目的:我们的目的是创建一种能够识别单导联心电图(ECG)信号中阻塞性睡眠呼吸暂停(OSA)模式的机器学习架构,在临床数据集中使用时表现出卓越的性能。
方法:我们使用由1656名患者组成的数据集进行了研究,代表不同的人口,来自中国医科大学附属医院睡眠中心。为了检测呼吸暂停ECG段并提取呼吸暂停特征,我们利用了EfficientNet和它的一些层,分别。此外,我们比较了各种训练和数据预处理技术,以增强模型的预测能力,例如设置类别和样本权重或采用重叠和规则切片。最后,我们针对呼吸暂停心电图数据库上的其他文献测试了我们的方法.
结果:我们的研究发现,EfficientNet模型使用重叠切片和样本权重设置实现了最佳的呼吸暂停节段检测,AUC为0.917,准确度为0.855。对于AHI>30的患者筛查,我们将训练模型与XGBoost相结合,导致0.975的AUC和0.928的准确性。使用PhysioNet数据的其他测试表明,我们的模型在筛选OSA水平的能力方面与现有模型的性能相当。
结论:我们建议的架构,加上训练和预处理技术,在不同的人口统计数据中表现出令人钦佩的表现,使我们更接近OSA诊断的实际实施。试验注册本研究的数据是在机构审查委员会CMUH109-REC3-018的批准下从台湾的中国医科大学医院回顾性收集的。
OBJECTIVE: Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data sets.
METHODS: We conducted our research using a data set consisting of 1656 patients, representing a diverse demographic, from the sleep center of
China Medical University Hospital. To detect apnea ECG segments and extract apnea features, we utilized the EfficientNet and some of its layers, respectively. Furthermore, we compared various training and data preprocessing techniques to enhance the model\'s prediction, such as setting class and sample weights or employing overlapping and regular slicing. Finally, we tested our approach against other literature on the Apnea-ECG database.
RESULTS: Our research found that the EfficientNet model achieved the best apnea segment detection using overlapping slicing and sample-weight settings, with an AUC of 0.917 and an accuracy of 0.855. For patient screening with AHI > 30, we combined the trained model with XGBoost, leading to an AUC of 0.975 and an accuracy of 0.928. Additional tests using PhysioNet data showed that our model is comparable in performance to existing models regarding its ability to screen OSA levels.
CONCLUSIONS: Our suggested architecture, coupled with training and preprocessing techniques, showed admirable performance with a diverse demographic dataset, bringing us closer to practical implementation in OSA diagnosis. Trial registration The data for this study were collected retrospectively from the
China Medical University Hospital in Taiwan with approval from the institutional review board CMUH109-REC3-018.