ECG signal feature extraction and classification using Harr Wavelet Transform and Neural Network

The heart is one of the crucial parts of a human being. The heart produces electrical signals and these cycles of electrical signals are called as cardiac cycles. The graphical recording of the cardiac cycle produced by an Electrocardiograph is called as Electro cardio gram (ECG) signal. The Electrocardiogram signal is used to diagnose the irregularity in heart beat. 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 an algorithm has been develop to detect the five abnormal beat 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. Harr Wavelet Transform (HWT) is used in order to extract features from the ECG signal. Preprocessing and the classification of ECG signals is done using Forward Feed Neural Network Finally, the MIT-BIH [10] database is used to evaluate the proposed algorithm.