使用LSTM神经网络的放射性动脉脉冲波的心力衰竭分类模型
Yi Lyu1,2, Wen-Yue Huang3, Hai-Mei Wu4
1School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China.
BMC medical informatics and decision making
|August 27, 2025
概括
深度学习模型可以使用非侵入性的辐射动脉脉冲波检测心力衰竭 (HF). 一个LSTM模型显示出高精度,为早期HF查提供了具有成本效益的工具.
科学领域:
- 生物医学工程
- 医疗保健中的人工智能
- 心血管诊断
背景情况:
- 心力衰竭是全球主要的健康问题,需要可及的早期检测方法.
- 深度学习 (DL) 为HF检测提供了有前途的非侵入性,快速和成本效益的解决方案.
研究的目的:
- 通过使用放射性动脉脉冲波数据来评估深度学习算法的有效性.
- 确定最有效的DL模型来区分健康个体,冠状动脉疾病 (CAD) 患者和HF患者.
主要方法:
- 使用了462名参与者的放射性动脉脉冲波数据 (健康,CAD,HF).
- 在数据预处理和平衡后,应用和比较了四种DL模型:LSTM,CNN,GRU和Bi-LSTM.
- 使用十倍交叉验证进行可靠的性能和稳定性评估.
主要成果:
- 长期短期记忆 (LSTM) 模型实现了最高的平均精度 (0.8595 ± 0.0522).
- 在关键指标上,LSTM表现出卓越的表现,包括曲线下的面积.
- SHAP框架用于模型解释性和特征重要性分析.
结论:
- 一个分析放射性动脉脉冲波的LSTM模型有效地区分健康,CAD和HF状态.
- 这种非侵入性,经济高效的方法显示了早期心力衰竭查的潜力.
- 建议进行进一步的临床验证,以确认该工具的实际应用.
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