An artificial neural network using the Pan Tompkins algorithm successfully analyzed ECG signals from the MIT database to diagnose arrhythmias based on QRS peak variations.
An artificial neural network combined with the Pan Tompkins algorithm can effectively classify cardiac arrhythmias from ECG signals.
Cardiac arrhythmia indicates abnormal electrical activity of heart that can be a great threat to human. So it needs to be identified for clinical diagnosis and treatment. Analysis of ECG signal plays an important role in diagnosing cardiac diseases. An efficient method of analysing ECG signal and predicting heart abnormalities have been proposed in this paper. In the proposed scheme, at first the QRS components have been extracted from the noisy ECG signal by rejecting the background noise. This is done by using the Pan Tompkins algorithm. The second task involves calculation of heart rate and detection of tachycardia, bradycardia, asystole and second degree AV block from detected QRS peaks using MATLAB. The results show that from detected QRS peaks, arrhythmias which are based on increase or decrease in the number of QRS peak, absence of QRS peak can be diagnosed. The final task is to classify the heart abnormalities according to previous extracted features. The back propagation (BP) trained feed-forward neural network has been selected for this research. Here, data used for the analysis of ECG signal are from MIT database.
Debnath et al. (Thu,) conducted a other in Cardiac arrhythmia. Artificial Neural Network was evaluated on Detection and classification of heart abnormalities (tachycardia, bradycardia, asystole, and second degree AV block). An artificial neural network using the Pan Tompkins algorithm successfully analyzed ECG signals from the MIT database to diagnose arrhythmias based on QRS peak variations.