Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4469-4472. doi: 10.1109/EMBC48229.2022.9871640.
Heart sound classification is one of the non-invasive methods for early detection of the cardiovascular diseases (CVDs), the leading cause for deaths. In recent years, Computer Audition (CA) technology has become increasingly sophisticated, auxiliary diagnosis technology of heart disease based on CA has become a popular research area. This paper proposes a deep Convolutional Neural Network (CNN) model for heart sound classification. To improve the classification accuracy of heart sound, we design a classification algorithm combining classical Residual Network (ResNet) and Long Short-Term Memory (LSTM). The model performance is evaluated in the PhysioNet/CinC Challenges 2016 datasets using a 2D time-frequency feature. We extract the four features from different filter-bank coefficients, including Filterbank (Fbank), Mel-Frequency Spectral Coefficients (MFSCs), and Mel-Frequency Cepstral Coefficients (MFCCs). The experimental results show the MFSCs feature outperforms the other features in the proposed CNN model. The proposed model performs well on the test set, particularly the F1 score of 84.3 % - the accuracy of 84.4 %, the sensitivity of 84.3 %, and the specificity of 85.6 %. Compared with the classical ResNet model, an accuracy of 4.9 % improvement is observed in the proposed model.
心音分类是心血管疾病（CVD）早期检测的无创方法之一，CVD 也是导致死亡的主要原因。近年来，计算机听觉（CA）技术变得越来越复杂，基于 CA 的心脏病辅助诊断技术已成为热门研究领域。本文提出了一种用于心音分类的深度卷积神经网络（CNN）模型。为了提高心音分类的准确性，我们设计了一种结合经典残差网络（ResNet）和长短时记忆（LSTM）的分类算法。该模型在 PhysioNet/CinC Challenges 2016 数据集上使用 2D 时频特征进行了性能评估。我们从不同滤波器组系数中提取了四个特征，包括滤波器组（Fbank）、梅尔频率频谱系数（MFSCs）和梅尔频率倒谱系数（MFCCs）。实验结果表明，MFSCs 特征在提出的 CNN 模型中优于其他特征。所提出的模型在测试集上表现良好，特别是 F1 得分为 84.3%——准确率为 84.4%，灵敏度为 84.3%，特异性为 85.6%。与经典的 ResNet 模型相比，所提出的模型的准确率提高了 4.9%。
