HEALTH OF THINGS MODEL FOR CLASSIFYING HUMAN HEART SOUND SIGNALS USING CO-OCCURRENCE MATRIX AND SPECTROGRAM
- Authors
- Arora, Vinay; Ng, Eddie Yin-Kwee; Leekha, Rohan Singh; Verma, Karun; Gupta, Takshi; Srinivasan, Kathiravan
- Issue Date
- Aug-2020
- Publisher
- World Scientific
- Keywords
- Heart sound signal; phonocardiogram; co-occurrence matrix; spectrogram
- Citation
- Journal of Mechanics in Medicine and Biology, v.20, no.6
- Journal Title
- Journal of Mechanics in Medicine and Biology
- Volume
- 20
- Number
- 6
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19467
- DOI
- 10.1142/S0219519420500402
- ISSN
- 0219-5194
1793-6810
- Abstract
- Cardiovascular diseases have become one of the world's leading causes of death today. Several decision-making systems have been developed with computer-aided support to help the cardiologists in detecting heart disease and thereby minimizing the mortality rate. This paper uses an unexplored sub-domain related to textural features for classifying phonocardiogram (PCG) as normal or abnormal using Grey Level Co-occurrence Matrix (GLCM). The matrix has been applied to extract features from spectrogram of the PCG signals taken from the Physionet 2016 benchmark dataset. Random Forest, Support Vector Machine, Neural Network, and XGBoost have been applied to assess the status of the human heart using PCG signal spectrogram. The result of GLCM is compared with the two other textural feature extraction methods, viz. structural co-occurrence matrix (SCM), and local binary patterns (LBP). Experimental results have proved that applying machine learning model to classify PCG signal on the dataset where GLCM has extracted the feature-set, the accuracy attained is greater as compared to its peer approaches. Thus, this methodology can go a long way to help the medical specialists in precisely and accurately assessing the heart condition of a patient.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19467)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.