School of Electrical and Electronic Information, Xihua University, Chengdu, 610039, Sichuan, China.
Sci Rep. 2024 Nov 25;14(1):29132. doi: 10.1038/s41598-024-70230-y.
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.
心音信号分析在预防和诊断心脏疾病方面起着至关重要的作用。本研究提出了一种基于频率平衡功率谱强度的多级特征编码算法，用于心音信号分类。首先，采用小波阈值函数对心音信号进行去噪。然后，计算频率平衡功率谱强度包络，并利用包络提取多级特征。最后，选择集成袋式树分类器进行分类。实验数据包括来自 2016 年 PhysioNet/CinC 挑战赛的二分类数据和来自自收集肥厚型心肌病数据集的三分类数据。结果表明，所提出的算法表现良好，对正常和异常心音的平均分类准确率为 98.73%，对正常和两种肥厚型心肌病心音的平均分类准确率为 98.12%。该方法对心脏病的早期诊断具有重要的参考价值。
