Artificial Intelligence Assisted Cardiac Signal Analysis for Heart Disease Prediction

Cardiovascular Disease (CVD) is the group of disorders of the heart and blood vessels and is a major health problem worldwide. It is highly essential to design the reliable and robust cardiac health monitoring systems that can detect any kind of cardiac abnormalities accurately within an acceptable time frame. However, identifying those irregularities with input modalities like Magnetic Resonance Imaging (MRI), Computerized Tomography (CT) scan, and Echocardiography (Echo) is not efficient due to their time-consuming, complex and expensive image acquisition methods. Therefore, there is a growing demand of affordable and user-intensive cardiac data analysis in the form of Electrocardiogram (ECG) and Seismocardiogram (SCG) signals. In this chapter, a detailed survey of various Mathematical and Artificial Intelligence (AI) based cardiac signal analysis models for coronary disease prediction is discussed. Delineation of candidate feature points in both ECG and SCG data before the abnormality prediction is well-studied. Finally, a comparison study of the recent developments in cardiac abnormality detection and their limitations is included in this chapter.