论文标题

心脏声音信号的时频分布:使用卷积神经网络的比较研究

Time-Frequency Distributions of Heart Sound Signals: A Comparative Study using Convolutional Neural Networks

论文作者

Bao, Xinqi, Xu, Yujia, Lam, Hak-Keung, Trabelsi, Mohamed, Chihi, Ines, Sidhom, Lilia, Kamavuako, Ernest N.

论文摘要

时间频分布(TFD)支持早期心脏筛查中的心脏声音表征和分类。然而,尽管经常在信号分析中使用TFD,但尚无研究对其在自动诊断的深度学习方面进行全面比较。此外,信号处理方法作为卷积神经网络(CNN)的输入的组合已被证明是提高信号分类性能的实际方法。因此,本研究旨在研究TFD/组合TFD作为CNN的最佳使用。提出的结果表明:1)与使用原始信号相比,心脏声信号转换为TF域的分类性能更高。在TFD中,所有CNN型号的性能差异很小(平均准确性$ 1.3 \%$以内)。但是,连续小波变换(CWT)和Chirpet Transform(CT)的表现优于其余部分。 2)CNN容量和体系结构优化的适当增加可以提高性能,而网络体系结构不应过于复杂。基于重新NET或SERESNET家族的结果,参数数量的增加和结构的深度显然不会改善性能。 3)将TFD与CNN输入相结合并不能显着改善分类结果。这项研究的发现提供了将TFD作为CNN输入和设计CNN体系结构的知识。

Time-Frequency Distributions (TFDs) support the heart sound characterisation and classification in early cardiac screening. However, despite the frequent use of TFDs in signal analysis, no study comprehensively compared their performances on deep learning for automatic diagnosis. Furthermore, the combination of signal processing methods as inputs for Convolutional Neural Networks (CNNs) has been proved as a practical approach to increasing signal classification performance. Therefore, this study aimed to investigate the optimal use of TFD/ combined TFDs as input for CNNs. The presented results revealed that: 1) The transformation of the heart sound signal into the TF domain achieves higher classification performance than using of raw signals. Among the TFDs, the difference in the performance was slight for all the CNN models (within $1.3\%$ in average accuracy). However, Continuous wavelet transform (CWT) and Chirplet transform (CT) outperformed the rest. 2) The appropriate increase of the CNN capacity and architecture optimisation can improve the performance, while the network architecture should not be overly complicated. Based on the ResNet or SEResNet family results, the increase in the number of parameters and the depth of the structure do not improve the performance apparently. 3) Combining TFDs as CNN inputs did not significantly improve the classification results. The findings of this study provided the knowledge for selecting TFDs as CNN input and designing CNN architecture for heart sound classification.

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