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基于深度学习的人体低氧状态识别

于露 金龙哲 王梦飞 徐明伟

于露, 金龙哲, 王梦飞, 徐明伟. 基于深度学习的人体低氧状态识别[J]. 工程科学学报, 2019, 41(6): 817-823. doi: 10.13374/j.issn2095-9389.2019.06.014
引用本文: 于露, 金龙哲, 王梦飞, 徐明伟. 基于深度学习的人体低氧状态识别[J]. 工程科学学报, 2019, 41(6): 817-823. doi: 10.13374/j.issn2095-9389.2019.06.014
YU Lu, JIN Long-zhe, WANG Meng-fei, XU Ming-wei. Recognition of human hypoxic state based on deep learning[J]. Chinese Journal of Engineering, 2019, 41(6): 817-823. doi: 10.13374/j.issn2095-9389.2019.06.014
Citation: YU Lu, JIN Long-zhe, WANG Meng-fei, XU Ming-wei. Recognition of human hypoxic state based on deep learning[J]. Chinese Journal of Engineering, 2019, 41(6): 817-823. doi: 10.13374/j.issn2095-9389.2019.06.014

基于深度学习的人体低氧状态识别

doi: 10.13374/j.issn2095-9389.2019.06.014
基金项目: 

国家“十三五”重点科技支撑资助项目(2016YFC0801700)

详细信息
  • 中图分类号: X912

Recognition of human hypoxic state based on deep learning

  • 摘要: 通过低氧实验提出一种快速识别人体低氧状态的方法.通过搭建深层神经网络训练实验数据识别氧气体积分数(16%~21%)与人体可耐受极端低氧气体积分数(15.5%~16%)条件下光电容积脉搏波(photoplethysmography,PPG)信号,获得人体生理状态的模式识别网络.经测试该网络的识别正确率可达92.8%.利用混淆矩阵及接受者操作性能(receiver operating characteristic,ROC)曲线分析,混淆矩阵的训练集、验证集、测试集、全集识别正确率分别达到97.9%、94.8%、92.8%和96.3%,AUC (area under curve)值接近1,认为该网络分类性能优良,并且可在4 s内完成整个识别过程.
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  • 收稿日期:  2019-03-06

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