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Multi-level feature encoding algorithm based on FBPSI for heart sound classification
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DOI：10.1038/s41598-024-70230-y.png)
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Abstract
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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.
Keywords:
Heart sound classification
Balanced power spectrum intensity
Multi-level feature encoding
Hypertrophic cardiomyopathy
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2020 AHA/ACC Guideline for the Diagnosis and Treatment of Patients With Hypertrophic Cardiomyopathy: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines (vol 142, e558, 2020).png)
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