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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-70230-y