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Detection and Identification of Irregularities in Human Heart Rate

Cardiovascular disease is the major reason for human death across the world. The physiological electrical activity of human heart is observed by Electrocardiogram (ECG) signals obtained from the body surface. These signals are used to diagnose the heart disease. We need to automatically detect the irregularities in heart rate. We propose Detection and Identification of irregularities in Human Heart Rate (DIIHR) using Pan-Tompkins method for R-Peak detection. The proposed model is assessed by taking ECG data from MIT/BIH arrhythmia database, the simulation is carried out using MATLAB. Further, classification is done by comparing heart beat as Normal heart rate and Abnormal heart rate. The Abnormal includes Slow heart rate (Bradycardia) and Fast heart rate (Tachycardia). The proposed method is able to achieve an average accuracy of 96.3% for Normal Heart Rate and 97.5% for Abnormal Heart Rate.