Review on the use of PPG Signals for Deep Learning based CVD Classification

Heart disease has emerged as one of the leading killers in the world in recent years. Statistics show that this disease claims the lives of almost 17 million people annually. Electrocardiograms (ECGs) are typically used to diagnose cardiac problems, but they are exceedingly expensive and difficult for individuals to afford, especially in rural regions. However, it is simpler, non-invasive, and less expensive to use Photoplethysmography (PPG) signals to find cardiac conditions and other bodily problems. Researchers have developed a lowcost, precise method using ECG data and artificial intelligence (AI) technology to overcome the issues with conventional invasive-based techniques for the detection of cardiac illness. Therefore, this research discusses low-cost AI-based methods for cardiovascular disease (CVD) detection utilizing PPG signal. This review will contribute to a more accurate classification of CVD from PPG Signals and the severity of CVD disease