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数据驱动的间歇低氧训练贝叶斯优化决策方法

陈婧 史大威 蔡德恒 王军政 朱玲玲

陈婧, 史大威, 蔡德恒, 王军政, 朱玲玲. 数据驱动的间歇低氧训练贝叶斯优化决策方法. 自动化学报, 2023, 49(8): 1667−1678 doi: 10.16383/j.aas.c220712
引用本文: 陈婧, 史大威, 蔡德恒, 王军政, 朱玲玲. 数据驱动的间歇低氧训练贝叶斯优化决策方法. 自动化学报, 2023, 49(8): 1667−1678 doi: 10.16383/j.aas.c220712
Chen Jing, Shi Da-Wei, Cai De-Heng, Wang Jun-Zheng, Zhu Ling-Ling. Data-driven Bayesian optimization method for intermittent hypoxic training strategy decision. Acta Automatica Sinica, 2023, 49(8): 1667−1678 doi: 10.16383/j.aas.c220712
Citation: Chen Jing, Shi Da-Wei, Cai De-Heng, Wang Jun-Zheng, Zhu Ling-Ling. Data-driven Bayesian optimization method for intermittent hypoxic training strategy decision. Acta Automatica Sinica, 2023, 49(8): 1667−1678 doi: 10.16383/j.aas.c220712

数据驱动的间歇低氧训练贝叶斯优化决策方法

doi: 10.16383/j.aas.c220712
基金项目: 国家自然科学基金(61973030), 北京市科技计划项目 (Z161100000216134)资助
详细信息
    作者简介:

    陈婧:北京理工大学自动化学院博士研究生. 主要研究方向为医学信号处理和性能评估. E-mail: jingchen@bit.edu.cn

    史大威:北京理工大学自动化学院教授. 主要研究方向为复杂采样控制系统分析与设计及在生物医学、机器人及运动系统中的应用. 本文通信作者. E-mail: daweishi@bit.edu.cn

    蔡德恒:北京理工大学自动化学院博士研究生. 主要研究方向为事件触发的采样控制、估计与学习以及闭环给药系统控制算法设计与实现. E-mail: dehengcai@bit.edu.cn

    王军政:北京理工大学自动化学院教授. 主要研究方向为运动驱动与控制, 电液伺服/比例控制, 试验测试与负载模拟, 机器人控制. E-mail: wangjz@bit.edu.cn

    朱玲玲:中国人民解放军军事科学院军事医学研究院研究员. 主要研究方向为高原等特殊环境对机体损伤与防护措施的研究. E-mail: zhull@bmi.ac.cn

Data-driven Bayesian Optimization Method for Intermittent hypoxic Training Strategy Decision

Funds: Supported by National Natural Science Foundation of China (61973030) and Beijing Municipal Science and Technology Commission (Z161100000216134)
More Information
    Author Bio:

    CHEN Jing Ph.D. candidate at the School of Automation, Beijing Institute of Technology. Her research interest covers medical signal processing and performance assessment

    SHI Da-Wei Professor at the School of Automation, Beijing Institute of Technology. His research interest covers analysis & design of advanced sampled-data control systems, with applications to biomedical engineering, robotics and motion systems. Corresponding author of this paper

    CAI De-Heng Ph.D. candidate at the School of Automation, Beijing Institute of Technology. His research interest covers event-triggered sampled-data control, state estimation and machine learning, and the control algorithm design and implementation of closed-loop drug delivery systems

    WANG Jun-Zheng Professor at the School of Automation, Beijing Institute of Technology. His research interest covers motion drive and control, electro-hydraulic servo/proportional control, test experiment and load simulation, and robotic control

    ZHU Ling-Ling Professor of Academy of Military Medical Sciences. Her research interest covers body damage and protective measures in high-altitude environment

  • 摘要: 青藏地区快速的经济发展使得进入高原的群体数量日益增加, 随之而来的高原健康问题也愈发突出. 间歇性低氧训练(Intermittent hypoxic training, IHT)是急进高原前常使用的预习服方法, 一般针对不同个体均设置固定的开环策略, 存在方案制定无标准、系统化的理论指导缺乏、效果不明显等问题. 针对以上情况, 设计了一种小样本数据驱动的IHT策略贝叶斯闭环学习优化框架, 建立自回归结构的高斯过程血氧饱和度(Peripheral oxygen saturation, SpO2)预测模型, 并考虑高低风险事件对训练的影响, 设计与氧浓度变化方向和速率相关的风险不对称代价函数, 提出具有安全约束的贝叶斯优化方法, 实现IHT最优供氧浓度的优化决策. 考虑到现有仿真器无法反映个体动态变化过程, 依据“最优速率理论”设计了合理的模型自适应变化律. 所提出闭环干预方法通过该仿真器进行了可行性和有效性验证. 说明该学习框架能够指导个体提升高原适应能力, 减轻其在预习服阶段的非适应性不良反应, 为个性化IHT提供精准调控手段.
  • 图  1  高原适应性能力提升的IHT策略优化决策算法流程图

    Fig.  1  Flow chart of IHT optimization decision algorithm for high-altitude adaptability improvement

    图  2  高斯过程预测算法示意图

    Fig.  2  Flow chart of Gaussian process prediction algorithm

    图  3  所设计代价函数$ {\cal{L}}_{v} $部分的惩罚强度在不同$ \Delta c $下的变化

    Fig.  3  The penalty changes of designed $ {\cal{L}}_v $ term under different $ \Delta c $ values

    图  4  虚拟受试者1和2采取开环和闭环策略进行IHT的$ {\rm{ SpO}}_2 $曲线

    Fig.  4  The $ {\rm{ SpO}}_2 $ curves of simulated subject 1 and 2 that perform IHT based on traditional open-loop strategy and proposed closed-loop strategy

    图  5  虚拟受试者3和4采取开环和闭环策略进行IHT的$ {\rm{ SpO}}_2 $曲线

    Fig.  5  The $ {\rm{ SpO}}_2 $ curves of simulated subject 3 and 4 that perform IHT based on traditional open-loop strategy and proposed closed-loop strategy

    图  6  10名虚拟受试者采取开环和闭环策略进行IHT的$ {\rm{ SpO}}_2 $曲线

    Fig.  6  The $ {\rm{ SpO}}_2 $ curves of 10 simulated subjects that perform IHT based on traditional open-loop strategy and proposed closed-loop strategy

    表  1  相关参数取值

    Table  1  Related parameters

    参数 含义 取值
    $ n_s $ 一日IHT的低氧段总数 8
    $ n_t $ 一段低氧段预测点总数 200
    $ n_p $ 一段低氧段采样点总数 300
    $ \sigma^{2} $ 平方指数核函数超参数 10
    $ \ell $ 平方指数核函数超参数 10
    $ \boldsymbol{y}_{r} $ 目标$ {\rm{ SpO}}_2 $向量 $ [95H_{50},90H_{50}, 85H_{100}]^\mathrm{T} $
    $ Q $ 代价函数惩罚矩阵 136I
    $ w $ 代价函数权重系数 1000
    $ c_{l,\min} $ 供氧浓度下阈值 (%) 10
    $ c_{l,\max} $ 供氧浓度上阈值 (%) 15
    下载: 导出CSV

    表  2  虚拟受试者1和2采取开环策略进行IHT和采取闭环策略进行IHT的效果对比

    Table  2  Comparison results of simulated subject 1 and 2 trained by using traditional open-loop strategy and proposed closed-loop strategy

    个体设定 指标 初始状态 开环策略 闭环策略
    $ c_{o}=13 $,
    $ \Delta c_{op}=-1.5 $
    DSI (s) 137 84 256
    SpO2平均值(%) 90.9 84.9 93.6
    SpO2标准差(%) 5.5 5.4 4.8
    $ c_{o}=12 $,
    $ \Delta c_{op}=-1.5 $
    DSI (s) 137 90 300
    SpO2平均值(%) 90.9 87.8 96.2
    SpO2标准差(%) 5.5 5.4 3.6
    下载: 导出CSV

    表  3  虚拟受试者3和4采取开环策略进行IHT和采取闭环策略进行IHT的效果对比

    Table  3  Comparison results of simulated subject 3 and 4 trained by using traditional open-loop strategy and proposed closed-loop strategy

    个体设定 指标 初始状态 开环策略 闭环策略
    $ c_{o}=12 $,
    $ \Delta c_{op}=-1 $
    DSI (s) 154 136 203
    SpO2平均值(%) 91.4 89.5 94.7
    SpO2标准差(%) 6.1 6.5 5.0
    $ c_{o}=12 $,
    $ \Delta c_{op}=-1.5 $
    DSI (s) 154 81 300
    SpO2平均值 (%) 91.4 89.5 96.4
    SpO2标准差(%) 6.1 6.6 4.1
    下载: 导出CSV

    表  4  10名虚拟受试者采取开环策略进行IHT和采取闭环策略进行IHT的效果对比

    Table  4  Comparison results of 10 simulated subjects trained by using traditional open-loop strategy and proposed closed-loop strategy

    受试者 指标 初始状态 开环策略 闭环策略
    1 DSI (s) 137 32 300
    SpO2平均值(%) 90.9 84.9 96.4
    SpO2标准差(%) 5.46 5.37 3.53
    2 DSI (s) 140 53 300
    SpO2平均值(%) 92.7 86.1 95.6
    SpO2标准差(%) 4.72 4.85 3.73
    3 DSI (s) 138 31 300
    SpO2平均值(%) 92.3 84.8 97.4
    SpO2标准差(%) 4.81 4.70 2.88
    4 DSI (s) 271 153 300
    SpO2平均值(%) 94.6 89.9 98.5
    SpO2标准差(%) 4.04 4.10 2.29
    5 DSI (s) 138 35 184
    SpO2平均值(%) 92.3 87.1 96.3
    SpO2标准差(%) 4.32 4.33 3.51
    6 DSI (s) 138 20 279
    SpO2平均值(%) 92.3 84.8 94.1
    SpO2标准差(%) 4.81 4.79 4.34
    7 DSI (s) 42 35 78
    SpO2平均值(%) 89.0 85.1 91.5
    SpO2标准差(%) 5.44 5.52 5.19
    8 DSI (s) 91 84 176
    SpO2平均值(%) 89.9 87.6 92.1
    SpO2标准差(%) 6.17 6.16 5.63
    9 DSI (s) 138 66 300
    SpO2平均值(%) 91.3 88.2 95.1
    SpO2标准差(%) 5.01 4.86 4.02
    10 DSI (s) 138 44 300
    SpO2平均值(%) 91.3 84.8 96.0
    SpO2标准差(%) 4.94 4.78 3.64
    下载: 导出CSV

    表  5  10名虚拟受试者的对比结果

    Table  5  Comparison results of 10 simulated subjects

    指标 初始状态 开环策略 闭环策略
    DSI (s) 137.1 56.3 251.7
    SpO2平均值(%) 91.7 86.3 95.3
    SpO2标准差(%) 4.97 4.95 3.88
    下载: 导出CSV
  • [1] Andrew M L, Peter H H. Medical conditions and high-altitude travel. New England Journal of Medicine, 2022, 386(4): 364-373 doi: 10.1056/NEJMra2104829
    [2] Joshua C T, Philip N A. Global and country-level estimates of human population at high altitude. Proceedings of the National Sciences, 2021, 118(18): e2102463118 doi: 10.1073/pnas.2102463118
    [3] Cobb A B, Levett D Z H, Mitchell K, Aveling W, Hurlbut D, Gilbert-Kawai E, et.al. Physiological responses during ascent to high altitude and the incidence of acute mountain sickness. Physiological reports, 2021, 9(7): e14809
    [4] Gudbjartsson T, Sigurdsson E, Gottfredsson M, Bjornsson O M, Gudmundsson G. High altitude illness and related diseases - A review. Laeknabladid, 2019, 105(11): 499-507
    [5] Victor S, Jan C P, and Katarína K. Manifestation of intracranial lesions at high altitude: Case report and review of the literature. High Altitude Medicine & Biology, 2021, 22(1): 87-89
    [6] Fulco C S, Beidleman B A, Muza S R. Effectiveness of preacclimatization strategies for highaltitude exposure. Exercise and Sport Sciences Reviews, 2013, 41(1): 55-63 doi: 10.1097/JES.0b013e31825eaa33
    [7] Ambroży T, Maciejczyk M, Klimek A T, Wiecha S, Stanula A, Snopkowski P, et.al. The effects of intermittent hypoxic training on anaerobic and aerobic power in boxers. International Journal of Environmental Research and Public Health, 2020, 17(24): 9361 doi: 10.3390/ijerph17249361
    [8] Wille M, Gatterer H, Mairer K, Philippe M, Schwarzenbacher H, Faulhaber M, et.al. Short-term intermittent hypoxia reduces the severity of acute mountain sickness. Medicine & Science in Sports, 2012, 22(5): e79-e85
    [9] 刘园园. 高原健康理论框架下的渐进型间歇性低氧预习服训练研究 [博士学位论文], 山东大学, 中国, 2014

    Liu Yuan-Yuan. Short-Term Intermittent Hypoxia Reduces the Severity of Acute Mountain Sickness [Ph.D. dissertation], Shandong University, China, 2014
    [10] Treml B, Kleinsasser A, Hell T, Knotzer H, Wille M, Burtscher M. Carry-over quality of pre-acclimatization to altitude elicited by intermittent hypoxia: A participant-blinded, randomized controlled trial on antedated acclimatization to altitude. Frontiers in Physiology, DOI: 10.3389/fphys.2020.00531
    [11] Gangwar A, Pooja, Sharma M, Singh K, Patyal A, Bhaumik G, et.al. Intermittent normobaric hypoxia facilitates high altitude acclimatization by curtailing hypoxia-induced infammation and dyslipidemia. Pflugers Archiv, 2019, 471(7):949-959 doi: 10.1007/s00424-019-02273-4
    [12] 杨军, 俞梦孙, 曹征涛, 吴峰, 张宏金, 王海涛, 等.间歇性递增式常压低氧暴露训练对高原习服效果的研究. 中华航空航天医学杂志, 2012, 3: 161-164

    Yang Jun, Yu Meng-Sun, Cao Zheng-Tao, Wu Feng, Zhang Hong-Jin, Wang Hai-Tao, et.al. Study on the effect of increasing intermittent hypoxia exposure on altitude acclimatization. Chinese Journal of Aerospace Medicine, 2012, 3: 161-164
    [13] Kwiatkowska M, Atkins M S, Ayas N T, Ryan C F. Knowledge-based data analysis: First step toward the creation of clinical prediction rules using a new typicality measure. IEEE Transactions on Information Technology in Biomedicine, 2007, 11(6):651-660 doi: 10.1109/TITB.2006.889693
    [14] Sakellarios A I, Räber L, Bourantas C V, Exarchos T P, Athanasiou L S, Pelosi G, et.al. Prediction of atherosclerotic plaque development in an In Vivo coronary arterial segment based on a multilevel modeling approach. IEEE Transactions on Biomedical Engineering, 2017, 64(8):1721-1730 doi: 10.1109/TBME.2016.2619489
    [15] 喻勇, 司小胜, 胡昌华, 崔忠马, 李洪鹏. 数据驱动的可靠性评估与寿命预测研究进展:基于协变量的方法. 自动化学报, 2018, 44(2): 216-227

    Yu Yong, Si Xiao-Sheng, Hu Chang-Hua, Cui Zhong-Ma, Li Hong-Peng. Data driven reliability assessment and life-time prognostics: A review on covariate models. Acta Automatica Sinica, 2018, 44(2): 216-227
    [16] 李天梅, 司小胜, 刘翔, 裴洪. 大数据下数模联动的随机退化设备剩余寿命预测技术. 自动化学报, 2022, 48(9): 2119-2141 doi: 10.16383/j.aas.c201068

    Li Tian-Mei, Si Xiao-Sheng, Liu Xiang, Pei Hong. Data-model interactive remaining useful life prediction technologies for stochastic degrading devices with big data. Acta Automatica Sinica, 2022, 48(9): 2119-2141 doi: 10.16383/j.aas.c201068
    [17] 蒋珂, 蒋朝辉, 谢永芳, 潘冬, 桂卫华. 基于动态注意力深度迁移网络的高炉铁水硅含量在线预测方法. 自动化学报, DOI: 10.16383/j.aas.c210524"> 10.16383/j.aas.c210524

    Jiang Ke, Jiang Zhao-Hui, Xie Yong-Fang, Pan Dong, Gui Wei-Hua. Online prediction method for silicon content of molten iron in blast furnace based on dynamic attention deep transfer network. Acta Automatica Sinica, DOI: 10.16383/j.aas.c210524"> 10.16383/j.aas.c210524
    [18] Box G E, Jenkins G M, Reinsel G C, Ljung G M. Time Series Analysis: Forecasting and Control. Hoboken: John Wiley & Sons, 2015.
    [19] Xie J, Wang Q. Benchmarking machine learning algorithms on blood glucose prediction for type I diabetes in comparison with classical time-series models. IEEE Transactions on Biomedical Engineering, 2020, 67(11): 3101-3124 doi: 10.1109/TBME.2020.2975959
    [20] Moniri A, Terracina D, Rodriguez-Manzano J, Strutton P H, Georgiou P. Real-time forecasting of sEMG features for trunk muscle fatigue using machine learning. IEEE Transactions on Biomedical Engineering, 2021, 68(2): 718-727 doi: 10.1109/TBME.2020.3012783
    [21] Michalis K T. Variational learning of inducing variables in sparse Gaussian processes. In: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics. Florida, USA: PMLR, 2009. 567−574
    [22] Beckers T, Hirche S. Prediction with approximated gaussian process dynamical models. IEEE Transactions on Automatic Control, 2022, 68: 6460-6473
    [23] Lee S I, Mortazavi B, Hoffman H A, Lu D S, Li C, Paak B H, et.al. A prediction model for functional outcomes in spinal cord disorder patients using gaussian process regression. IEEE Transactions on Biomedical Engineering, 2016, 20(1): 91-99
    [24] Huang H, Song Y, Peng X, Ding S X, Zhong W, Du W, et.al. A sparse nonstationary trigonometric gaussian process regression and its application on nitrogen oxide prediction of the diesel engine. IEEE Transactions on Industrial Informatics, 2021, 17(12): 8367-8377 doi: 10.1109/TII.2021.3068288
    [25] 史大威, 蔡德恒, 刘蔚, 王军政, 纪立农. 面向智能血糖管理的餐前胰岛素剂量贝叶斯学习优化方法. 自动化学报, DOI: 10.16383/j.aas.c210067

    Shi Da-Wei, Cai De-Heng, Liu Wei, Wang Jun-Zheng, Ji Li-Nong. Bayesian learning based optimization of meal bolus dosage for intelligent glucose management. Acta Automatica Sinica, DOI: 10.16383/j.aas.c210067
    [26] 金哲豪, 刘安东, 俞立. 基于GPR和深度强化学习的分层人机协作控制. 自动化学报, 2022, 48(9): 1-11

    Jin Zhe-Hao, Liu An-Dong, Yu Li. Hierarchical human-robot cooperative control based on GPR and DRL. Acta Automatica Sinica, 2022, 48(9): 1-11
    [27] Rosolia U, Zhang X, Borrelli F. Data-driven predictive control for autonomous systems. Annual Review of Control, Robotics, and Autonomous Systems, 2018, 1(1): 259-286 doi: 10.1146/annurev-control-060117-105215
    [28] Yu M. Human-performance engineering at high altitude. Science Supp, 2014: 7−8
    [29] Chen J, Xiao R, Wang L, Zhu L, Shi D. Unveiling interpretable key performance indicators in hypoxic response: a system identification approach. IEEE Transactions on Industrial Electronics, 2022, 69(12): 13676-13685 doi: 10.1109/TIE.2021.3137618
    [30] Chen J, Tian Y, Zhang G, Cao Z, Zhu L, Shi D. IoT-enabled intelligent dynamic risk assessment of acute mountain sickness: The role of event-triggered signal processing. IEEE Transactions on Industrial Informatics, 2023, 19(1): 730−738
    [31] Williams C K I, Rasmussen C E. Gaussian Processes for Machine Learning. Cambridge: The MIT Press, 2006.
    [32] Hackett, Peter H. and Roach, Robert C. High-altitude llness. New England Journal of Medicine, 2001, 345(2): 107-114 doi: 10.1056/NEJM200107123450206
    [33] Levine B D, Stray-Gundersen J. Dose-response of altitude training: how much altitude is enough? Advances in Experimental Medicine and Biology, 2006, 69: 233-247
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  • 收稿日期:  2022-09-08
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  • 网络出版日期:  2023-03-21
  • 刊出日期:  2023-08-21

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