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AuthorZabihi, Morteza
AuthorRad, Ali Bahrami
AuthorKiranyaz, Serkan
AuthorGabbouj, Moncef
AuthorKatsaggelos, Aggelos K.
Available date2021-04-22T10:16:23Z
Publication Date2016
Publication NameComputing in Cardiology
ResourceScopus
ISSN23258861
URIhttp://hdl.handle.net/10576/18297
AbstractPhonocardiogram (PCG) signal is used as a diagnostic test in ambulatory monitoring in order to evaluate the heart hemodynamic status and to detect a cardiovascular disease. The objective of this study is to develop an automatic classification method for anomaly (normal vs. abnormal) and quality (good vs. bad) detection of PCG recordings without segmentation. For this purpose, a subset of 18 features is selected among 40 features based on a wrapper feature selection scheme. These features are extracted from time, frequency, and time-frequency domains without any segmentation. The selected features are fed into an ensemble of 20 feedforward neural networks for classification task. The proposed algorithm achieved the overall score of 91.50% (94.23% sensitivity and 88.76% specificity) and 85.90% (86.91% sensitivity and 84.90% specificity) on the train and unseen test datasets, respectively. The proposed method got the second best score in the PhysioNet/CinC Challenge 2016.
Languageen
PublisherIEEE Computer Society
SubjectCardiology
Diagnosis
Phonocardiography
Ambulatory monitoring
Automatic classification
Cardio-vascular disease
Classification tasks
Diagnostic tests
Phonocardiograms
Quality detection
Time frequency domain
Feedforward neural networks
TitleHeart sound anomaly and quality detection using ensemble of neural networks without segmentation
TypeConference Paper
Pagination613-616
Volume Number43
dc.accessType Abstract Only


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