Validation of Deep Learning-based Sleep State Classification.
Validation of Deep Learning-based Sleep State Classification.
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DOI：
10.17912/micropub.biology.000643
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发表时间：
2022
影响因子：
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通讯作者：
Landsness, Eric C
中科院分区：
文献类型：
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作者：
Chen, Wei;Zhang, Xiaohui;Miao, Hanyang;Tang, Michelle J;Anastasio, Mark;Culver, Joseph;Lee, Jin-Moo;Landsness, Eric C
深度学习方法已经被开发用于对小鼠脑电(EEG)和肌电(EMG)记录的睡眠状态进行分类，据报道准确率高达97%。然而，当应用于具有各种实验和记录条件的独立数据集时，睡眠状态分类的准确率往往会由于分布偏移而下降。混合z评分是EEG/EMG信号的一种预处理标准化，已被建议用来解释这些变化。这项研究试图在一个独立的数据集上验证混合z评分和深度学习方法的结合。通过卷积神经网络实现混合z评分与深度学习相结合的开源软件Accusept被用来对12个三小时的EEG/EMG记录进行睡眠状态分类，这些记录来自于睡在头部固定位置的小鼠。混合z评分和深度学习在两个独立的录音上对睡眠状态进行分类，准确率为85-92%，科恩的κ为0.66-0.71。这些结果验证了混合z评分与深度学习相结合对睡眠状态进行分类具有广泛应用的潜力。
Deep learning methods have been developed to classify sleep states of mouse electroencephalogram (EEG) and electromyogram (EMG) recordings with accuracy reported as high as 97%. However, when applied to independent datasets, with a variety of experimental and recording conditions, sleep state classification accuracy often drops due to distributional shift. Mixture z-scoring, a pre-processing standardization of EEG/EMG signals, has been suggested to account for these variations. This study sought to validate mixture z-scoring in combination with a deep learning method on an independent dataset. The open-source software Accusleep, which implements mixture z-scoring in combination with deep learning via a convolutional neural network, was used to classify sleep states in 12, three-hour EEG/EMG recordings from mice sleeping in a head-fixed position. Mixture z-scoring with deep learning classified sleep states on two independent recordings with 85-92% accuracy and a Cohen’s κ of 0.66-0.71. These results validate mixture z-scoring in combination with deep learning to classify sleep states with the potential for widespread use.