基于主动重心的青年高血压患者心肺运动时序数据增强

Data Augmentation for Cardiopulmonary Exercise Time Series of Young Hypertensive Patients Based on Active Barycenter

精准医疗的逐步兴起,如挖掘青年高血压患者的心肺运动时序数据,可以了解不同个体对有氧运动训练的响应性,有助于提高患者高血压管理计划的制定效率,更有效地实现有氧运动干预的治疗。开展该研究的瓶颈之一在于难以获取充足的样本数据。为了解决获取数据难度大、成本高等问题,利用加权动态时间规整重心平均算法来进行时间序列数据增强,重点针对重心选择和权重分配进行了研究。针对重心选择问题,首次引入了主动重心的概念,提出了代表性重心与多样性重心选择策略,改善了数据增强的效果。此外,针对现有权重分配策略的不足,提出了随机权重距离递减分配策略,避免了合成重复样本,进一步提升了模型的泛化能力。实验结果表明,在该研究背景下同时考虑重心选择与权重分配进行数据增强,可以进一步提升青年高血压患者有氧运动干预疗效预测的准确性。

The gradual rise of precision medicine,such as mining cardiopulmonary exercise time series of young hypertensive patients,can understand the response of different individuals to aerobic exercise training.This helps to improve the efficiency of hypertension management plan and achieve aerobic exercise intervention more effectively.One of the bottlenecks in this study is that it is difficult to obtain sufficient sample data.To solve the above problem,this paper adopts the weighted dynamic-time-warping barycenter averaging algorithm(WDBA)to realize data augmentation of time series,focusing on the barycenter selection and the weight assignment.In this paper,the concept of active barycenter is introduced for the first time,and the selection strategies of representative barycenter and diversity barycenter are proposed to improve the effect of data augmentation.Furthermore,aiming at the shortcomings of the existing weight assignment strategies,a random strategy with decreasing distance is proposed to further improve the generalization ability of the model by avoiding the synthesis of duplicate samples.Experimental results show that the accuracy of predicting the efficacy of aerobic exercise intervention in young hypertensive patients can be further improved by considering both the barycenter selection and the weight assignment for data augmentation in the background of this study.