Classification enhancible grey relational analysis for cardiac arrhythmias discrimination.

This paper proposes a method for electrocardiogram (ECG) heartbeat recognition using classification enhancible grey relational analysis (GRA). The ECG beat recognition can be divided into a sequence of stages, starting with feature extraction and then according to characteristics to identify the cardiac arrhythmias including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. Gaussian wavelets are used to enhance the features from each heartbeat, and GRA performs the recognition tasks. With the MIT-BIH arrhythmia database, the experimental results demonstrate the efficiency of the proposed non-invasive method. Compared with artificial neural network, the test results also show high accuracy, good adaptability, and faster processing time for the detection of heartbeat signals.