ECG Data-Acquisition and classification system by using wavelet-domain Hidden Markov Models

This article is concerned with the classification of ECG pulses by using state of the art Continuous Density Hidden Markov Models (CDHMM´s). The ECG signal is simultaneously observed at three different level of focus by means of the Wavelet Transform (WT). The types of beat being selected are normal (N), premature ventricular contraction (V) which is often precursor of ventricular arrhythmia, two of the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF), atrial flutter (AFL), and normal rhythm (N). Both MLII and V1 derivations are used. Run time classification errors can be detected at the decoding stage if the classification of each derivation is different. These pulses are selected for a posterior physician analysis. Experimental results were obtained in real data from MIT-BIH Arrhythmia Database and also in data acquired from a developed low-cost Data-Acquisition System.