection of the P wave in addition to the QRS complex. The HMM approach combines structural and statistical knowledge of the ECG signal in a single parametric model. Model parameters are estimated from training data using an iterative, maximum-likelihood reestimation algorithm. Initial results suggest that this approach can provide improved supraventricular arrhythmia analysis through accurate representation of the entire beat, including the P-wave.