通过使用非接触性生命信号的特征融合模型来诊断心力衰竭和排放分数
Shen Feng1, Xianda Wu1, Huan Cen2
1School of Electronic Science and Engineering (School of Microelectronics), South China Normal University, Foshan, 528225, China.
Computer methods and programs in biomedicine
|September 4, 2025
概括
这项研究引入了一种新的混合深度学习框架,用于准确的家庭心力衰竭 (HF) 监测和左心室喷射率 (LVEF) 分类. 该方法显著提高了HF和LVEF状态的诊断准确性.
科学领域:
- 生物医学工程
- 心脏病学
- 医疗保健中的人工智能
背景情况:
- 胆心图 (BCG) 显示可能对心力衰竭进行远程监测.
- 目前对BCG的手动特征分析不足以描述左心室射出小部分 (LVEF) 的动态,特别是LVEF< 40%.
- 需要使用先进的方法来改善使用BCG的HF诊断和LVEF分类.
研究的目的:
- 开发和验证混合功能融合框架,整合BCG和呼吸信号的手动和深度学习功能.
- 提高心力衰竭诊断的准确性和左心室喷射分数的分类.
- 建立家庭HF和LVEF评估的非临床框架.
主要方法:
- 招募了83名参与者,将他们分为健康,HF (LVEF ≥ 40%) 和HF (LVEF < 40%) 组.
- 使用压电传感器收集非接触性生命体征, 隔离BCG和呼吸信号.
- 开发了一种混合模型,将手动功能与多尺度ResNet-BiLSTM网络的深度功能相结合,用于信号分析.
主要成果:
- 与传统手工方法相比,拟议的混合方法取得了更高的性能.
- 获得高分类准确度:两类 (健康与HF) 的分类为98. 20%,三类 (健康,HF LVEF ≥ 40%,HF LVEF < 40%) 的分类为98. 76%.
- 使用五倍交叉验证验证结果.
结论:
- 开发的以医疗为导向的框架可以有效地在家诊断HF和LVEF分类.
- 在非临床环境中促进快速初步查和辅助诊断.
- 展示了混合深度学习方法在远程心血管健康监测中的潜力.
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