北京生物医学工程

基于梯度提升树的ECG-SAS自动识别方法

Automatic identification of ECG-SAS based on gradient boosting decision treealgorithm

作者：王伟梁杰牛洋洋刘洪涛

单位：深圳和而泰家居在线网络科技有限公司(广东深圳 ;518063) ; 深圳和而泰智能控制股份有限公司(广东深圳 ;518057)

分类号：R318.04

出版年·卷·期（页码）：2019·38·6（617-622）

摘要：

目的睡眠呼吸暂停综合征(sleep apnea syndrome, SAS)是威胁生命健康的多发病之一，目前判断SAS的方法大多采用多导睡眠图(polysomnography, PSG)，但其操作难度大、专业性高，不能有效推广，因此，设计一种自动检测SAS的方法显得尤为迫切和重要。方法本文设计了一种基于梯度提升树(gradient boosting decision tree, GBDT)的算法方案，首先通过信号处理方法提取心电图（electrocardiogram，ECG）数据的心率变异性(heart rate variability, HRV)特征，然后结合上下文相关性策略处理HRV数据训练模型。在得到模型后，采用动态阈值策略微调预测结果。最后统计每小时内的SAS发生次数，得到呼吸暂停低通气指数(apnea–hypopnea index, AHI)，完成SAS病情预测。结果本文使用Apnea-ECG数据库的ECG数据验证该算法效果。结果显示，采用本文方案，35个测试样本的SAS单分钟识别率为88.56%，按照AHI指标，将样本分为健康、轻度、中度、重度4类，其准确率为91.43%。结论本文所述基于GBDT的SAS-ECG识别方案，可以有效检测SAS事件，评估个体的SAS病情。

Objective Sleep apnea syndrome (SAS) is one of the most common life threatening diseases. At present, the method of judging SAS is polysomnography (PSG), with difficult and professional operation. This make it cannot be effectively popularized. Therefore, it is urgent and important to design an automatic SAS detection method. Methods In this paper, we designed a kind of ascension based on gradient boosting decision tree (GBDT) algorithm. In the scheme design, heart rate variability characteristics of electrocardiogram data were firstly extracted by signal processing method. Then we trained the model by using the HRV data which was processed according to the context correlation strategy, and used dynamic threshold strategy to fine-tune the prediction results. Finally, we counted the number of SAS case per hour, obtained the apnea-hypopnea index, and completed the SAS prediction. Results This paper used ECG data from Apnea-ECG database to verify the effectiveness of the algorithm. The SAS single-minute recognition rate of the 35 test samples was 88.56%, and the accuracy was 91.43% when samples were divided into four categories: healthy, mild, moderate and severe. Conclusions The ECG-SAS recognition scheme based on GBDT described in this paper can effectively detect SAS events and evaluate individual SAS conditions.

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