关于多模式传感器融合用于使用BCG和PPG信号估计心率的研究
Jisheng Xing1, Xin Fang1, Jing Bai1
1College of Electrical and Information Engineering, Beihua University, Jilin 132021, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种新的多式联络网络,用于使用弹性心率 (BCG) 和光电脉冲图 (PPG) 信号的连续心率监测. MM-TFNet准确地预测心率,为家庭心血管疾病检测提供了对心电图的舒适替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 心血管健康 心血管健康
背景情况:
- 持续监测心率对于早期发现心血管疾病至关重要.
- 电心电图 (ECG) 对舒适的长期家庭监测存在局限性.
- 非侵入性生理信号提供了远程健康评估的潜力.
研究的目的:
- 开发一个多式时融合网络 (MM-TFNet),用于准确的心率监测.
- 整合弹性心电图 (BCG) 和光电图 (PPG) 信号,以加强心血管监测.
- 为了克服与家庭环境中的传统ECG监测相关的不适和局限性.
主要方法:
- 利用时间卷积网络 (TCN) 和双向长期短期记忆网络 (BiLSTM) 来从BCG和PPG信号中提取时间特征.
- 实施了一种跨模式的注意力机制,用于BCG和PPG特征的自适应融合.
- 采用多头自我注意 (MHSA) 专注于关键的心跳波形,提高动态活动期间的强度.
主要成果:
- 在心率预测中,MM-TFNet实现了0.88 BPM的平均绝对误差 (MAE).
- 拟议的模型在心率预测任务中表现优于现有的主流深度学习方法.
- 实验验证对40名受试者的公开BCG-PPG-ECG数据集进行了实验验证.
结论:
- MM-TFNet在使用非侵入性BCG和PPG信号的持续心率监测方面表现出高准确性和稳定性.
- 这种多式联络融合方法为无接触,低功耗,边缘部署的家庭健康监测系统提供了基础.
- 该研究强调了多模式融合在分析复杂的生理信号以改善心血管健康管理方面的巨大潜力.
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