Real-time, automatic, open-source sleep stage classification system using single EEG for mice
Abstract We developed a real-time sleep stage classification system with a convolutional neural network using only a one-channel electro-encephalogram source from mice and universally available features in any time-series data: raw signal, spectrum, and zeitgeber time. To accommodate historical info...
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Nature Portfolio
2021
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oai:doaj.org-article:32e217a2d3eb4aa5aa44105bcfdee0582021-12-02T15:00:51ZReal-time, automatic, open-source sleep stage classification system using single EEG for mice10.1038/s41598-021-90332-12045-2322https://doaj.org/article/32e217a2d3eb4aa5aa44105bcfdee0582021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90332-1https://doaj.org/toc/2045-2322Abstract We developed a real-time sleep stage classification system with a convolutional neural network using only a one-channel electro-encephalogram source from mice and universally available features in any time-series data: raw signal, spectrum, and zeitgeber time. To accommodate historical information from each subject, we included a long short-term memory recurrent neural network in combination with the universal features. The resulting system (UTSN-L) achieved 90% overall accuracy and 81% multi-class Matthews Correlation Coefficient, with particularly high-quality judgements for rapid eye movement sleep (91% sensitivity and 98% specificity). This system can enable automatic real-time interventions during rapid eye movement sleep, which has been difficult due to its relatively low abundance and short duration. Further, it eliminates the need for ordinal pre-calibration, electromyogram recording, and manual classification and thus is scalable. The code is open-source with a graphical user interface and closed feedback loop capability, making it easily adaptable to a wide variety of end-user needs. By allowing large-scale, automatic, and real-time sleep stage-specific interventions, this system can aid further investigations of the functions of sleep and the development of new therapeutic strategies for sleep-related disorders.Taro TezukaDeependra KumarSima SinghIyo KoyanagiToshie NaoiMasanori SakaguchiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021) |
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Medicine R Science Q |
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Medicine R Science Q Taro Tezuka Deependra Kumar Sima Singh Iyo Koyanagi Toshie Naoi Masanori Sakaguchi Real-time, automatic, open-source sleep stage classification system using single EEG for mice |
| description |
Abstract We developed a real-time sleep stage classification system with a convolutional neural network using only a one-channel electro-encephalogram source from mice and universally available features in any time-series data: raw signal, spectrum, and zeitgeber time. To accommodate historical information from each subject, we included a long short-term memory recurrent neural network in combination with the universal features. The resulting system (UTSN-L) achieved 90% overall accuracy and 81% multi-class Matthews Correlation Coefficient, with particularly high-quality judgements for rapid eye movement sleep (91% sensitivity and 98% specificity). This system can enable automatic real-time interventions during rapid eye movement sleep, which has been difficult due to its relatively low abundance and short duration. Further, it eliminates the need for ordinal pre-calibration, electromyogram recording, and manual classification and thus is scalable. The code is open-source with a graphical user interface and closed feedback loop capability, making it easily adaptable to a wide variety of end-user needs. By allowing large-scale, automatic, and real-time sleep stage-specific interventions, this system can aid further investigations of the functions of sleep and the development of new therapeutic strategies for sleep-related disorders. |
| format |
article |
| author |
Taro Tezuka Deependra Kumar Sima Singh Iyo Koyanagi Toshie Naoi Masanori Sakaguchi |
| author_facet |
Taro Tezuka Deependra Kumar Sima Singh Iyo Koyanagi Toshie Naoi Masanori Sakaguchi |
| author_sort |
Taro Tezuka |
| title |
Real-time, automatic, open-source sleep stage classification system using single EEG for mice |
| title_short |
Real-time, automatic, open-source sleep stage classification system using single EEG for mice |
| title_full |
Real-time, automatic, open-source sleep stage classification system using single EEG for mice |
| title_fullStr |
Real-time, automatic, open-source sleep stage classification system using single EEG for mice |
| title_full_unstemmed |
Real-time, automatic, open-source sleep stage classification system using single EEG for mice |
| title_sort |
real-time, automatic, open-source sleep stage classification system using single eeg for mice |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/32e217a2d3eb4aa5aa44105bcfdee058 |
| work_keys_str_mv |
AT tarotezuka realtimeautomaticopensourcesleepstageclassificationsystemusingsingleeegformice AT deependrakumar realtimeautomaticopensourcesleepstageclassificationsystemusingsingleeegformice AT simasingh realtimeautomaticopensourcesleepstageclassificationsystemusingsingleeegformice AT iyokoyanagi realtimeautomaticopensourcesleepstageclassificationsystemusingsingleeegformice AT toshienaoi realtimeautomaticopensourcesleepstageclassificationsystemusingsingleeegformice AT masanorisakaguchi realtimeautomaticopensourcesleepstageclassificationsystemusingsingleeegformice |
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