Fuzzy-Hybrid Neural Network Based ECG Beat Recognition Using Three Different Types of Feature Sets

Abstract

This paper represents the application on the fuzzy-hybrid neural network for electrocardiographic (ECG) beat recognition and classification. We proposed the approach to ECG beat recognition that is based on QRS complex classification. Instead of the original QRS waveform, we have used three different types of QRS features sets. The main objective of this work was to develop a technique which was less sensitive to the morphological variation of the QRS waveform. Linear predictive coefficients (LPC), third-order cumulant-based auto regressive coefficient (AR), and the variance of the wavelet transform detail coefficients of the isolated QRS complexes are used as the features. It will be shown that the wavelet transform based approach is better than the other techniques. The main properties of the proposed method are simplicity, moderate recognition rate, and fast computation time.