PCG Classification Using Multidomain Features and SVM Classifier
Joint Authors
Li, Ting
Dai, Ziyin
Jiang, Yuanlin
Liu, Chengyu
Tang, Hong
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-09
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
This paper proposes a method using multidomain features and support vector machine (SVM) for classifying normal and abnormal heart sound recordings.
The database was provided by the PhysioNet/CinC Challenge 2016.
A total of 515 features are extracted from nine feature domains, i.e., time interval, frequency spectrum of states, state amplitude, energy, frequency spectrum of records, cepstrum, cyclostationarity, high-order statistics, and entropy.
Correlation analysis is conducted to quantify the feature discrimination abilities, and the results show that “frequency spectrum of state”, “energy”, and “entropy” are top domains to contribute effective features.
A SVM with radial basis kernel function was trained for signal quality estimation and classification.
The SVM classifier is independently trained and tested by many groups of top features.
It shows the average of sensitivity, specificity, and overall score are high up to 0.88, 0.87, and 0.88, respectively, when top 400 features are used.
This score is competitive to the best previous scores.
The classifier has very good performance with even small number of top features for training and it has stable output regardless of randomly selected features for training.
These simulations demonstrate that the proposed features and SVM classifier are jointly powerful for classifying heart sound recordings.
American Psychological Association (APA)
Tang, Hong& Dai, Ziyin& Jiang, Yuanlin& Li, Ting& Liu, Chengyu. 2018. PCG Classification Using Multidomain Features and SVM Classifier. BioMed Research International،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1126444
Modern Language Association (MLA)
Tang, Hong…[et al.]. PCG Classification Using Multidomain Features and SVM Classifier. BioMed Research International No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1126444
American Medical Association (AMA)
Tang, Hong& Dai, Ziyin& Jiang, Yuanlin& Li, Ting& Liu, Chengyu. PCG Classification Using Multidomain Features and SVM Classifier. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1126444
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126444