Publication: SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species
SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species
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Miladinović, Đ., Muheim, C., Bauer, S., Spinnler, A., Noain, D., Bandarabadi, M., Gallusser, B., Krummenacher, G., Baumann, C., Adamantidis, A., Brown, S. A., & Buhmann, J. M. (2019). SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species. PLoS Computational Biology, 15(4), e1006968. https://doi.org/10.1371/journal.pcbi.1006968
Abstract
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Abstract
Understanding sleep and its perturbation by environment, mutation, or medication remains a central problem in biomedical research. Its examination in animal models rests on brain state analysis via classification of electroencephalographic (EEG) signatures. Traditionally, these states are classified by trained human experts by visual inspection of raw EEG recordings, which is a laborious task prone to inter-individual variability. Recently, machine learning approaches have been developed to automate this process, but their generalizat
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- Miladinović, Đorđe
- Muheim, Christine
- Bauer, Stefan
- Spinnler, Andrea
- Noain, Daniela
- Bandarabadi, Mojtaba
- Gallusser, Benjamin
- Krummenacher, Gabriel
- Baumann, Christian
- Adamantidis, Antoine
- Brown, Steven A
- Buhmann, Joachim M
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Miladinović, Đ., Muheim, C., Bauer, S., Spinnler, A., Noain, D., Bandarabadi, M., Gallusser, B., Krummenacher, G., Baumann, C., Adamantidis, A., Brown, S. A., & Buhmann, J. M. (2019). SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species. PLoS Computational Biology, 15(4), e1006968. https://doi.org/10.1371/journal.pcbi.1006968
