Detection of QRS complex in ECG signal based on classification approach

Electrocardiogram (ECG) signals are used to analyze the cardiovascular activity in the human body and have a primary role in the diagnosis of several heart diseases. The QRS complex is the most important and distinguishable component in the ECG because of its spiked nature and high amplitude. Automatic detection and delineation of the QRS complex in ECG is of extreme importance for computer aided diagnosis of cardiac disorder. Therefore, the accurate detection of this component is crucial to the performance of subsequent machine learning algorithms for cardiac disease classification. The aim of the present work is to detect the QRS wave from electrocardiogram (ECG) signals. Initially the baseline drift has been removed from the signal followed by the decomposition using continuous wavelet transform. Modulus maxima approach proposed by Mallat has been used to compute the Lipschitz exponent of the components. By using the property of R peak, having highest and prominent amplitude and Lipschitz exponents, we have applied the K means clustering technique to classify QRS complex. In order to evaluate the algorithm, the analysis has been done on MIT-BIH Arrhythmia database.