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

Abstract

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.

Keywords

• Heart sound classification
• Balanced power spectrum intensity
• Multi-level feature encoding
• Hypertrophic cardiomyopathy