Sensors, Vol. 24, Pages 2484: Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets

Sensors, Vol. 24, Pages 2484: Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets Sensors doi: 10.3390/s24082484 Authors: Shoaib Sattar Rafia Mumtaz Mamoon Qadir Sadaf Mumtaz Muhammad Ajmal Khan Timo De Waele Eli De Poorter Ingrid Moerman Adnan Shahid ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The p...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research