A switchable scheme for ECG beat classification based on independent component analysis

A switchable scheme is proposed to discriminate different types of electrocardiogram (ECG) beats based on independent component analysis (ICA). The RR-interval serves as an indicator for the scheme to select between the longer (1.0 s) and the shorter (0.556 s) data samples for the following processing. Six ECG beat types, including 13900 samples extracted from 25 records in the MIT-BIH database, are employed in this study. Three conventional statistical classifiers are employed to testify the discrimination power of this method. The result shows a promising accuracy of over 99%, with equally well recognition rates throughout all types of ECG beats. Only 27 ICA features are needed to attain this high accuracy, which is substantially smaller in quantity than that in the other methods. The results prove the capability of the proposed scheme in characterizing heart diseases based on ECG signals.