Classification of ECG Signal Using Hybrid Feature Extraction and Neural Network Classifier

Abstract

The heart is one of the crucial parts of a human being. The heart produces electrical signals and these cycles of electrical signals are normally called as cardiac cycles. The graphical recording of the cardiac cycles is known as Electrocardiogram (ECG) signal. The Electrocardiogram signal is used to diagnose the irregularity in heart beat which in turn can be used to identify heart problems. Automatic classification of ECG signals has applications in human-computer interaction, as well as in clinical application such as detection of key indicators of the onset of the certain illness. In this work, we have developed an algorithm to detect the five abnormal beat [2, 3] signals includes Left bundle branch block beat (LBBB), Right bundle branch block beat (RBBB), Premature Ventricular Contraction (PVC), Atrial Premature Beat (APB) and Nodal (junction) Premature Beat (NPB) along with the normal beat. In order to prepare an appropriate input vector for the neural classifier several pre processing stages have been applied. For efficient feature extraction we use hybrid feature extractor. The hybrid feature extraction is done in three steps, (i) Morphological based feature extraction (ii) Haar wavelet based feature extraction (iii) Tri-spectrum based feature extraction. The classification is done using Forward Feed Neural Network. Finally, the MIT-BIH [4] database is used to evaluate the proposed algorithm. The beat classification hybrid system (Hybrid + FFBN) based gives an accuracy is achieved 78 %, (Morp + FFBN) is achieved 62 %, (wavelet +FFBN) is achieved 65 %, (Spect + FFBN) is achieved 70 %, (Morp + Wavelet + FFBN) is achieved 62 %, (Morp + spect + FFBN) is achieved only 73 %.