Part of the book series:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ((LNICST,volume 181))
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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.
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Authors and Affiliations
Innovation Labs, Tata Consultancy Services Ltd., Kolkata, India
Rohan Banerjee, Anirban Dutta Choudhury, Shreyasi Datta & Arpan Pal
Fortis Hospital, Kolkata, India
Kayapanda M. Mandana
- Rohan Banerjee
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- Anirban Dutta Choudhury
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- Shreyasi Datta
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- Arpan Pal
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- Kayapanda M. Mandana
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Correspondence toRohan Banerjee.
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Applied Informatics in mHealth, National Technical University of Athens, Zografou, Greece
Kostas Giokas
Budapest University of Technology, Budapest, Hungary
Laszlo Bokor
University of Glasgow, Glaswow, United Kingdom
Frank Hopfgartner
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Banerjee, R., Choudhury, A.D., Datta, S., Pal, A., Mandana, K.M. (2017). Non Invasive Detection of Coronary Artery Disease Using PCG and PPG. In: Giokas, K., Bokor, L., Hopfgartner, F. (eds) eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-319-49655-9_32
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