Automated Detection of Abnormalities in ECG signals using Deep Neural Network

The electrocardiogram (ECG) is a diagnostic procedure that uses a skin electrode to record the heart's electrical activity. Heart diseases are the leading cause of mortality globally, and they have significant monetary cost. In the clinic, automated detection and classification technology of abnormalities in heart beats can assist physicians in making prompt and accurate medical diagnoses. This paper concentrates on two proposed models: Model A: 1-D 8-Layer CNN Model and Model B: 1- D CNN and the LSTM model to characterise ECG data into five categories: normal beat, right bundle branch block beat, left bundle branch block beat, premature ventricular contraction beat, and atrial premature beat. The data used to create and validate the models is obtained from the MIT-BIH Dataset, which comprises of 48 half-hour samples of two-lead continuous ECG signals obtained from 48 individuals. The dataset is divided into two files-.csv and.txt. For each sample, the.csv files include the readings collected from both leads. The experimental results show that Model A has an Accuracy = 99.68%, Precision = 99.23%, and a F1 score = 99.22%. And Model B has an Accuracy= 99.51%, Precision = 98.76%, and a F1 score = 98.76%. Our study aims to assist the medical sector by reducing the diagnoses time by automating the process of detecting arrythmias.