Device-measured physical activity data for classification of patients with ventricular arrhythmia events: A pilot investigation.
Device-measured physical activity data for classification of patients with ventricular arrhythmia events: A pilot investigation.
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DOI：
10.1371/journal.pone.0206153
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Rosenberg MA
中科院分区：
文献类型：
--
作者：
Marzec L;Raghavan S;Banaei-Kashani F;Creasy S;Melanson EL;Lange L;Ghosh D;Rosenberg MA
低水平的体力活动与死亡风险增加有关，特别是在心脏病患者中，但大多数研究都是基于自我报告。心脏植入式电子设备（CIED）提供了更长时间收集数据的机会。然而，由于数据的时间序列性质，在量化活动计量的最佳办法上的一致意见有限。我们检查了235名CIED受试者的身体活动时间序列数据，至少365天不间断测量。原始每日体力活动的汇总统计（分钟/天），包括统计时刻（例如，平均值、标准差、偏度、峰度）、时间序列回归系数、频域分量和预测的预测值，并用于预测装置记录的室性心动过速（VT）事件的发生。在使用主成分分析的无监督分析中，我们发现，虽然某些特征倾向于彼此靠近，但大多数特征在活动空间中提供了合理的分布，而没有很大程度的冗余。在监督分析中，我们发现在单变量和多变量方法中与结果相关的几个特征（P < 0.05），但很少在模型之间保持一致。使用机器学习方法，将数据分为训练集和测试集，并拟合从简单单变量逻辑回归到集成决策树等复杂性的模型，与任何方法的朴素方法相比，风险分类没有任何改进。尽管标准方法识别了与VT风险相关的身体活动数据的汇总特征，但机器学习方法发现这些特征都没有提供分类方面的改进。未来的研究需要探索和验证基于设备测量活动的VT风险分类中的特征提取和机器学习方法。
Low levels of physical activity are associated with increased mortality risk, especially in cardiac patients, but most studies are based on self-report. Cardiac implantable electronic devices (CIEDs) offer an opportunity to collect data for longer periods of time. However, there is limited agreement on the best approaches for quantification of activity measures due to the time series nature of the data. We examined physical activity time series data from 235 subjects with CIEDs and at least 365 days of uninterrupted measures. Summary statistics for raw daily physical activity (minutes/day), including statistical moments (e.g., mean, standard deviation, skewness, kurtosis), time series regression coefficients, frequency domain components, and forecasted predicted values, were calculated for each individual, and used to predict occurrence of ventricular tachycardia (VT) events as recorded by the device. In unsupervised analyses using principal component analysis, we found that while certain features tended to cluster near each other, most provided a reasonable spread across activity space without a large degree of redundancy. In supervised analyses, we found several features that were associated with the outcome (P < 0.05) in univariable and multivariable approaches, but few were consistent across models. Using a machine-learning approach in which the data was split into training and testing sets, and models ranging in complexity from simple univariable logistic regression to ensemble decision trees were fit, there was no improvement in classification of risk over naïve methods for any approach. Although standard approaches identified summary features of physical activity data that were correlated with risk of VT, machine-learning approaches found that none of these features provided an improvement in classification. Future studies are needed to explore and validate methods for feature extraction and machine learning in classification of VT risk based on device-measured activity.
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