检测高血压智能可穿戴设备中的异常:一个深度学习机制
C Kishor Kumar Reddy1, Vijaya Sindhoori Kaza1, R Madana Mohana2
1Stanley College of Engineering and Technology for Women, Hyderabad, India.
Frontiers in public health
|January 24, 2025
概括
本研究介绍了ResNet-LSTM用于非侵入性血压 (BP) 预测,使用智能健康监测中的深度学习. 该模型根据生理信号准确预测血压,增强远程心血管疾病管理.
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
- 心血管疾病管理管理
背景情况:
- 对实时,可访问的医疗保健的日益增长的需求需要先进的非侵入性健康监测技术.
- 非侵入性预测动脉血压 (BP) 对于管理心血管疾病至关重要,特别是在偏远地区.
- 目前的智能健康监测 (SHM) 系统在及时和准确的血压评估方面存在局限性.
研究的目的:
- 开发和验证一种新的深度学习模型,用于准确的,非侵入性的BP预测.
- 利用ResNet-LSTM架构进行增强的特征提取和从生理信号中的顺序数据处理.
- 解决当前医疗保健系统的局限性,特别是在远程监测场景中.
主要方法:
- 开发了一个新的ResNet-LSTM神经网络架构来预测BP.
- 包括心电图 (ECG) 和光电图 (PPG) 在内的生理信号被用作输入.
- 模型验证包括全面的错误分析,Leave-One-Out (LOO) 交叉验证以及对额外数据集的测试.
主要成果:
- ResNet-LSTM模型在BP预测方面表现出卓越的性能,特别是使用PPG数据.
- 在BP预测中,达到6.2mmHg的平均绝对误差 (MAE) 和8.9mmHg的根平均平方误差 (RMSE).
- 该模型表现出数据集的稳定性和概括性,尽管计算成本更高.
结论:
- 将ResNet-LSTM集成到SHM系统中显示出准确,非侵入性BP预测的巨大潜力.
- 该研究强调了异常检测在可穿戴设备的持续监控中的重要性.
- 未来的研究将专注于基于云的实时分析和精细的异常检测,以改善患者的治疗结果.
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