PDF(Pubmed)
Abstract：
This study introduces a deep-learning-based automatic sleep scoring system to detect sleep apnea using a single-lead electrocardiography (ECG) signal, focusing on accurately estimating the apnea-hypopnea index (AHI). Unlike other research, this work emphasizes AHI estimation, crucial for the diagnosis and severity evaluation of sleep apnea. The suggested model, trained on 1465 ECG recordings, combines the deep-shallow fusion network for sleep apnea detection network (DSF-SANet) and gated recurrent units (GRUs) to analyze ECG signals at 1-min intervals, capturing sleep-related respiratory disturbances. Achieving a 0.87 correlation coefficient with actual AHI values, an accuracy of 0.82, an F1 score of 0.71, and an area under the receiver operating characteristic curve of 0.88 for per-segment classification, our model was effective in identifying sleep-breathing events and estimating the AHI, offering a promising tool for medical professionals.
摘要：
这项研究引入了一种基于深度学习的自动睡眠评分系统，用于使用单导联心电图（ECG）信号检测睡眠呼吸暂停，重点是准确估计呼吸暂停低通气指数（AHI）。与其他研究不同,这项工作强调AHI估计，对于睡眠呼吸暂停的诊断和严重程度评估至关重要。建议的模型，接受过1465次心电图记录的训练,结合睡眠呼吸暂停检测网络的深浅融合网络（DSF-SANet）和门控复发单元（GRU），以1分钟的间隔分析ECG信号，捕获睡眠相关的呼吸障碍。与实际AHI值实现0.87的相关系数，每段分类的准确度为0.82，F1评分为0.71，接受者工作特征曲线下面积为0.88，我们的模型在识别睡眠呼吸事件和估计AHI方面是有效的，为医疗专业人员提供了一个有前途的工具。
