使用基于变压器的模型,从心电图和光电图中预测呼吸速率.
Qi Zhao1, Fang Liu2, Yide Song2
1School of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China.
Bioengineering (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了TransRR,这是一个增强的变压器模型,用于使用心电图和PPG信号预测呼吸速率 (RR). TransRR显著提高了准确性,为自动化临床RR估计铺平了道路.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 医疗保健中的机器学习
背景情况:
- 呼吸速率 (RR) 对于诊断和预后至关重要,但很难准确测量.
- 手动RR计数是标准的,但容易出现错误.
- 从心电图和PPG信号中预测RR的现有算法具有有限的准确性和概括性.
研究的目的:
- 开发一个增强的变压器模型 (TransRR) 以使用ECG和PPG信号准确预测RR.
- 评估模型对未见数据的概括能力.
- 引入一种新的预处理管道,以提高模型性能.
主要方法:
- 开发了一种增强的变压器模型,其中包含了初始块.
- 在BIDMC和CapnoBase数据集上使用了主体级十倍交叉验证.
- 实施了ECG和PPG信号的新预处理管道.
主要成果:
- 与五种流行的深度学习方法相比,实现了更高的性能.
- 平均绝对误差减少了36.5% (MAE=1.2).
- 在测试组上,相关系数增加了84.8% (R=0.85).
结论:
- TransRR模型在RR预测准确性和概括性方面取得了显著的改进.
- 拟议的预处理管道进一步提高了模型性能.
- 预计TransRR将加速自动化RR估计的临床采用.
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