Mathematical Morphology Based ECG Feature Extraction for the Purpose of 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. Configuration adopted a Kohonen self-organizing maps (SOM) for analysis of signal features and clustering. In this study, a classifier was developed with SOM and learning vector quantization (LVQ) algorithms using the data from the records recommended by ANSI/AAMI EC57 standard. 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 is used for the preprocessing of ECG signal. The problem of choosing an appropriate structuring element of mathematical morphology filtering for ECG signal processing was studied. The performance of the algorithm is evaluated on the MIT-BIH Arrhythmia Database following the AAMI recommendations. Using this method the results of recognition beats either as normal or arrhythmias was improved.