IEEE J Biomed Health Inform. 2022 Dec;26(12):5953-5963. doi: 10.1109/JBHI.2022.3206100. Epub 2022 Dec 7.
Physiological response to physical exercise through analysis of cardiopulmonary measurements has been shown to be predictive of a variety of diseases. Nonetheless, the clinical use of exercise testing remains limited because interpretation of test results requires experience and specialized training. Additionally, until this work no methods have identified which dynamic gas exchange or heart rate responses influence an individual's decision to start or stop physical activity. This research examines the use of advanced machine learning methods to predict completion of a test consisting of multiple exercise bouts by a group of healthy children and adolescents. All participants could complete the ten bouts at low or moderate-intensity work rates, however, when the bout work rates were high-intensity, 50% refused to begin the subsequent exercise bout before all ten bouts had been completed (task failure). We explored machine learning strategies to model the relationship between the physiological time series, the participant's anthropometric variables, and the binary outcome variable indicating whether the participant completed the test. The best performing model, a generalized spectral additive model with functional and scalar covariates, achieved 93.6% classification accuracy and an F1 score of 93.5%. Additionally, functional analysis of variance testing showed that participants in the 'failed' and 'success' groups have significantly different functional means in three signals: heart rate, oxygen uptake rate, and carbon dioxide uptake rate. Overall, these results show the capability of functional data analysis with generalized spectral additive models to identify key differences in the exercise-induced responses of participants in multiple bout exercise testing.
通过分析心肺测量得出的生理反应已被证明可以预测多种疾病。尽管如此，运动测试的临床应用仍然受到限制，因为测试结果的解释需要经验和专业培训。此外，在这项工作之前，没有方法可以确定哪些动态气体交换或心率反应会影响个体开始或停止体育活动的决定。这项研究考察了使用先进的机器学习方法来预测一组健康儿童和青少年完成多次运动回合测试的情况。所有参与者都可以在低或中等强度的工作率下完成十次回合，但当回合工作率较高时，有 50%的人拒绝在完成所有十次回合之前开始随后的运动回合（任务失败）。我们探索了机器学习策略，以建立生理时间序列、参与者的人体测量变量以及表示参与者是否完成测试的二进制结果变量之间的关系模型。表现最好的模型是具有功能和标量协变量的广义谱加模型，其分类准确率为 93.6%，F1 得分为 93.5%。此外，方差功能分析测试表明，在“失败”和“成功”组中，参与者在三个信号（心率、摄氧量和二氧化碳摄取率）中的功能均值有显著差异。总的来说，这些结果表明，广义谱加模型的功能数据分析具有识别多次回合运动测试中参与者运动反应关键差异的能力。
