Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice
Abstract We explore an innovative approach to sleep stage analysis by incorporating complexity features into sleep scoring methods for mice. Traditional sleep scoring relies on the power spectral features of electroencephalogram (EEG) and the electromyogram (EMG) amplitude. We introduced a novel met...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-74008-0 |
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| author | Naoki Furutani Yuki C. Saito Yasutaka Niwa Yu Katsuyama Yuta Nariya Mitsuru Kikuchi Tetsuya Takahashi Takeshi Sakurai |
| author_facet | Naoki Furutani Yuki C. Saito Yasutaka Niwa Yu Katsuyama Yuta Nariya Mitsuru Kikuchi Tetsuya Takahashi Takeshi Sakurai |
| author_sort | Naoki Furutani |
| collection | DOAJ |
| description | Abstract We explore an innovative approach to sleep stage analysis by incorporating complexity features into sleep scoring methods for mice. Traditional sleep scoring relies on the power spectral features of electroencephalogram (EEG) and the electromyogram (EMG) amplitude. We introduced a novel methodology for sleep stage classification based on two types of complexity analysis, namely multiscale entropy and detrended fluctuation analysis. Our analysis revealed significant variances in these complexities, not only within the specific theta and delta bands but across a wide frequency spectrum. Based on these findings, we developed a sleep stage scoring model, termed Sleep Analyzer Complex (SAC), a convolutional neural network model that integrates these complexity features with conventional EEG spectrum and EMG amplitude analysis. This integrated model significantly enhances the accuracy of sleep stage identification, achieving an accuracy of 97.4–98.1% for novel wild-type mice, on par with the agreement level among human scorers (97.3–97.8%). The efficacy of SAC was validated through tests conducted on wild-type mice, and it demonstrated remarkable success in identifying sleep architecture abnormalities in narcoleptic mice as well. This approach not only facilitates automated scoring of sleep/wakefulness states but also holds the potential to uncover detailed physiological insights, thereby advancing EEG-based sleep research. |
| format | Article |
| id | doaj-art-35781c9a48144bfca7d1330084989e0f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-35781c9a48144bfca7d1330084989e0f2025-01-26T12:28:46ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-024-74008-0Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in miceNaoki Furutani0Yuki C. Saito1Yasutaka Niwa2Yu Katsuyama3Yuta Nariya4Mitsuru Kikuchi5Tetsuya Takahashi6Takeshi Sakurai7Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa UniversityInternational Institute for Integrative Sleep Medicine (WPI-IIIS), University of TsukubaInternational Institute for Integrative Sleep Medicine (WPI-IIIS), University of TsukubaInternational Institute for Integrative Sleep Medicine (WPI-IIIS), University of TsukubaKameda Medical CenterDepartment of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa UniversityResearch Center for Child Mental Development, Kanazawa UniversityInternational Institute for Integrative Sleep Medicine (WPI-IIIS), University of TsukubaAbstract We explore an innovative approach to sleep stage analysis by incorporating complexity features into sleep scoring methods for mice. Traditional sleep scoring relies on the power spectral features of electroencephalogram (EEG) and the electromyogram (EMG) amplitude. We introduced a novel methodology for sleep stage classification based on two types of complexity analysis, namely multiscale entropy and detrended fluctuation analysis. Our analysis revealed significant variances in these complexities, not only within the specific theta and delta bands but across a wide frequency spectrum. Based on these findings, we developed a sleep stage scoring model, termed Sleep Analyzer Complex (SAC), a convolutional neural network model that integrates these complexity features with conventional EEG spectrum and EMG amplitude analysis. This integrated model significantly enhances the accuracy of sleep stage identification, achieving an accuracy of 97.4–98.1% for novel wild-type mice, on par with the agreement level among human scorers (97.3–97.8%). The efficacy of SAC was validated through tests conducted on wild-type mice, and it demonstrated remarkable success in identifying sleep architecture abnormalities in narcoleptic mice as well. This approach not only facilitates automated scoring of sleep/wakefulness states but also holds the potential to uncover detailed physiological insights, thereby advancing EEG-based sleep research.https://doi.org/10.1038/s41598-024-74008-0 |
| spellingShingle | Naoki Furutani Yuki C. Saito Yasutaka Niwa Yu Katsuyama Yuta Nariya Mitsuru Kikuchi Tetsuya Takahashi Takeshi Sakurai Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice Scientific Reports |
| title | Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice |
| title_full | Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice |
| title_fullStr | Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice |
| title_full_unstemmed | Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice |
| title_short | Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice |
| title_sort | utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep wake states in mice |
| url | https://doi.org/10.1038/s41598-024-74008-0 |
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