Journal ArticleDOI
Classification of coronary artery diseased and normal subjects using multi-channel phonocardiogram signal
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TLDR
A new multi-channel PCG-based system to classify CAD-affected and normal subjects is proposed, and it does not require any additional reference signal, such as an electrocardiogram (ECG) signal.About:
This article is published in Biocybernetics and Biomedical Engineering.The article was published on 2019-04-01. It has received 36 citations till now. The article focuses on the topics: Phonocardiogram.read more
Citations
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Journal ArticleDOI
Automated Detection of Heart Valve Disorders From the PCG Signal Using Time-Frequency Magnitude and Phase Features
TL;DR: The results reveal that the proposed method has the average individual accuracy (IA) values of 98.83%, 97.66%, 91.16%, and 92.83% for normal, AS, MS, and MR classes.
Journal ArticleDOI
A two-stage classification model integrating feature fusion for coronary artery disease detection and classification
Muhammad Umar Khan,Sumair Aziz,Khushbakht Iqtidar,Galila F. Zaher,Shareefa A. ALGhamdi,Munazza Gull +5 more
TL;DR: Comparative analysis with existing approaches confirmed the reliability of the proposed method for categorizing CAD in general clinical environments and enhances the diagnosis performance by providing a second opinion during the medical examination.
Journal ArticleDOI
A fusion framework based on multi-domain features and deep learning features of phonocardiogram for coronary artery disease detection.
Han Li,Xinpei Wang,Changchun Liu,Qiang Zeng,Yansong Zheng,Xi Chu,Lianke Yao,Jikuo Wang,Yu Jiao,Chandan Karmakar +9 more
TL;DR: A novel feature fusion framework is proposed to provide a comprehensive basis for CAD diagnosis and achieved better classification performance than multi-domain features or deep learning features alone, with accuracy, sensitivity, and specificity.
Journal ArticleDOI
Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals
Turker Tuncer,Sengul Dogan,Ru San Tan,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +5 more
TL;DR: High classification accuracy suggests that the proposed PGP and TEP based model can be used for heart sound classification using PCG signals, and a novel multilevel feature generation network was developed.
Journal ArticleDOI
Novel three kernelled binary pattern feature extractor based automated PCG sound classification method
Mehmet Ali Kobat,Sengul Dogan +1 more
TL;DR: This research presents a stable feature generator-based automated heart diseases diagnosis model that is basic and high accurate, ready for the development of real-time implementations.
References
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