从弹性心电图中重建心电图,使用带有注意力的生成对抗网络
Hong Chen1, Jing Zhan1, Ruilin Feng1
1The School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, People's Republic of China.
Biomedical physics & engineering express
|March 3, 2025
概括
本研究使用人工智能模型从非接触式弹性心电图 (BCG) 信号重建了心电图 (ECG) 信号. 该方法使持续的心脏监测和心率变异性分析能够用于长期的家庭使用.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 电心电图 (ECG) 对心血管疾病的检测至关重要.
- 目前的心电图监测方法 (12-,智能手表) 不足以进行日常的长期家庭监测.
- 无接触式心脏弹道图 (BCG) 为持续监测提供了一个潜在的替代方案.
研究的目的:
- 从非接触BCG信号中重建ECG信号的系统.
- 为了实现持续的心脏周期监测和心率变化 (HRV) 分析.
- 为长期的家庭心电图监测提供一种新的解决方案.
主要方法:
- 使用光纤传感器和心电图机同步收集BCG和ECG信号.
- 预处理信号以创建训练数据集.
- 训练一个Att-SNGAN模型,从BCG输入中重建ECG.
主要成果:
- 重建的心电图信号与原来的心电图信号具有很高的一致性.
- 实现了0.0651.1的平均绝对误差 (MAE).
- 取得了0.0735的根平均平方误差 (RMSE) 和0.0342.0的Fréchet距离 (FD).
结论:
- 拟议的系统有效地从BCG数据中重建ECG信号.
- 这项技术具有持续的心脏监测和HRV分析的巨大潜力.
- 提供了一种有前途的新方法,用于方便的长期家庭ECG监测.
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