循环与非循环卷积神经网络用于心脏声音分类
Arash Gharehbaghi1, Elaheh Partovi2, Ankica Babic1,3
1Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Studies in health technology and informatics
|June 30, 2023
概括
传统的卷积神经网络 (CNN) 有效地分类异常-正常的心声. 虽然复杂的循环神经网络集成显示了高准确度,但更简单的CNN提供了可比性能,心脏声音分析的复杂性更小.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 卷积神经网络 (CNN) 在心声分析中很普遍.
- 重复神经网络 (RNN),如门式重复网络 (GRN) 和长期短期记忆 (LSTM),通常与复杂任务的CNN相结合.
- 对复杂的混合架构进行简单模型的评估对于高效的诊断工具至关重要.
研究的目的:
- 为了比较传统CNN与各种CNN-RNN混合架构的性能,以进行异常-正常的心声分类.
- 评估不同并行和级联集成方法的准确性和灵敏性.
- 为了确定最佳的模型复杂性,可靠的心脏声音信号分类.
主要方法:
- 利用Physionet数据集进行心脏声音记录.
- 实施和评估传统的CNN架构.
- 开发并测试了CNN与GRN和LSTM的并行和级联集成架构.
- 对每个模型配置的测量精度和灵敏度.
主要成果:
- 平行LSTM-CNN架构实现了最高的精度 (98.0%),灵敏度为87.2%.
- 传统的CNN显示出强的性能,准确率为97.3%,灵敏度为95.9%.
- 与混合模型相比,传统的CNN需要的计算复杂性要少得多.
结论:
- 传统的CNN适用于异常-正常的心声分类.
- 简单的CNN模型提供了高性能和降低复杂性的平衡.
- 这些发现表明,传统的CNN可以独立地用于有效的心脏声音分析.
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