根据心肺最佳点构建反向传播神经网络最大摄氧量预测模型  

Constructing the prediction model of maximal oxygen uptake by back-propagation neural network based on the cardiorespiratory optimal point

作　　者：吴东哲 高晓嶙[1] 李闯涛 王昊 Wu Dongzhe;Gao Xiaolin;Li Chuangtao;Wang Hao(China Institute of Sport Science,Beijing 100061,China)

机构地区：[1]国家体育总局体育科学研究所,北京市100061

出　　处：《中国组织工程研究》2023年第8期1224-1231,共8页Chinese Journal of Tissue Engineering Research

基　　金：国家体育总局体育科学研究所基本科研业务费资助项目(基本19-18),项目负责人:高晓嶙。

摘　　要：背景:研究证明最大摄氧量被认为是评价有氧运动能力的“金标准”,但测试其所需运动强度较大且存在指标再现性低、测试者主观影响效应等限制因素。目的:通过反向传播神经网络采用新型次最大运动评估指标“心肺最佳点”构建最大摄氧量预测模型。方法:试验经国家体育总局体育科学研究所伦理委员会批准,招募80名健康大学生受试者(男40名,女40名),了解试验流程、目的并自愿签署知情同意书配合完整试验过程。受试者进行递增负荷心肺运动试验,采集最大摄氧量与心肺最佳点等相关指标,进行相关性分析获得具有统计学意义的指标,并构建最大摄氧量预测模型。结果与结论:①最大摄氧量与心肺最佳点、体质量指数、性别、心肺最佳点对应的摄氧量和功率均存在显著相关性(P<0.01);②运用反向传播神经网络构建经典3层拓扑结构最大摄氧量预测模型(包含5个输入层、10个隐藏层和1个输出层),该模型预测值与实测值绝对误差均值为0.227 L/min、相对误差均值为12%,提示基于心肺最佳点构建的反向传播神经网络可准确且有效预测最大摄氧量;③反向传播神经网络模型最大摄氧量预测值与多元线性回归预测值相比差异无显著性意义(P>0.05),但依据心肺最佳点构建的反向传播神经网络模型预测精度要优于多元线性回归模型。BACKGROUND:Maximal oxygen uptake is considered as the“gold standard”for evaluating aerobic exercise capacity and cardiopulmonary health.However,the measurement of maximal oxygen uptake requires a strong exercise load,and there are some limitations such as low reproducibility of the index and subjective effect of the test participants.OBJECTIVE:To construct the prediction model of maximal oxygen uptake using the new sub-maximal exercise evaluation index-cardiorespiratory optimal point-through the back-propagation neural network.METHODS:The trial protocol was approved by the Ethics Committee of the China Institute of Sports Science.Eighty healthy college students(40 males and 40 females)were randomly recruited.They were fully informed of the trial process and purpose and voluntarily signed informed consent to cooperate with the whole trial process.The participants underwent an incremental load cardiopulmonary exercise test to identify the maximal oxygen uptake,cardiorespiratory optimal point,and other related indicators for correlation analysis to obtain statistically significant indicators.Then,the prediction model of maximal oxygen uptake was built.RESULTS AND CONCLUSION:There were significant correlations between maximal oxygen uptake and cardiorespiratory optimal point,body mass index,sex,oxygen uptake and power corresponding to the cardiorespiratory optimal point(P<0.01).The prediction model of maximal oxygen uptake with classical threelayer topology was established using the back-propagation neural network,including 5 input layers,10 hidden layers and 1 output layer.The mean absolute and relative errors between the predicted and measured values of the model were 0.227 L/min and 12%,respectively.This indicated that the back propagation neural network model built based on the cardiorespiratory optimal point could accurately and effectively predict the maximal oxygen uptake.There was no significant difference between the maximal oxygen uptake predicted value of the back propagation neural network model and t

关 键 词：心肺最佳点 最大摄氧量 反向传播神经网络 心肺运动试验 

分 类 号：R459.9[医药卫生—治疗学] R318[医药卫生—临床医学] R874