Maurice Abou Jaoude, Aravind Ravi, Jiansheng Niu, Hubert J. Banville, Nicolas Florez Torres, Christopher Aimone
{"title":"Automated Sleep Staging on Wearable EEG Enables Sleep Analysis at Scale","authors":"Maurice Abou Jaoude, Aravind Ravi, Jiansheng Niu, Hubert J. Banville, Nicolas Florez Torres, Christopher Aimone","doi":"10.1109/NER52421.2023.10123829","DOIUrl":null,"url":null,"abstract":"This study presents automated sleep staging on a large number of sleep electroencephalography (EEG) recordings collected using the Muse S headband. Two recent deep learning models; a single-channel Deep Sleep Net (DSN) and a multi-channel Muse Net (MNet) were evaluated on a 5-class sleep stage classification task on 200 expert-labelled overnight sleep EEG recordings. The learned representations of the models were visualized using uniform manifold approximation projection (UMAP). Moreover, a large scale analysis of the relationship between sleep stage distribution of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep with age was performed on 1020 unlabelled EEG recordings. The results showed that the proposed models achieved high accuracy (DSN: 85.2%, MNet: 86.3%) and Cohen's Kappa (DSN: 0.77, MNet: 0.79) indicating substantial agreement with human expert sleep scoring. Furthermore, the features learned by the deep neural networks showed a sleep continuum beyond the traditionally used sleep stages. Hypnogram analysis revealed a decrease in percentage of NREM 3 and REM sleep with increasing age, and an increase in percentage of NREM 2 sleep with increasing age. The results suggested that a 4-channel wearable EEG headband provides low-cost and powerful means to automatically score and analyze sleep at a large scale.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NER52421.2023.10123829","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This study presents automated sleep staging on a large number of sleep electroencephalography (EEG) recordings collected using the Muse S headband. Two recent deep learning models; a single-channel Deep Sleep Net (DSN) and a multi-channel Muse Net (MNet) were evaluated on a 5-class sleep stage classification task on 200 expert-labelled overnight sleep EEG recordings. The learned representations of the models were visualized using uniform manifold approximation projection (UMAP). Moreover, a large scale analysis of the relationship between sleep stage distribution of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep with age was performed on 1020 unlabelled EEG recordings. The results showed that the proposed models achieved high accuracy (DSN: 85.2%, MNet: 86.3%) and Cohen's Kappa (DSN: 0.77, MNet: 0.79) indicating substantial agreement with human expert sleep scoring. Furthermore, the features learned by the deep neural networks showed a sleep continuum beyond the traditionally used sleep stages. Hypnogram analysis revealed a decrease in percentage of NREM 3 and REM sleep with increasing age, and an increase in percentage of NREM 2 sleep with increasing age. The results suggested that a 4-channel wearable EEG headband provides low-cost and powerful means to automatically score and analyze sleep at a large scale.