Using Deep Learning to Classify Arrhythmias - The Cardiology Advisor

Efforts to automate the analysis of electrocardiograms (ECGs) date back to the 1950s when researchers first converted ECG signals from analog to digital form, enabling the subsequent creation of algorithms that could be used in computer-interpreted ECG (CIE).1,2 With continued technological advances, the use of CIE has become so common that more than 100 million ECGS are interpreted by computer each year in the United States.2 However, conventional CIE models require over-reading by a physician, and despite these checks, certain ECG features may be missed. A growing body of research highlights the potential value of deep learning-based CIE, which could detect features that may be overlooked or undetectable by a physician reader.3,4 "Deep learning is a subfield of machine learning which tends to solve a problem end to end to eliminate the need for domain expertise and to fully explore ECG features from raw ECG data," according to an article co-authored by Shijie Zhou, PhD, assistant research scientist in the department of biomedical engineering and the Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE) Institute at Johns Hopkins University in Baltimore.5 "Deep-learning models use neural networks to capture only the most important features from the input data and disregard redundant input features by means of network pruningto maintain model accuracy."