Non-invasive polysomnography recording on dogs has been claimed to produce data comparable to those for humans regarding sleep macrostructure, EEG spectra and sleep spindles. While functional parallels have been described relating to both affective (e.g., emotion processing) and cognitive (e.g., memory consolidation) domains, methodologically relevant questions about the reliability of sleep stage scoring still need to be addressed. In Study 1, we analyzed the effects of different coders and different numbers of visible EEG channels on the visual scoring of the same polysomnography recordings. The lowest agreement was found between independent coders with different scoring experience using full (3 h-long) recordings of the whole dataset, and the highest agreement within-coder, using only a fraction of the original dataset (randomly selected 100 epochs (i.e., 100 × 20 s long segments)). The identification of drowsiness was found to be the least reliable, while that of non-REM (NREM) was the most reliable. Disagreements resulted in no or only moderate differences in macrostructural and spectral variables. Study 2 targeted the task of automated sleep EEG time series classification. Supervised machine learning (ML) models were used to help the manual annotation process by reliably predicting if the dog was sleeping or awake. Logistic regression models (LogREG), gradient boosted trees (GBT) and convolutional neural networks (CNN) were set up and trained for sleep state prediction from already collected and manually annotated EEG data. The evaluation of the individual models suggests that their combination results in the best performance: ~0.9 AUC test scores.

中文翻译：

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基于手动和自动睡眠阶段识别的家犬睡眠结构评分的可靠性。


据称，对狗进行的非侵入性多导睡眠图记录可以产生与人类在睡眠宏观结构、脑电图谱和睡眠纺锤波方面相当的数据。虽然已经描述了与情感（例如，情绪处理）和认知（例如，记忆巩固）领域相关的功能相似性，但仍然需要解决有关睡眠阶段评分可靠性的方法论相关问题。在研究 1 中，我们分析了不同编码器和不同数量的可见脑电图通道对相同多导睡眠图记录的视觉评分的影响。使用整个数据集的完整（3 小时长）记录，具有不同评分经验的独立编码员之间发现了最低的一致性，而仅使用原始数据集的一小部分（随机选择的 100 个时期（即， 100 × 20 s 长段））。睡意的识别最不可靠，而非快速眼动睡眠 (NREM) 的识别最可靠。分歧导致宏观结构和光谱变量没有或只有中等差异。研究 2 针对自动睡眠脑电图时间序列分类的任务。监督机器学习 (ML) 模型用于通过可靠地预测狗是在睡觉还是醒着来帮助手动注释过程。逻辑回归模型（LogREG）、梯度提升树（GBT）和卷积神经网络（CNN）被建立和训练，用于根据已收集和手动注释的脑电图数据进行睡眠状态预测。对各个模型的评估表明，它们的组合会产生最佳性能：~0.9 AUC 测试分数。