12 - Transferable approach for cardiac disease classification using deep learning

Abstract

Cardiovascular disease is a condition that causes damage to the heart muscle, valves, rhythm, or blockage in the blood vessels. It requires early diagnosis, as it is the leading cause for the sudden death in humans. Electrocardiogram (ECG) is the most important biomedical signal used extensively by the cardiologist to diagnose cardiovascular disease. The classification of ECG signals helps physicians to make decisions in the diagnosis of cardiac diseases. There are many conventional machine learning and deep learning algorithms used in the literature for the automatic classification of ECG signals. Conventional machine learning algorithms require handcrafted features. There are many features such as morphological feature extraction, computation of RR interval, QRS peak detection, ST segment, ST distance, and amplitude computation. The classical machine learning algorithms used to classify the extracted features are shallow neural network, K nearest neighbor, support vector machines (SVM), random forest, and decision tree.