ECG Feature Extraction and Classification Using Cepstrum and Neural Networks

An integrated system for ECG diagnosis that combines cepstrum coefficient method for feature extraction from long-term ECG signals and artificial neural network (ANN) models for the classification is presented in this paper. Unlike the previous methods using only one single heartbeat for analysis, we analyze a meaningful segment ECG data, usually containing 5-6 heartbeats, to obtain the corresponding cepstrum coefficients and classify the cardiac systems through ANN models. Utilizing the proposed method, one can identify characteristics hiding inside an ECG signal and then classify the signal as well as diagnose the abnormalities. To evaluate this method, various types of ECG data from the MIT/BIH database were used for verification. The experimental results showed that the accuracy of diagnosing caridac disease was above 97.5%. The proposed method successfully extracted the corresponding feature vectors, distinguished the difference and classified ECG signals.