{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Kam K"],"funding":["NIA NIH HHS","NHLBI NIH HHS","Merck &amp; Co Inc","NIH","Alzheimer&apos;s Association"],"pagination":["109224"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8457529"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["360"],"pubmed_abstract":["<h4>Background</h4>Recent advancement in deep learning provides a pivotal opportunity to potentially supplement or supplant the limiting step of manual sleep scoring.<h4>New method</h4>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.<h4>Results</h4>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"],"journal":["Journal of neuroscience methods"],"pubmed_title":["WaveSleepNet: An interpretable deep convolutional neural network for the continuous classification of mouse sleep and wake."],"pmcid":["PMC8457529"],"funding_grant_id":["R01 AG066870","K25 HL151912","K24 HL109156","R21 AG059179","2018-AARG-589632","L30 AG057120","R01 AG056682"],"pubmed_authors":["Ayappa I","Parekh A","Varga AW","Kam K","Rapoport DM"],"additional_accession":[]},"is_claimable":false,"name":"WaveSleepNet: An interpretable deep convolutional neural network for the continuous classification of mouse sleep and wake.","description":"<h4>Background</h4>Recent advancement in deep learning provides a pivotal opportunity to potentially supplement or supplant the limiting step of manual sleep scoring.<h4>New method</h4>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.<h4>Results</h4>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","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Aug","modification":"2025-04-04T09:37:35.559Z","creation":"2025-04-04T09:37:35.559Z"},"accession":"S-EPMC8457529","cross_references":{"pubmed":["34052291"],"doi":["10.1016/j.jneumeth.2021.109224"]}}