Publication

Abstract : The Electrocardiogram (ECG) is a graphical recording of the electrical signals generated by the heart. The signals are generated when the cardiac muscles depolarize in response to electrical impulses generated by the pacemaker. In this work, we propose an efficient method to monitor and classify the ECG signals. The initial task carried out was to eliminate the noise, which involved extracting the required cardiac components by rejecting the background noise. The second task was to perform R peak detection, which was achieved by using the Windowed Short Time Fourier Transform (STFT). The Heart Rate Variability (HRV) was also found by calculating the difference between two simultaneous R-Peaks. The simulations were carried out in the MATLAB environment. The experiments were carried out using data from the MIT-BIH Database. This paper proposes an algorithm to monitor cardiac atrial fibrillation, which is an essential precursor to myocardial infarction.