CHAMPS: Cardiac health Hypergraph Analysis using Multimodal Physiological Signals

State-of-the-art methods have reported various features for the non-invasive screening of Coronary Artery Disease (CAD). In this paper, we propose a novel approach to represent such features extracted from multiple physiological signals using hypergraph. Firstly, the biological and statistical interconnections among Photoplethysmogram (PPG) and Phonocardiogram (PCG) features are exploited by connecting them as hyperedges. Then, metadata features (age, weight and height) are connected using hyperedges with the rest of the features. Hypergraph based formalism provides greater flexibility in capturing the interrelationships among different features as compared to the graph counterpart. Finally, hypergraph laplacian as a derived feature is applied to classify CAD against non-CAD. The proposed method is validated on PPG and PCG data collected in a hospital setup. The results reveal 98% Sensitivity and 82% Specificity, leading to 92% classification accuracy.