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  • Title: [Research on bark-frequency spectral coefficients heart sound classification algorithm based on multiple window time-frequency reassignment].
    Author: Xia J, Sun J, Yang H, Pan J, Guo T, Wang W.
    Journal: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2024 Feb 25; 41(1):51-59. PubMed ID: 38403604.
    Abstract:
    The multi-window time-frequency reassignment helps to improve the time-frequency resolution of bark-frequency spectral coefficient (BFSC) analysis of heart sounds. For this purpose, a new heart sound classification algorithm combining feature extraction based on multi-window time-frequency reassignment BFSC with deep learning was proposed in this paper. Firstly, the randomly intercepted heart sound segments are preprocessed with amplitude normalization, the heart sounds were framed and time-frequency rearrangement based on short-time Fourier transforms were computed using multiple orthogonal windows. A smooth spectrum estimate is calculated by arithmetic averaging each of the obtained independent spectra. Finally, the BFSC of reassignment spectrum is extracted as a feature by the Bark filter bank. In this paper, convolutional network and recurrent neural network are used as classifiers for model comparison and performance evaluation of the extracted features. Eventually, the multi-window time-frequency rearrangement improved BFSC method extracts more discriminative features, with a binary classification accuracy of 0.936, a sensitivity of 0.946, and a specificity of 0.922. These results present that the algorithm proposed in this paper does not need to segment the heart sounds and randomly intercepts the heart sound segments, which greatly simplifies the computational process and is expected to be used for screening of congenital heart disease. 多窗口时频重排有助于提升对心音进行巴克频谱系数（BFSC）分析的时频分辨率。为此，本文提出一种基于多窗口时频重排的BFSC特征提取与深度学习结合的心音分类新算法。首先，对随机截取的心音片段进行幅值归一化等预处理，然后分别用多个正交窗口对心音做分帧处理，及计算基于短时傅里叶变换的时频重排，将得到的各独立频谱通过算术平均计算出平稳的频谱估计。最后，通过巴克滤波器组提取该重排频谱的BFSC作为特征。本文采用卷积网络与循环神经网络作为分类器，对提取的特征进行模型比较与性能评估。最终，多窗口时频重排改进BFSC的方法提取了更具有辨别力的特征，二分类准确率达到0.936，灵敏度为0.946，特异度为0.922。研究结果表明，本文所提算法无需分割心音，随机截取心音片段，大大简化了计算流程，有望用于先天性心脏病筛查。.
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