数据驱动的间歇低氧训练贝叶斯优化决策方法  

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

作　　者：陈婧 史大威 蔡德恒 王军政[1] 朱玲玲 CHEN Jing;SHI Da-Wei;CAI De-Heng;WANG Jun-Zheng;ZHU Ling-Ling(Ministry of Industry and Information Technology Key Laboratory of Servo Motion System Drive and Control,School of Automation,Beijing Institute of Technology,Beijing 100081;Academy of Military Medical Sciences,Beijing 100850)

机构地区：[1]北京理工大学自动化学院工业和信息化部伺服运动系统驱动与控制重点实验室,北京100081 [2]中国人民解放军军事科学院军事医学研究院,北京100850

出　　处：《自动化学报》2023年第8期1667-1678,共12页Acta Automatica Sinica

基　　金：国家自然科学基金(61973030);北京市科技计划项目(Z161100000216134)资助。

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.The rapid economic development of Qinghai-Tibet region has led to an increasing number of groups entering the plateau,and the consequent problem of high-altitude health has become increasingly prominent.Intermittent hypoxic training(IHT)is a commonly-used preacclimatization approach before rapidly going to the plateau.It is usually designed as fixed open-loop strategies for different individuals,which has several disadvantages such as no standard formulation,lack of systematic theoretical guidance and poor efficacy.In this paper,a data-driven Bayesian closed-loop learning optimization framework of IHT strategy is designed by using small samples,and a Gaussian process model with autoregressive structure of peripheral oxygen saturation(SpO2)is built for prediction.Based on the predictive model,a risk asymmetric cost function related to the oxygen concentration rate and its direction is developed.Finally,a Bayesian optimization method with safety constraints is proposed to enable the optimal decision of IHT oxygen concentration.Given that the existing simulator cannot reflect the process dynamics of individuals,a reasonable model adaptation law is designed according to the“optimal rate theory”.The feasibility and effectiveness of the proposed closed-loop intervention method are verified by the simulator.These results indicate that the proposed learning framework can help individuals to improve their adaptability to high-altitudes,reduce their non-adaptive adverse reactions in the pretraining stage,and provide precise control solution to personalized IHT.

关 键 词：数据驱动控制 高斯过程 贝叶斯优化 风险不对称代价函数 高原适应能力提升 间歇性低氧训练 

分 类 号：R594.3[医药卫生—内科学]