使用尖端神经网络进行基于PPG的节能呼吸速率估计.
Geunbo Yang1, Youngshin Kang1, Peter H Charlton2
1Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
这项研究引入了一种新的尖端神经网络,用于从光电缩图 (PPG) 信号中估计呼吸速率 (RR). 该模型提供精确和节能的RR监控,推进生物医学信号处理.
科学领域:
- 生物医学信号处理
- 医疗保健中的人工智能
- 生理监测 生理监测
背景情况:
- 呼吸速率 (RR) 是与整体健康相关的关键生命体征.
- 光电缩图 (PPG) 信号通常用于提取呼吸系统信息.
- 使用信号处理和深度学习的现有RR估计方法存在局限性.
研究的目的:
- 提出使用尖端神经网络 (SNN) 的端到端呼吸速率估计方法.
- 为了最大限度地减少RR估计数据转换期间的信息丢失.
- 评估拟议的SNN模型的有效性和能源效率.
主要方法:
- 使用了第三代人工神经网络模型:尖端神经网络 (SNN).
- 使用PPG信号段作为直接输入,将它们转换为连续的尖峰事件.
- 嵌入式基于反的整合和发射神经元用于时间信息传输.
- 对BIDMC呼吸数据集的模型进行了评估,窗口大小各不相同 (16,32,64秒).
主要成果:
- 实现的平均绝对误差为1.37 ± 0.04,1.23 ± 0.03和1.15 ± 0.07分别为16,32,64秒的窗口大小.
- 与其他深度学习模型相比,证明了更高的能源效率.
- 从PPG信号直接成功估计了RR,信息损失最小.
结论:
- 尖端神经网络显示出精确高效的呼吸率监测的巨大潜力.
- 拟议的SNN方法提供了一种新且有效的方法,用于从PPG信号中估计RR.
- 这一进步有助于生物医学信号处理和远程患者监控领域.
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