Providing an Efficient Algorithm for Finding R Peaks in ECG Signals and Detecting Ventricular Abnormalities With Morphological Features

Ventricular arrhythmias are one of the most important causes of annual deaths in the world, which may lead to sudden cardiac deaths. accurate and early diagnosis of ventricular arrhythmias in heart diseases is essential for preventing mortality in cardiac patients. ventricular activity on the electrocardiogram (ecg) signal is in the interval from the beginning of qrs complex to t wave end. variations in the ecg signal and its features may indicate heart condition of patients. the first step to extract features of ecg in time domain is finding r peaks. in this paper, a combination of two algorithms of pan–tompkins and state logic machine has been used to find r peaks in heart signals for normal sinus signals and ventricular abnormalities. then, a healthy or sick beat may be realized by comparing the difference between r peaks obtained from two algorithms in each beat. the morphological features of the ecg signal in the range of qrs complex are evaluated. ventricular tachycardia (vt), ventricular flutter (vfl), ventricular fibrillation (vfi), ventricular escape beat (veb), and premature ventricular contractions (pvcs) are abnormalities studied in this paper. in the classification step, the support vector machine (svm) classifier with gaussian kernel (one in front of everyone) is used. accuracy percentages of ventricular abnormalities mentioned above and normal sinus rhythm are respectively obtained as 95.8%, 92.8%, 94.5, 98.9%, 91.5%, and 100%. the database of this paper has been taken from normal sinus rhythm and mit-scd banks available on physionet.org.