基于卷积神经网络的心脏声音分类,并具有卷积阻断注意模块
Ximing Huai1, Lei Jiang2,3, Chao Wang1
1Ningbo Key Laboratory of Intelligent Manufacturing of Textiles and Garments, Zhejiang Fashion Institute of Technology, Ningbo, Zhejiang, China.
Frontiers in physiology
|June 20, 2025
概括
这项研究增强了用于诊断心血管疾病 (CVD) 的心声分类,使用了基于注意力的新型卷积神经网络 (CNN). 改进的模型实现了高精度,显示出临床应用的前景.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 心血管疾病 (CVD) 是全球死亡的主要原因之一.
- 对心血管疾病而言,准确有效的诊断工具至关重要.
- 目前的诊断方法可能缺乏早期检测所需的精度.
研究的目的:
- 使用深度学习开发一个增强的心声分类框架.
- 将卷积块注意模块 (CBAM) 与卷积神经网络 (CNN) 集成.
- 为了评估基于注意力的CNN用于分类心脏声音的性能.
主要方法:
- 利用了PhysioNet CinC 2016数据集中的心脏声音记录.
- 将处理的音频数据转化为光谱图以进行分析.
- 系统地评估了12个具有不同CBAM配置的CNN模型.
主要成果:
- 最优的CNN模型与CBAM集成实现了98.66%的准确性,主要的数据集.
- 在一个独立的PhysioNet 2022数据集上进行验证,准确率为95.6%,AUC为96.29%.
- T-SNE可视化显示了明确的类分离,表明有效的特征提取.
结论:
- 选择性整合CBAM显著改善了CNN在心脏声音分类中的表现.
- 基于注意力的架构对于医疗信号分类是有效的.
- 开发的框架显示了在诊断心血管疾病方面实际临床应用的潜力.
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