Research Article
Non Invasive Detection of Coronary Artery Disease Using PCG and PPG
@INPROCEEDINGS{10.1007/978-3-319-49655-9_32, author={Rohan Banerjee and Anirban Choudhury and Shreyasi Datta and Arpan Pal and Kayapanda Mandana}, title={Non Invasive Detection of Coronary Artery Disease Using PCG and PPG}, proceedings={eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers}, proceedings_a={EHEALTH360}, year={2017}, month={1}, keywords={Coronary Artery Disease Phonocardiogram Classification Photoplethysmogram Transfer function}, doi={10.1007/978-3-319-49655-9_32} }- Rohan Banerjee
Anirban Choudhury
Shreyasi Datta
Arpan Pal
Kayapanda Mandana
Year: 2017
Non Invasive Detection of Coronary Artery Disease Using PCG and PPG
EHEALTH360
Springer
DOI: 10.1007/978-3-319-49655-9_32
Abstract
Coronary Artery Disease (CAD) kills more than a million of people every year. However, there is no significant marker for identifying CAD patients unobtrusively. In this paper, we propose a methodology for non invasive screening of CAD patients from heart sound analysis. Instead of segregating the diastolic heart sound as mentioned in prior arts, the proposed methodology extracts spectral features from the entire phonocardiogram (PCG) signal, broken into small overlapping windows. Support vector machine (SVM) is used for classification. Our methodology produces 80% classification accuracy on a dataset of 25 subjects, containing PCG data of both cardiac an non cardiac patients as well as healthy subjects. Results also reveal that a simple transfer function can be formed to identify the CAD patients if photoplethysmogram (PPG) signal is available simultaneously along with PCG.
