Cardiac Classification with Multi-Scale Convolutional Neural Network From Paper ECG

In cardiology, the classification of electrocardiograms (ECGs) or heartbeats serves as a vital instrument. Techniques grounded in deep learning for ECG signal examination support medical professionals in swiftly identifying heart ailments, thereby aiding in life preservation. The present investigation endeavors to convert a dataset comprising ECG record images into time-series signals, followed by the implementation of deep learning (DL) methodologies on this transformed dataset. Cutting-edge DL methodologies are introduced for categorizing ECG signals across diverse cardiac categories. This work examines and juxtaposes various DL architectures, encompassing a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning framework leveraging autoencoders. Training of these models occurs on a dataset derived from ECG tracings of individuals at multiple medical facilities in Pakistan. Initially, the ECG images undergo digitization with segmentation of lead II heartbeats, after which the resulting signals are inputted into the advocated DL models for categorization. Within the array of DL models evaluated herein, the advocated CNN architecture attains the peak accuracy of > 90%. This architecture exhibits superior precision and expedited inference, facilitating instantaneous and unmediated surveillance of ECG signals acquired via electrodes (sensors) positioned on various bodily regions. Employing the digitized variant of ECG signals, as opposed to pictorial representations, for cardiac arrhythmia categorization empowers cardiologists to deploy DL models directly onto signals emanating from ECG apparatus, enabling contemporaneous and precise ECG oversight.