BPINet:基于心电图和PPG信号的同步血压估计和用户身份验证,具有多任务学习
Xianliang Jiang1, Dingxin Yu2, Guang Jin2
1College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China; Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China.
Artificial intelligence in medicine
|September 30, 2025
概括
BPINet使用ECG/PPG信号来准确估计血压和识别用户. 这种新的方法克服了袖子式监视器的局限性,使个人,特别是老年人能够进行个性化健康监测.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 精确的血压监测对于管理慢性疾病至关重要,特别是在单独生活的老年人中.
- 基于手的家用血压监测器经常因不当使用而出现不准确性,并且缺乏用户识别功能,妨碍了个性化的护理.
研究的目的:
- 开发一种使用心电图 (ECG) 和光电图 (PPG) 信号同时估计血压和用户身份验证的新型非侵入性系统.
- 通过实现个性化健康跟踪来解决现有的家庭血压监测设备的局限性.
主要方法:
- 开发了一种多任务学习模型,BPINet,在多门专家混合 (MMoE) 框架内采用卷积神经网络-长期短期记忆 (CNN-BiLSTM) 架构.
- 用ECG和PPG信号进行特征提取,同时进行血压估计和用户身份识别.
- 创建了ECG/PPG信号和血压测量的定制数据集,并与昆士兰大学生命体征数据集 (UQVS) 结合进行评估.
主要成果:
- 在用户身份识别方面,BPINet实现了高精度,在UQVS数据集上达到97.54%,在定制数据集上达到94.30%.
- 系统性血压 (SBP) 估计的平均绝对误差 (MAE) 为3.317 ± 5.771 mmHg (UQVS) 和2.940 ± 4.753 mmHg (定制数据集).
- 透气血压 (DBP) 估计结果为MAE为2.444 ± 4.147 mmHg (UQVS),符合严格的英国高血压学会 (BHS) A级和医学仪器仪表协会 (AAMI) 标准.
结论:
- BPINet有效地估计血压,同时认证用户,比传统的袖子式方法有了显著的进步.
- 该系统识别个人用户的能力有助于创建个性化的长期健康监测报告.
- 已证明的临床可行性和有效性支持采用BPINet以改善远程患者监测和高血压管理.
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