Exploring interpretable representations for heart sound abnormality detection
Exploring interpretable representations for heart sound abnormality detection
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DOI：
10.1016/j.bspc.2023.104569
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发表时间：
2023-01-18
影响因子：
5.1
通讯作者：
Yamamoto，Yoshiharu
中科院分区：
文献类型：
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作者：
Wang，Zhihua;Qian，Kun;Yamamoto，Yoshiharu
基于计算机听觉的心音异常检测方法具有无创、实时、方便等优点，越来越受到心血管疾病界的重视。时频分析对于基于计算机听觉的应用是至关重要的。然而，到目前为止，还缺乏一个全面的调查，发现一个优化的方法，从心音提取时频表示。为此，我们提出了一个全面的调查时频方法分析心音，即，短时傅立叶变换、Log-Mel变换、Hilbert-Huang变换、小波变换、Mel变换和Stockwell变换。时间-频率表示通过预先训练的深度卷积神经网络自动学习。考虑到智能听诊器在真实的环境中对高鲁棒性检测算法的迫切需求，在广泛评估中使用的训练、验证和测试集是与主题无关的。最后，为了进一步理解心血管疾病的心音数字表型，使用可解释的人工智能方法来揭示四种时频表示在心音异常检测中的性能差异的原因。实验结果表明，斯托克韦尔变换可以击败其他方法，达到最高的总得分为65.2%。实验结果表明，Stockwell变换不仅能提供更多的心音信息，而且具有一定的噪声鲁棒性。此外，所考虑的微调深度模型在与受试者无关的测试中使平均准确度比以前的最先进结果提高了9.0%。
The advantages of non-invasive, real-time and convenient, computer audition-based heart sound abnormality detection methods have increasingly attracted efforts among the community of cardiovascular diseases. Time–frequency analyses are crucial for computer audition-based applications. However, a comprehensive investigation on discovering an optimised way for extracting time–frequency representations from heart sounds is lacking until now. To this end, we propose a comprehensive investigation on time–frequency methods for analysing the heart sound, i.e., short-time Fourier transformation, Log-Mel transformation, Hilbert–Huang transformation, wavelet transformation, Mel transformation, and Stockwell transformation. The time–frequency representations are automatically learnt via pre-trained deep convolutional neural networks. Considering the urgent need of smart stethoscopes for high robust detection algorithms in real environment, the training, verification, and testing sets employed in the extensive evaluation are subject-independent. Finally, to further understand the heart sound-based digital phenotype for cardiovascular diseases, explainable artificial intelligence approaches are used to reveal the reasons for the performance differences of four time–frequency representations in heart sound abnormality detection. Experimental results show that Stockwell transformation can beat other methods by reaching the highest overall score of 65.2%. The interpretable results demonstrate that Stockwell transformation does not only present more information for heart sounds, but also provides a certain noise robustness. Besides, the considered fine-tuned deep model brings an improvement of the mean accuracy over the previous state-of-the-art results by 9.0% in subject-independent testing.