Thorey Valentin, Hernandez Albert Bou, Arnal Pierrick J, During Emmanuel H
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:1596-1600. doi: 10.1109/EMBC.2019.8856877.
Polysomnography (PSG) is the gold standard for diagnosing sleep obstructive apnea (OSA). It allows monitoring of breathing events throughout the night. The detection of these events is usually done by trained sleep experts. However, this task is tedious, highly time-consuming and subject to important inter-scorer variability. In this study, we adapted our state-of-the-art deep learning method for sleep event detection, DOSED, to the detection of sleep breathing events in PSG for the diagnosis of OSA. We used a dataset of 52 PSG recordings with apnea-hypopnea event scoring from 5 trained sleep experts. We assessed the performance of the automatic approach and compared it to the inter-scorer performance for both the diagnosis of OSA severity and, at the microscale, for the detection of single breathing events. We observed that human sleep experts reached an average accuracy of 75% while the automatic approach reached 81% for sleep apnea severity diagnosis. The F1 score for individual event detection was 0.55 for experts and 0.57 for the automatic approach, on average. These results demonstrate that the automatic approach can perform at a sleep expert level for the diagnosis of OSA.
多导睡眠图（PSG）是诊断睡眠呼吸暂停低通气综合征（OSA）的金标准。它可以监测整夜的呼吸事件。这些事件的检测通常由训练有素的睡眠专家完成。然而，这项任务繁琐、耗时且评分者间差异很大。在本研究中，我们将我们用于睡眠事件检测的先进深度学习方法DOSED应用于PSG中睡眠呼吸事件的检测，以诊断OSA。我们使用了一个包含52份PSG记录的数据集，这些记录由5位训练有素的睡眠专家进行了呼吸暂停低通气事件评分。我们评估了自动方法的性能，并将其与评分者间在OSA严重程度诊断以及微观层面单个呼吸事件检测方面的性能进行了比较。我们观察到，在睡眠呼吸暂停严重程度诊断方面，人类睡眠专家的平均准确率为75%，而自动方法达到了81%。在个体事件检测方面，专家的F1分数平均为0.55，自动方法为0.57。这些结果表明，自动方法在OSA诊断方面可以达到睡眠专家的水平。
