基于PPG和ECG信号的血压估计,使用知识蒸
Hui Tang1, Gang Ma2,3, Lishen Qiu2,3
1School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China.
Cardiovascular engineering and technology
|January 9, 2024
概括
这项研究引入了一种使用光电心电图 (PPG) 和心电图 (ECG) 信号进行准确血压估计的深度学习模型. 知识蒸提高了模型的效率和预测准确性,为传统方法提供了有希望的替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
- 心血管监测 心血管监测
背景情况:
- 基于袖子和侵入性血压 (BP) 监测存在重大局限性.
- 易于访问的生物信号为非侵入性BP估计提供了潜在的解决方案.
- 光电心电图 (PPG) 和心电图 (ECG) 信号是随时可用的生物信号.
研究的目的:
- 开发和验证一种深度学习模型,用于估计心压和腹压血压.
- 为了利用知识蒸培训一个高效和准确的BP估计模型.
- 评估模型的性能与既定医疗标准相比.
主要方法:
- 设计了一种多阶段的深度学习架构,包括卷积,双向反复和注意层.
- 使用知识蒸,训练一个较小的学生模型,使用来自较大的教师模型的见解.
- 该模型从MIMIC III数据库中对1205名受试者进行了训练和验证.
主要成果:
- 该模型在压缩性血压 (SBP) 和压缩性血压 (DBP) 估计方面实现了A级性能,符合AAMI标准.
- 经过知识蒸 (KD),该模型显示SBP的平均绝对误差为2.94 ± 5.61 mmHg,DBP的平均误差为2.02 ± 3.60 mmHg.
- 该KD训练方法显著减少了模型参数,同时提高了预测准确性.
结论:
- 知识蒸是血压回归模型的有益训练策略.
- 拟议的深度学习模型为血压监测提供了一种可行的,非侵入性的方法.
- 该研究强调了PPG和ECG信号的潜力,以准确估计BP.
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