Cargando…

Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method

BACKGROUND: The diagnosis of cardiac abnormalities based on heart sound signal is a research hotspot in recent years. The early diagnosis of cardiac abnormalities has a crucial significance for the treatment of heart diseases. METHODS: For the sake of achieving more practical clinical applications o...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Wanrong, Xu, Jiajie, Xiang, Junhong, Yan, Zhonghong, Zhou, Hengyu, Wen, Binbin, Kong, Hai, Zhu, Rui, Li, Wang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9439280/
https://www.ncbi.nlm.nih.gov/pubmed/36056352
http://dx.doi.org/10.1186/s12911-022-01976-6
_version_ 1784782020725964800
author Yang, Wanrong
Xu, Jiajie
Xiang, Junhong
Yan, Zhonghong
Zhou, Hengyu
Wen, Binbin
Kong, Hai
Zhu, Rui
Li, Wang
author_facet Yang, Wanrong
Xu, Jiajie
Xiang, Junhong
Yan, Zhonghong
Zhou, Hengyu
Wen, Binbin
Kong, Hai
Zhu, Rui
Li, Wang
author_sort Yang, Wanrong
collection PubMed
description BACKGROUND: The diagnosis of cardiac abnormalities based on heart sound signal is a research hotspot in recent years. The early diagnosis of cardiac abnormalities has a crucial significance for the treatment of heart diseases. METHODS: For the sake of achieving more practical clinical applications of automatic recognition of cardiac abnormalities, here we proposed a novel fuzzy matching feature extraction method. First of all, a group of Gaussian wavelets are selected and then optimized based on a template signal. Convolutional features of test signal and the template signal are then computed. Matching degree and matching energy features between template signal and test signal in time domain and frequency domain are then extracted. To test performance of proposed feature extraction method, machine learning algorithms such as K-nearest neighbor, support vector machine, random forest and multilayer perceptron with grid search parameter optimization are constructed to recognize heart disease using the extracted features based on phonocardiogram signals. RESULTS: As a result, we found that the best classification accuracy of random forest reaches 96.5% under tenfold cross validation using the features extracted by the proposed method. Further, Mel-Frequency Cepstral Coefficients of phonocardiogram signals combing with features extracted by our algorithm are evaluated. Accuracy, sensitivity and specificity of integrated features reaches 99.0%, 99.4% and 99.7% respectively when using support vector machine, which achieves the best performance among all reported algorithms based on the same dataset. On several common features, we used independent sample t-tests. The results revealed that there are significant differences (p < 0.05) between 5 categories. CONCLUSION: It can be concluded that our proposed fuzzy matching feature extraction method is a practical approach to extract powerful and interpretable features from one-dimensional signals for heart sound diagnostics and other pattern recognition task. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-022-01976-6.
format Online
Article
Text
id pubmed-9439280
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-94392802022-09-04 Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method Yang, Wanrong Xu, Jiajie Xiang, Junhong Yan, Zhonghong Zhou, Hengyu Wen, Binbin Kong, Hai Zhu, Rui Li, Wang BMC Med Inform Decis Mak Research BACKGROUND: The diagnosis of cardiac abnormalities based on heart sound signal is a research hotspot in recent years. The early diagnosis of cardiac abnormalities has a crucial significance for the treatment of heart diseases. METHODS: For the sake of achieving more practical clinical applications of automatic recognition of cardiac abnormalities, here we proposed a novel fuzzy matching feature extraction method. First of all, a group of Gaussian wavelets are selected and then optimized based on a template signal. Convolutional features of test signal and the template signal are then computed. Matching degree and matching energy features between template signal and test signal in time domain and frequency domain are then extracted. To test performance of proposed feature extraction method, machine learning algorithms such as K-nearest neighbor, support vector machine, random forest and multilayer perceptron with grid search parameter optimization are constructed to recognize heart disease using the extracted features based on phonocardiogram signals. RESULTS: As a result, we found that the best classification accuracy of random forest reaches 96.5% under tenfold cross validation using the features extracted by the proposed method. Further, Mel-Frequency Cepstral Coefficients of phonocardiogram signals combing with features extracted by our algorithm are evaluated. Accuracy, sensitivity and specificity of integrated features reaches 99.0%, 99.4% and 99.7% respectively when using support vector machine, which achieves the best performance among all reported algorithms based on the same dataset. On several common features, we used independent sample t-tests. The results revealed that there are significant differences (p < 0.05) between 5 categories. CONCLUSION: It can be concluded that our proposed fuzzy matching feature extraction method is a practical approach to extract powerful and interpretable features from one-dimensional signals for heart sound diagnostics and other pattern recognition task. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-022-01976-6. BioMed Central 2022-09-02 /pmc/articles/PMC9439280/ /pubmed/36056352 http://dx.doi.org/10.1186/s12911-022-01976-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Yang, Wanrong
Xu, Jiajie
Xiang, Junhong
Yan, Zhonghong
Zhou, Hengyu
Wen, Binbin
Kong, Hai
Zhu, Rui
Li, Wang
Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method
title Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method
title_full Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method
title_fullStr Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method
title_full_unstemmed Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method
title_short Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method
title_sort diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9439280/
https://www.ncbi.nlm.nih.gov/pubmed/36056352
http://dx.doi.org/10.1186/s12911-022-01976-6
work_keys_str_mv AT yangwanrong diagnosisofcardiacabnormalitiesbasedonphonocardiogramusinganovelfuzzymatchingfeatureextractionmethod
AT xujiajie diagnosisofcardiacabnormalitiesbasedonphonocardiogramusinganovelfuzzymatchingfeatureextractionmethod
AT xiangjunhong diagnosisofcardiacabnormalitiesbasedonphonocardiogramusinganovelfuzzymatchingfeatureextractionmethod
AT yanzhonghong diagnosisofcardiacabnormalitiesbasedonphonocardiogramusinganovelfuzzymatchingfeatureextractionmethod
AT zhouhengyu diagnosisofcardiacabnormalitiesbasedonphonocardiogramusinganovelfuzzymatchingfeatureextractionmethod
AT wenbinbin diagnosisofcardiacabnormalitiesbasedonphonocardiogramusinganovelfuzzymatchingfeatureextractionmethod
AT konghai diagnosisofcardiacabnormalitiesbasedonphonocardiogramusinganovelfuzzymatchingfeatureextractionmethod
AT zhurui diagnosisofcardiacabnormalitiesbasedonphonocardiogramusinganovelfuzzymatchingfeatureextractionmethod
AT liwang diagnosisofcardiacabnormalitiesbasedonphonocardiogramusinganovelfuzzymatchingfeatureextractionmethod