A multi-modal approach for non-invasive detection of coronary artery disease

Coronary Artery Disease (CAD) is a leading cause of death globally. Coronary angiography, the clinical diagnosis for CAD involves a surgery and admission to hospital. While this is a proven gold standard, having a less exact low-cost non-invasive screening method would be very helpful in mass diagnosis and pre-diagnosis. However, all physiological manifestations of CAD either appear late in the time-curve or are non-specific surrogate markers. With the advent of Artificial Intelligence (AI), there is new hope using multi-modal non-invasive sensing and analysis. In this paper, we combine domain knowledge with AI based data analysis to propose a novel two-stage approach that effectively incorporates multiple CAD markers in various non-invasive cardiovascular signals for an improved diagnosis system. At first stage, a hierarchical rule-engine identifies the high cardiac risk population using patient demography and medical history, who are further analysed at the second stage using numeric features from various cardiovascular signals. Results show that the proposed approach achieves sensitivity = 0.96 and specificity = 0.91 in classifying CAD patients on an in-house hospital dataset, recorded using commercially available sensors.