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Predicting Cardiopulmonary Response to Incremental Exercise Test

机译：预测递增运动试验的心肺反应

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Cardiopulmonary exercise testing is a non-invasive method widely used to monitor various physiological signals, describing the cardiac and respiratory response of the patient to increasing workload. Since this method is physically very demanding, innovative data analysis techniques are needed to predict patient response thus lowering body stress and avoiding cardiopulmonary overload. This paper proposes the Cardiopulmonary Response Prediction (CRP) framework for early predicting the physiological signal values that can be reached during an incremental exercise test. The learning phase creates different models tailored to specific conditions (i.e., single-test and multiple-test models). Each model can be exploited in the real-time stream prediction phase to periodically predict, during the test execution, signal values achievable by the patient. Experimental results on a real dataset showed that CRP prediction is performed with a limited and acceptable error.
机译：心肺运动测试是一种非侵入性方法，广泛用于监视各种生理信号，描述患者对不断增加的工作量的心脏和呼吸反应。由于这种方法在物理上非常苛刻，因此需要创新的数据分析技术来预测患者的反应，从而降低身体压力并避免心肺超负荷。本文提出了一种心肺反应预测（CRP）框架，用于尽早预测在增量运动测试中可以达到的生理信号值。学习阶段会创建针对特定条件量身定制的不同模型（即单项测试和多项测试模型）。可以在实时流预测阶段利用每种模型来在测试执行期间定期预测患者可达到的信号值。在真实数据集上的实验结果表明，CRP预测的误差有限且可以接受。

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