Study on the prediction of cardiovascular disease based on sleep heart rate variability analysis

清晨醒来时刻前后是心血管疾病（CVD）事件发生的高峰期，这一点很可能与夜间睡眠结束时交感神经活动的激增有关。本文以 70 位在两年随访期内发生了 CVD 事件和 70 位未发生 CVD 事件的 140 位受试者为研究对象，提出了一种两层模型方法以探究觉醒前的心率变异性（HRV）特征是否有利于两类受试者的区分。在该方法中，第一层采用极端梯度提升算法（XGBoost）构建分类器，通过评估该分类器的特征重要性实现特征筛选；筛选出来的特征作为第二层模型的输入来构建最终的分类器。在第二层模型中，比较了 XGBoost、随机森林和支持向量机三种机器学习算法，以确定基于何种算法建立的模型可以得到最优的分类效果。研究结果显示，基于觉醒前 HRV 特征构建的 XGBoost+XGBoost 模型性能最优，准确率高达 84.3%；在所使用的 HRV 特征中，非线性动力学指标在模型中的重要性优于传统的时域、频域分析指标；其中尺度 1 下的排列熵、尺度 3 下的样本熵较为重要。本研究结果对 CVD 的预防、诊断以及 CVD 风险评估系统的设计有着积极的参考价值。.; The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn't during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.