Certain investigations on ecg feature extraction using wavelet transform and classification of ventricular arrhythmia

Abstract

The Electrocardiogram is routinely used in clinical practice which newlinedescribes the electrical activity of the heart In physical checkups at hospitals newlinephysicians record the ECG after the patient has exercised to check hisher newlinecardiac condition The Holter ECG device is used most frequently for newlinerecoding the ECG Physicians apply the ECG device to a patient when they newlineneed to monitor hisher ECG to find the few abnormal cycles in the ECG newlinethroughout the day Physicians then interpret the shapes of those waves and newlinecomplexities They calculate parameters to determine whether the ECG shows newlinesigns of cardiac disease or not Recognition of the fiducial points and newlinecalculation of the parameters is a tedious routine for the physicians newlineApproximately 100000 ECG cycles are recorded per patient in a day The newlinephysicians have to interpret this large amount of ECG data to search only a newlinefew abnormal beats in the ECG Physicians may overlook some abnormal newlinecycles due to fatigue and human error in interpreting such a large amount of newlinedata Therefore there is an urgent need for an automatic ECG interpreting newlinesystem to help to reduce the burden of the physicians newline