Multi-level feature encoding algorithm based on FBPSI for heart sound classification

Analysis of heart sound signals plays an essential role in preventing and diagnosing cardiac diseases. This study proposes a multi-level feature encoding algorithm based on frequency-balanced power spectral intensity for heart sound signal classification. Firstly, a wavelet threshold function is emp...

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Published in:	Scientific reports Vol. 14; no. 1; pp. 29132 - 12
Main Authors:	Fang, Yu, Leng, Hongxia, Wang, Weibo, Liu, Dongbo
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 25.11.2024 Nature Publishing Group Nature Portfolio
Subjects:	631/1647 639/166 692/700 Accuracy Algorithms Balanced power spectrum intensity Cardiomyopathy Cardiomyopathy, Hypertrophic - diagnosis Cardiomyopathy, Hypertrophic - physiopathology Cardiovascular disease Cardiovascular diseases Classification Coronary artery disease Datasets Electroencephalography Heart Heart diseases Heart sound classification Heart Sounds - physiology Humanities and Social Sciences Humans Hypertrophic cardiomyopathy Image coding Multi-level feature encoding multidisciplinary Neural networks Science Science (multidisciplinary) Signal Processing, Computer-Assisted Sound Heart sound classification Hypertrophic cardiomyopathy Balanced power spectrum intensity Multi-level feature encoding
ISSN:	2045-2322, 2045-2322
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Abstract	Analysis of heart sound signals plays an essential role in preventing and diagnosing cardiac diseases. This study proposes a multi-level feature encoding algorithm based on frequency-balanced power spectral intensity for heart sound signal classification. Firstly, a wavelet threshold function is employed to denoise the heart sound signals. Then, the frequency-balanced power spectral intensity envelope is calculated, and an encoder is utilized to extract multi-level features based on the envelope. Finally, an ensemble bagging tree classifier is selected for classification. The experimental data includes binary classification data from the 2016 PhysioNet/CinC Challenge and ternary classification data from the self-collected hypertrophic cardiomyopathy dataset. Results demonstrate that the proposed algorithm performs well, achieving an average classification accuracy of 98.73% for normal and abnormal heart sounds, and 98.12% for normal and two types of hypertrophic cardiomyopathy heart sounds. The proposed method holds significant reference value for the early diagnosis of heart diseases.
AbstractList	Analysis of heart sound signals plays an essential role in preventing and diagnosing cardiac diseases. This study proposes a multi-level feature encoding algorithm based on frequency-balanced power spectral intensity for heart sound signal classification. Firstly, a wavelet threshold function is employed to denoise the heart sound signals. Then, the frequency-balanced power spectral intensity envelope is calculated, and an encoder is utilized to extract multi-level features based on the envelope. Finally, an ensemble bagging tree classifier is selected for classification. The experimental data includes binary classification data from the 2016 PhysioNet/CinC Challenge and ternary classification data from the self-collected hypertrophic cardiomyopathy dataset. Results demonstrate that the proposed algorithm performs well, achieving an average classification accuracy of 98.73% for normal and abnormal heart sounds, and 98.12% for normal and two types of hypertrophic cardiomyopathy heart sounds. The proposed method holds significant reference value for the early diagnosis of heart diseases. Analysis of heart sound signals plays an essential role in preventing and diagnosing cardiac diseases. This study proposes a multi-level feature encoding algorithm based on frequency-balanced power spectral intensity for heart sound signal classification. Firstly, a wavelet threshold function is employed to denoise the heart sound signals. Then, the frequency-balanced power spectral intensity envelope is calculated, and an encoder is utilized to extract multi-level features based on the envelope. Finally, an ensemble bagging tree classifier is selected for classification. The experimental data includes binary classification data from the 2016 PhysioNet/CinC Challenge and ternary classification data from the self-collected hypertrophic cardiomyopathy dataset. Results demonstrate that the proposed algorithm performs well, achieving an average classification accuracy of 98.73% for normal and abnormal heart sounds, and 98.12% for normal and two types of hypertrophic cardiomyopathy heart sounds. The proposed method holds significant reference value for the early diagnosis of heart diseases.Analysis of heart sound signals plays an essential role in preventing and diagnosing cardiac diseases. This study proposes a multi-level feature encoding algorithm based on frequency-balanced power spectral intensity for heart sound signal classification. Firstly, a wavelet threshold function is employed to denoise the heart sound signals. Then, the frequency-balanced power spectral intensity envelope is calculated, and an encoder is utilized to extract multi-level features based on the envelope. Finally, an ensemble bagging tree classifier is selected for classification. The experimental data includes binary classification data from the 2016 PhysioNet/CinC Challenge and ternary classification data from the self-collected hypertrophic cardiomyopathy dataset. Results demonstrate that the proposed algorithm performs well, achieving an average classification accuracy of 98.73% for normal and abnormal heart sounds, and 98.12% for normal and two types of hypertrophic cardiomyopathy heart sounds. The proposed method holds significant reference value for the early diagnosis of heart diseases. Abstract Analysis of heart sound signals plays an essential role in preventing and diagnosing cardiac diseases. This study proposes a multi-level feature encoding algorithm based on frequency-balanced power spectral intensity for heart sound signal classification. Firstly, a wavelet threshold function is employed to denoise the heart sound signals. Then, the frequency-balanced power spectral intensity envelope is calculated, and an encoder is utilized to extract multi-level features based on the envelope. Finally, an ensemble bagging tree classifier is selected for classification. The experimental data includes binary classification data from the 2016 PhysioNet/CinC Challenge and ternary classification data from the self-collected hypertrophic cardiomyopathy dataset. Results demonstrate that the proposed algorithm performs well, achieving an average classification accuracy of 98.73% for normal and abnormal heart sounds, and 98.12% for normal and two types of hypertrophic cardiomyopathy heart sounds. The proposed method holds significant reference value for the early diagnosis of heart diseases.
ArticleNumber	29132
Author	Liu, Dongbo Leng, Hongxia Wang, Weibo Fang, Yu
Author_xml	– sequence: 1 givenname: Yu surname: Fang fullname: Fang, Yu organization: School of Electrical and Electronic Information, Xihua University – sequence: 2 givenname: Hongxia surname: Leng fullname: Leng, Hongxia organization: School of Electrical and Electronic Information, Xihua University – sequence: 3 givenname: Weibo surname: Wang fullname: Wang, Weibo organization: School of Electrical and Electronic Information, Xihua University – sequence: 4 givenname: Dongbo surname: Liu fullname: Liu, Dongbo email: liudb@mail.xhu.edu.cn organization: School of Electrical and Electronic Information, Xihua University
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Keywords	Heart sound classification Hypertrophic cardiomyopathy Balanced power spectrum intensity Multi-level feature encoding
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Snippet	Analysis of heart sound signals plays an essential role in preventing and diagnosing cardiac diseases. This study proposes a multi-level feature encoding... Abstract Analysis of heart sound signals plays an essential role in preventing and diagnosing cardiac diseases. This study proposes a multi-level feature...
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SubjectTerms	631/1647 639/166 692/700 Accuracy Algorithms Balanced power spectrum intensity Cardiomyopathy Cardiomyopathy, Hypertrophic - diagnosis Cardiomyopathy, Hypertrophic - physiopathology Cardiovascular disease Cardiovascular diseases Classification Coronary artery disease Datasets Electroencephalography Heart Heart diseases Heart sound classification Heart Sounds - physiology Humanities and Social Sciences Humans Hypertrophic cardiomyopathy Image coding Multi-level feature encoding multidisciplinary Neural networks Science Science (multidisciplinary) Signal Processing, Computer-Assisted Sound
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Title	Multi-level feature encoding algorithm based on FBPSI for heart sound classification
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