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Qualitative features selection techniques by profiling statistical features of ECG for classification of heart beats

Chinmay Chandrakar, Monisha Sharma

The measurement of the electrical activity of the heart can be done with electrocardiogram (ECG). Automatic arrhythmia-diagnosis systems which results in high accuracy rates for inside and outside patient are still an important area of research. The accuracy of such system depends on accuracy of the classification system. All this classification system required qualitative features for classification. This paper proposed a unique method of profiling of statistical features for selection of qualitative features through ECG waveform. The proposed approach for selection of qualitative features can classify and differentiate abnormal heartbeats and normal (NORM). Left bundle branch block (LBBB), right bundle branch block (RBBB), ventricular premature contractions (VPC) and atrial premature contractions (APC) comes under abnormal heart beats.