Non-Invasive Detection of Coronary Artery Disease Based on Clinical Information and Cardiovascular Signals: A Two-Stage Classification Approach

In this paper we propose a novel process flow of a low-cost, non-invasive screening system for identifying Coronary Artery Disease (CAD) patients using a two-stage classification approach. A statistical rule engine is designed based on patient demography and medical history which is applied at the first stage of the proposed classification system. The misclassification error at this stage is reduced at the second stage based on numerical features extracted from multiple cardiovascular signals. Two sets of features are extracted from phonocardiogram (PCG) and photoplethysmogram (PPG) signals, collected from each subject for creating two independent Support Vector Machine (SVM) classifiers. Outcomes of the two classifiers are fused at the decision level for final prediction at second stage based on absolute distance of the test data-point from its respective SVM hyperplane. Results show that the proposed approach achieves sensitivity of 0.92 and specificity of 0.90 in classifying CAD patients on a hospital dataset of 99 subjects including CAD and non-CAD patients.