Abstract A common symptom of obstructive sleep apnea (OSA) is excessive daytime sleepiness (EDS). The gold standard test for EDS is the multiple sleep latency test (MSLT). However, due to its high cost, MSLT is not routinely conducted for OSA patients and EDS is instead evaluated using sleep questionnaires. This is problematic however, since sleep questionnaires are subjective and correlate poorly with the MSLT. Therefore, new objective tools are needed for reliable evaluation of EDS. The aim of this study was to test our hypothesis that EDS can be estimated with neural network analysis of previous night polysomnographic signals. We trained a convolutional neural network (CNN) classifier using electroencephalography, electrooculography, and chin electromyography signals from 2,014 patients with suspected OSA. The CNN was trained to classify the patients into four sleepiness categories based on their mean sleep latency (MSL); severe (MSL < 5min), moderate (5 ≤ MSL < 10), mild (10 ≤ MSL < 15), and normal (MSL ≥ 15). The CNN classified patients to the four sleepiness categories with an overall accuracy of 60.6% and Cohen’s kappa value of 0.464. In two-group classification scheme with sleepy (MSL < 10 min) and non-sleepy (MSL ≥ 10) patients, the CNN achieved an accuracy of 77.2%, with sensitivity of 76.5%, and specificity of 77.9%. Our results show that previous night’s polysomnographic signals can be used for objective estimation of EDS with at least moderate accuracy. Since the diagnosis of OSA is currently confirmed by polysomnography, the classifier could be used simultaneously to get an objective estimate of the daytime sleepiness with minimal extra workload.

中文翻译：

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使用卷积神经网络通过前一天晚上的脑电图、眼电图和肌电图来估计疑似睡眠呼吸暂停患者的白天困倦程度


摘要 阻塞性睡眠呼吸暂停（OSA）的常见症状是白天过度嗜睡（EDS）。 EDS 的黄金标准测试是多次睡眠潜伏期测试 (MSLT)。然而，由于成本较高，MSLT 并不常规对 OSA 患者进行，而是使用睡眠问卷来评估 EDS。然而，这是有问题的，因为睡眠调查问卷是主观的，并且与 MSLT 的相关性很差。因此，需要新的客观工具来可靠地评估 EDS。本研究的目的是检验我们的假设，即可以通过前晚多导睡眠图信号的神经网络分析来估计 EDS。我们使用来自 2,014 名疑似 OSA 患者的脑电图、眼电图和下巴肌电图信号训练了卷积神经网络 (CNN) 分类器。 CNN 经过训练，根据患者的平均睡眠潜伏期 (MSL) 将患者分为四个嗜睡类别；严重（MSL < 5 分钟）、中度（5 ≤ MSL < 10）、轻度（10 ≤ MSL < 15）和正常（MSL ≥ 15）。 CNN 将患者分为四种嗜睡类别，总体准确率为 60.6%，Cohen 的 kappa 值为 0.464。在困倦（MSL < 10 分钟）和非困倦（MSL ≥ 10）患者的两组分类方案中，CNN 的准确度为 77.2%，敏感性为 76.5%，特异性为 77.9%。我们的结果表明，前一天晚上的多导睡眠图信号可用于客观估计 EDS，至少具有中等准确度。由于 OSA 的诊断目前是通过多导睡眠图来确认的，因此可以同时使用分类器，以最小的额外工作量来客观估计白天的嗜睡程度。