College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China.
Department of Cardiology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
J Healthc Eng. 2021 Apr 21;2021:6633832. doi: 10.1155/2021/6633832. eCollection 2021.
Recently, the incidence of hypertension has significantly increased among young adults. While aerobic exercise intervention (AEI) has long been recognized as an effective treatment, individual differences in response to AEI can seriously influence clinicians' decisions. In particular, only a few studies have been conducted to predict the efficacy of AEI on lowering blood pressure (BP) in young hypertensive patients. As such, this paper aims to explore the implications of various cardiopulmonary metabolic indicators in the field by mining patients' cardiopulmonary exercise testing (CPET) data before making treatment plans. CPET data are collected "breath by breath" by using an oxygenation analyzer attached to a mask and then divided into four phases: resting, warm-up, exercise, and recovery. To mitigate the effects of redundant information and noise in the CPET data, a sparse representation classifier based on analytic dictionary learning was designed to accurately predict the individual responsiveness to AEI. Importantly, the experimental results showed that the model presented herein performed better than the baseline method based on BP change and traditional machine learning models. Furthermore, the data from the exercise phase were found to produce the best predictions compared with the data from other phases. This study paves the way towards the customization of personalized aerobic exercise programs for young hypertensive patients.
最近，年轻人中高血压的发病率显著增加。虽然有氧运动干预（AEI）早已被认为是一种有效的治疗方法，但个体对 AEI 的反应差异会严重影响临床医生的决策。特别是，只有少数研究探讨了预测 AEI 降低年轻高血压患者血压效果的方法。因此，本文旨在通过挖掘患者心肺运动测试（CPET）数据来探讨各种心肺代谢指标在该领域的意义，以便制定治疗计划。CPET 数据通过附在面罩上的氧气分析仪“逐口气”收集，然后分为休息、热身、运动和恢复四个阶段。为了减轻 CPET 数据中冗余信息和噪声的影响，设计了一种基于解析字典学习的稀疏表示分类器，以准确预测个体对 AEI 的反应性。重要的是，实验结果表明，与基于血压变化的基线方法和传统机器学习模型相比，本文提出的模型表现更好。此外，与其他阶段的数据相比，运动阶段的数据产生了最佳预测。本研究为年轻高血压患者制定个性化有氧运动方案铺平了道路。
