一种基于心电图和光电图的多参数融合方法,用于基于心电图和光电图的无袖连续血压估计
Gang Ma1,2, Jie Zhang2, Jing Liu3
1School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China.
Micromachines
|July 8, 2023
概括
本研究介绍了一种可穿戴设备,用于使用心电图和PPG信号监测无袖血压 (BP). 特性选择显著提高了非侵入性血压估计的准确性,使得健康状况的持续评估.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 在医疗保健中的数据科学.
背景情况:
- 血压 (BP) 是一个关键的健康指标,但传统的袖口方法提供有限的动态见解.
- 无袖血压监测有望提供持续的生理数据,以改善健康管理和血压控制评估.
研究的目的:
- 开发一种可穿戴设备,用于持续的生理信号采集 (ECG和PPG).
- 提出一种多参数融合方法,用于使用选定的特征进行非侵入性BP估计.
- 验证特征选择和融合技术在提高BP预测准确性的有效性.
主要方法:
- 设计了一种可穿戴设备,用于连续心电图 (ECG) 和光电图 (PPG) 信号采集.
- 从经过处理的波形中提取了25个特征,并使用高斯偶数相互信息 (MI) 来进行特征选择.
- 训练了一个随机森林 (RF) 模型来估计心压 (SBP) 和心压 (DBP),使用MIMIC-III进行训练和私人数据进行测试.
主要成果:
- 特性选择减少了SBP和DBP的平均绝对误差 (MAE) 和标准偏差 (STD).
- 在选择后,SBP MAE/STD从9.12 ± 9.83 mmHg提高到7.93 ± 9.12 mmHg,DBP从8.31 ± 9.23 mmHg提高到7.63 ± 8.61 mmHg.
- 校准进一步将SBP MAE降低到5.21 mmHg和DBP MAE降低到4.15 mmHg,显示出显著的精度改进.
结论:
- 相互信息 (MI) 是高效的特征选择在BP预测.
- 建议使用ECG和PPG的多参数融合方法显示了准确,长期,无袖的BP监测的巨大潜力.
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