NDLI: Childhood musical murmur classification with Support Vector Machine technique

Content Provider	IEEE Xplore Digital Library
Author	Chatunapalak, I. Phatiwuttipat, P. Kongprawechnon, W. Tungpimolrut, K.
Copyright Year	2012
Description	Author affiliation: School of Information, Computer and Communication Technology (ICT), Sirindhorn International Institute of Technology (SIIT), Thammasat University, Pathum Thani, Thailand 12000 (Chatunapalak, I.; Phatiwuttipat, P.; Kongprawechnon, W.) \|\| National Electronics and Computer Technology Center (NECTEC), NSTDA, Pathum Thani, Thailand 12120 (Tungpimolrut, K.)
Abstract	Musical murmur, a typical occurrence of heart sound, frequently found in the pediatric population reflects no harm compared to murmur. 8 out of 10 children are then by nature prone to this physiological phenomenon. Correctly distinguishing musical murmur from pathological one is a critical assessment to avoid unnecessary treatment at vain cost disbursement and ease parents' concern. This research investigates the algorithm development for automated high-yield classification with less time on analysis. 493 recorded heart sounds (HSs), comprised of 144 normal HSs, 141 musical murmurs, and 208 pathological murmurs, are first undergone through Preprocessing stage to form consistency of each incoming HSs. Derived HSs are extracted with Wavelet Packet (WP), as to form significant characteristics as a feature vector set. Then, Principal Component Analysis (PCA) is applied to the compactness of feature set in which the dimensionality reduction is attained through data-driven retrieval as to signify the most relevant features of HS record. Support Vector Machine (SVM) classifier trained by Radial Basis Function (RBF) kernel is constructed to detect and discriminate musical murmur from other murmur types by cross-validation process. Finally, classifier performance is evaluated by Confusion Matrix where an accuracy rate of 90.26% is ensured for the potential classification of model.
Starting Page	1
Ending Page	4
File Size	339758
Page Count	4
File Format	PDF
ISBN	9781467320269
e-ISBN	9781467320252
DOI	10.1109/ECTICon.2012.6254138
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-16
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart Entropy cross-validation Classification algorithms wavelet packet Support vector machines Accuracy support vector machine RBF kernel Feature extraction Wavelet packets Shannon's entropy musical murmur principal component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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