Identifying coronary artery disease from photoplethysmogram

Abstract

This paper presents the idea of a non invasive screening system for identifying Coronary Artery Disease (CAD) patients from fingertip Photoplethysmogram (PPG) signal. A combined feature set, related to heart rate variability (HRV) as well as shapes of PPG waveform has been defined for distinguishing CAD and non CAD subjects. Support Vector Machine (SVM) is used for classification. Our methodology yields sensitivity and specificity scores of 0.82 and 0.88 respectively in identifying CAD patients on a corpus of 112 subjects, selected from MIMIC II dataset. Further, we achieved sensitivity and specificity scores of of 0.73 and 0.87 on another dataset of 30 subjects, collected from an urban hospital using commercial oximeter device.