ECG Signals Classification Using MFCC Coefficients and ANN Classifier

recently, the detection of cardiac pathologies from the electrocardiogram, i.e. recording the electrical activity of the heart muscle, requires the use of more accurate tools for signal processing and decision making. In this context, this paper presents the design of a cardiac pathologies detection system with high precision of calculation and decision, which consists of the Mel Frequency Coefficient Cepstrum algorithms like fingerprint extractor (or features) of the cardiac signal and the algorithms of artificial neural network multilayer perceptron type multilayer perceptron classifier as fingerprints extracted into two classes: normal or abnormal. The design and testing of the proposed system are performed on two types of data extracted from the MIT-BIH database: a learning base containing labeled data (electrocardiogram normal and diseased) and another test base containing non-labeled data. The experimental results have shown that the proposed system combines between the respective advantages of the descriptor Mel Frequency Cepstrum Coefficient and the Multilayer Perceptron classifier.