Adam Tugdual, Tanty Jérôme, Barateau Lucie, Dauvilliers Yves
Institute of Neurosciences of Montpellier (INM), University of Montpellier, INSERM, Montpellier, France.
Sleep-Wake Disorders Unit, Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier, Montpellier, France.
J Sleep Res. 2025 Feb 20:e70007. doi: 10.1111/jsr.70007.
Actigraphy, a tool known for investigating sleep-wake patterns at home, lacks scientific validation in hypersomnolent subjects. We aim to validate an actigraphy-based sleep-wake prediction algorithm against 32-h continuous polysomnography in patients with suspected idiopathic hypersomnia, and to compare its performance to predict sleep-wake parameters assessed by polysomnography with those of a commercially available algorithm. Two hundred and six hypersomnolent subjects were included prospectively in a Reference Centre for Hypersomnias, and underwent a 32-h bedrest protocol, wearing wrist-actigraphy, to diagnose idiopathic hypersomnia. Among them, 126 patients (91 females, 30.6 ± 15.5 years, 101 idiopathic hypersomnia, 25 non-specified hypersomnia) with synchronised actigraphy and polysomnography were analysed. Age, sex, and Epworth Sleepiness Scale scores were collected. We trained various supervised algorithms and selected a recurrent neural network (S2S sequence-to-sequence long short-term memory network) for comparison with Actiwatch Software (AS) on sleep-wake variables and prediction errors during daytime and nighttime. S2S outperformed AS across all relevant metrics, and Bland-Altman analysis showed disagreement between the two algorithms. S2S had a lower absolute error than AS. AS mainly overestimated sleep, an overestimation that was substantially reduced with S2S, overall as well as during day and night. Performance was not correlated with age, sex, or subjective sleepiness, but objective sleepiness and longer sleep time on the bedrest were associated with sleep underestimation. Our S2S algorithm using deep learning performed better to predict sleep-wake parameters than AS and other commonly used algorithms. The next objective is to leverage this algorithm to study sleep-wake patterns in patients with hypersomnolence at home.
活动记录仪是一种用于在家中研究睡眠-觉醒模式的工具，但在患有过度嗜睡症的受试者中缺乏科学验证。我们旨在针对疑似特发性过度嗜睡症患者，通过32小时连续多导睡眠图来验证基于活动记录仪的睡眠-觉醒预测算法，并将其预测多导睡眠图评估的睡眠-觉醒参数的性能与一种商用算法的性能进行比较。206名过度嗜睡症受试者被前瞻性纳入一家过度嗜睡症参考中心，并接受了32小时卧床休息方案，期间佩戴腕部活动记录仪，以诊断特发性过度嗜睡症。其中，对126名活动记录仪和多导睡眠图同步的患者（91名女性，年龄30.6±15.5岁，101例特发性过度嗜睡症，25例未明确的过度嗜睡症）进行了分析。收集了年龄、性别和爱泼华嗜睡量表评分。我们训练了各种监督算法，并选择了一个递归神经网络（S2S序列到序列长短期记忆网络），以便在白天和夜间的睡眠-觉醒变量和预测误差方面与活动记录仪软件（AS）进行比较。在所有相关指标上，S2S的表现均优于AS，Bland-Altman分析显示这两种算法存在差异。S2S的绝对误差低于AS。AS主要高估了睡眠时间，而S2S在总体上以及白天和夜间都大幅减少了这种高估。性能与年龄、性别或主观嗜睡程度无关，但客观嗜睡程度和卧床休息时较长的睡眠时间与睡眠低估有关。我们使用深度学习的S2S算法在预测睡眠-觉醒参数方面比AS和其他常用算法表现更好。下一个目标是利用该算法在家中研究过度嗜睡症患者的睡眠-觉醒模式。
