血压估计和分类使用无基准信号的光电质量学信号:一个深度学习框架
Pankaj1, Ashish Kumar1,2, Rama Komaragiri1
1Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India.
Physical and engineering sciences in medicine
|September 25, 2023
概括
这项研究引入了一种使用光聚缩学 (PPG) 信号的深度学习模型,以准确估计血压 (BP) 和分类BP阶段,即使使用运动器件. 优化的模型提供了一个强大的,单传感器解决方案,用于心血管健康监测.
科学领域:
- 生物医学工程 生物医学工程
- 心血管健康监测 监测心血管健康
- 医疗保健中的人工智能
背景情况:
- 血压 (BP) 等血液动力学参数对于评估心血管系统功能至关重要.
- 持续的血压监测有助于早期检测心血管疾病 (CVD),但运动器件往往阻碍了准确性.
- 现有的方法可能需要多个传感器 (例如,ECG和PPG),这限制了实际应用.
研究的目的:
- 开发一个优化的深度学习模型,以从单个光聚光显微镜 (PPG) 信号同时估计静缩血压 (SBP) 和静缩血压 (DBP).
- 用同一个网络来分类血压阶段 (正常血压,高血压前期,高血压),坚固于运动的文物.
- 为了实现准确的,非侵入性的血压监测,适合于可穿戴设备.
主要方法:
- 使用卷积神经网络 (CNN) 来自动从PPG信号中提取特征.
- 采用超级变换来将1D PPG信号转换为2D时间频率 (TF) 谱图,将真信号与运动器件分开.
- 在MIMIC-III数据集上训练并验证模型.
主要成果:
- 获得的平均绝对误差 (MAE) 为SBP的2.71 mmHg和DBP的2.42 mmHg.
- 证明了高分类准确度:SBP的96.79%和DBP阶段的98.94%.
- 超级转换器有效地减轻了运动工件干扰.
结论:
- 拟议的优化深度学习框架准确地估计和分类BP从单通道PPG信号,即使在存在运动工件.
- 该方法不那么复杂,适合在资源有限的可穿戴设备上部署.
- 这种方法为持续的心血管健康监测提供了一个有希望的,非侵入性的替代方案.
关键词:
血压 血压 血压 血压分类 分类 分类 分类.卷积神经网络是一个卷积神经网络.深度学习 (Deep Learning) 是一种深度学习.在高血压的高血压.摄影电解质量学 摄影电解质量学回归是一种回归.可穿戴设备是一种可穿戴设备.更多相关视频
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