Hybrid wavelet-mathematical morphology feature extraction for heartbeat classification

The paper presents the classification performance of an automatic classifier of the electrocardiogram (ECG) for the detection abnormal beats with new concept of feature extraction stage. Feature sets were based on ECG morphology and RR - intervals. This paper compares two strategies for classification of annotated QRS complexes: based on original ECG morphology features and proposed new approach -based on preprocessed ECG morphology features. The mathematical morphology filtering and wavelet transform is used for the preprocessing of ECG signal. Within this framework, the problem of choosing an appropriate wavelet in signal preprocessing was studied. Configuration adopted a Kohonen self-organizing maps (SOM) and Support Vector Machine (SOM) for analysis of signal features and clustering. In this study, a classifiers was developed with SOMLVQ and SVM algorithms using the data from the records recommended by ANSI/AAMI EC57 standard. The performance of the algorithm is evaluated on the MIT-BIH Arrhythmia Database following the AAMI recommendations. Using this method the results of identify beats either as normal or arrhythmias was improved.