Sleep-Deep-Learner is taught sleep-wake scoring by the end-user to complete each record in their style.
Sleep-Deep-Learner is taught sleep-wake scoring by the end-user to complete each record in their style.
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睡眠深度学习者由最终用户教授睡眠觉醒评分，以按照他们的风格完成每条记录。
DOI：
10.1093/sleepadvances/zpae022
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Uygun,DavidS
中科院分区：
文献类型：
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作者：
Katsuki,Fumi;Spratt,TristanJ;Brown,RitchieE;Basheer,Radhika;Uygun,DavidS
睡眠-觉醒评分是临床和临床前睡眠研究的一个耗时、乏味但必不可少的组成部分。由于状态之间的脑电图振幅差异较小以及快速的状态转换（需要在较短的时期内进行评分），啮齿类动物的睡眠评分更加费力且更具挑战性。尽管存在许多自动啮齿动物睡眠评分方法，但它们在对新数据集进行评分时表现不佳，尤其是那些涉及脑电图/肌电图变化的数据集。因此，专家评分员的手动评分仍然是黄金标准。在这里，我们采用不同的方法来解决这个问题，即使用神经网络来加速专家评分者的评分。 Sleep-Deep-Learner 通过 GoogLeNet 的迁移学习，通过学习最终用户提供的每个 EEG/LFP 记录的一小部分手动分数，为个人脑电图或局部场电位 (LFP) 记录创建定制的深度卷积神经网络模型。然后，Sleep-Deep-Learner 会自动对 EEG/LFP 记录的其余部分进行评分。一种新颖的快速眼动睡眠评分校正程序进一步提高了准确性。与手动评分相比，Sleep-Deep-Learner 能够可靠地对脑电图和 LFP 数据进行评分，并在野生型小鼠、催眠唑吡坦诱导的睡眠中、阿尔茨海默病小鼠模型以及基因敲除研究中保留睡眠-觉醒结构。 Sleep-Deep-Learner 将手动评分时间减少至 1/12。由于 Sleep-Deep-Learner 在每个独立记录上使用迁移学习，因此它不会因先前评分的现有数据集而产生偏差。因此，我们发现 Sleep-Deep-Learner 在处理因药物、疾病模型或基因改造而改变的信号时表现良好。
Sleep–wake scoring is a time-consuming, tedious but essential component of clinical and preclinical sleep research. Sleep scoring is even more laborious and challenging in rodents due to the smaller EEG amplitude differences between states and the rapid state transitions which necessitate scoring in shorter epochs. Although many automated rodent sleep scoring methods exist, they do not perform as well when scoring new datasets, especially those which involve changes in the EEG/EMG profile. Thus, manual scoring by expert scorers remains the gold standard. Here we take a different approach to this problem by using a neural network to accelerate the scoring of expert scorers. Sleep-Deep-Learner creates a bespoke deep convolution neural network model for individual electroencephalographic or local-field-potential (LFP) records via transfer learning of GoogLeNet, by learning from a small subset of manual scores of each EEG/LFP record as provided by the end-user. Sleep-Deep-Learner then automates scoring of the remainder of the EEG/LFP record. A novel REM sleep scoring correction procedure further enhanced accuracy. Sleep-Deep-Learner reliably scores EEG and LFP data and retains sleep–wake architecture in wild-type mice, in sleep induced by the hypnotic zolpidem, in a mouse model of Alzheimer’s disease and in a genetic knock-down study, when compared to manual scoring. Sleep-Deep-Learner reduced manual scoring time to 1/12. Since Sleep-Deep-Learner uses transfer learning on each independent recording, it is not biased by previously scored existing datasets. Thus, we find Sleep-Deep-Learner performs well when used on signals altered by a drug, disease model, or genetic modification.