Publication

Abstract : In recent days, Artificial Intelligence is one of the emerging fields. Conventional machine learning algorithms require feature extraction, which is hand-crafted based on the required application, creates the need for the development of deep learning algorithms. In general, deep learning algorithms learn the features based on the given input data. Recently, deep learning is applied in various domains such as computer vision, speech processing, cybersecurity, healthcare, etc. The development of deep learning applications in health care domain is increasing rapidly. One of the emerging healthcare applications is cardiovascular disease classification from ECG signals using deep learning. Myocardial infarction is a predominant type of cardiovascular disease, which affects one-third of the population annually. Conventional machine learning and deep learning techniques are used for diagnosis of myocardial infarction. In this paper, we explore the deep residual CNN, which is designed for arrhythmia classification and transferred the knowledge learnt from arrhythmia to classify myocardial disease. The existing deep residual CNN architecture is retrained with ECG beats of myocardial infarction disease, which is proposed in the present work. We used the publically available kaggle dataset for the experimental evaluation of the proposed method. The present work improves the existing performance of the myocardial disease classification from 95% to 99%.