WaveSleepNet: An interpretable deep convolutional neural network for the continuous classification of mouse sleep and wake.

ABSTRACT:

Background

Recent advancement in deep learning provides a pivotal opportunity to potentially supplement or supplant the limiting step of manual sleep scoring.

New method

In this paper, we characterize the WaveSleepNet (WSN), a deep convolutional neural network (CNN) that uses wavelet transformed images of mouse EEG/EMG signals to autoscore sleep and wake.

Results

WSN achieves an epoch by epoch mean accuracy of 0.86 and mean F1 score of 0.82 compared to manual scoring by a human expert. In mice experiencing mechanically induced sleep fragmentation, an overall epoch by epoch mean accuracy of 0.80 is achieved by WSN and classification of non-REM (NREM) sleep is not compromised, but the high level of sleep fragmentation results in WSN having greater difficulty differentia

SUBMITTER: Kam K 

PROVIDER: S-EPMC8457529 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature