基于卷积神经网络的无校准血压估计
Jinwoo Cho1, Hangsik Shin2, Ahyoung Choi3
1Bud-on Co., Ltd., Seoul, Republic of Korea.
Psychophysiology
|November 16, 2023
概括
这项研究开发了一个深度学习模型,用于使用心电图和PPG信号进行可穿戴血压 (BP) 估计. 电动心电图信号对噪声更具稳定性,使得在资源有限的环境中能够准确地监测BP.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 可穿戴技术可穿戴技术
背景情况:
- 可穿戴设备需要高效的血压 (BP) 估计模型,具有有限的计算能力和对信号噪声的敏感性.
- 传统的血压监测方法往往是侵入性的或不方便的连续,实时跟踪.
研究的目的:
- 开发和评估一个基于深度学习的BP估计模型,优化为可穿戴环境.
- 评估使用心电图 (ECG) 和心电图 (PPG) 信号用于估计血压的有效性.
- 调查噪声和信号长度对模型性能的影响.
主要方法:
- 为了估计BP,使用了三层卷积神经网络 (CNN).
- 时间序列ECG和PPG信号使用差异和值方法进行预处理,以减少噪声.
- 应用了最大聚合技术来从输入信号中提取特征.
- 该模型在49名重症监护病房患者的240万个数据样本上进行了训练和验证 (MIMIC数据库).
主要成果:
- 该模型实现了脉冲压,缩血压 (SBP) 和腹缩血压 (DBP) 的平均根平均平方误差分别为3.41,5.80和2.78mm Hg.
- 累计误差百分比小于5mm Hg是SBP的68%和DBP的93%.
- 与PPG信号相比,心电图信号在降噪方面表现优越,平均绝对误差较低 (SBP为9.72 mm Hg,DBP为6.67 mm Hg).
- 输入信号长度没有显著影响CNN的性能.
结论:
- 深度学习模型,特别是CNN,对于可穿戴式BP估计非常有效.
- 电脑心电图信号比PPG信号更适合在杂的可穿戴环境中进行BP估计.
- 没有校准的短采样框架可以用于有效的BP估计.
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Direct Method
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