ECG Beat Classification Using Mirrored Gauss Model

Accurate electrocardiogram (ECG) beat classification is essential for automated detection of arrhythmias. A novel classification algorithm of the ECG beats, applying mirrored Gauss model (MGM) had been proposed in this paper. The MGM has strong morphological representation ability for QRS complex waves using curve fitting. With the MGM, the width of QRS complex wave could be extracted and applied to ECG beat classification easily, effectively and automatically. It was proved by experiment carrying out using all of ECG records in MIT-BIH Arrhythmia Database that the MGM is a promising algorithm for ECG beat classification. The whole classification accuracy is 93.93% for normal beats and 93.94% for premature ventricular contraction (PVC) beats

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