一个新的呼吸速率估计算法从使用深度学习模型的光电脉冲图
Wee Jian Chin1,2, Ban-Hoe Kwan1,2, Wei Yin Lim3
1Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia.
Diagnostics (Basel, Switzerland)
|February 10, 2024
概括
这项研究提出了一种新方法,可以使用较小的数据窗口,从光斑血图 (PPG) 信号中准确估计呼吸速率 (RR). 这种新的方法可以在减少计算需求的情况下实现可靠的RR估计.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 信号处理 信号处理
背景情况:
- 呼吸速率 (RR) 是诊断肺炎等疾病的关键重要标志.
- 手动的RR计数通常是不准确和不一致的.
- 现有的自动化RR估计方法需要大数据窗口或缺乏精度.
研究的目的:
- 开发一种新的连续RR估计方法,使用光电发热图 (PPG) 信号.
- 为了实现精确的RR估计,减少窗口大小和处理需求.
- 为了比较RR估计的经典和深度学习算法的性能.
主要方法:
- 使用BIDMC和CapnoBase数据集进行算法评估.
- 使用呼吸速率估计 (RRest) 工具箱.
- 开发了一种深度学习模型,具有卷积和长期短期记忆层.
- 评估了经典的信号处理技术和深度学习模型.
主要成果:
- 最优的古典方法在BIDMC数据集上实现了1.9次呼吸/分钟的平均绝对误差 (MAE).
- 新的深度学习模型使用7s窗口实现了2次呼吸/分钟的MAE.
- 拟议的模型在使用更大的窗口大小 (16s,32s,64s) 的其他深度学习算法中表现出色.
结论:
- 从PPG信号进行连续RR估计是可行的,窗口大小减少.
- 开发的深度学习模型为RR监控提供了一个有希望的,计算效率高的方法.
- 对信号处理的进一步研究可能会提高基于PPG的RR估计准确性.
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