gist-ailab/IITNet-official
This is an official implementation for "Intra- and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG".
This helps researchers and clinicians automatically categorize sleep stages using raw single-channel EEG data. It takes standardized EEG recordings (like .edf files) as input and outputs a classification of sleep stages, such as Wake, N1, N2, N3, and REM. Researchers studying sleep patterns, neurologists, or sleep lab technicians who need to analyze large volumes of EEG data would find this useful.
No commits in the last 6 months.
Use this if you need to quickly and consistently score sleep stages from single-channel EEG recordings, particularly if you're working with large datasets like MASS, SHHS, or Sleep-EDF.
Not ideal if you're working with multi-channel EEG data or need a tool for real-time sleep monitoring outside of a research or clinical analysis setting.
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Language
Python
License
MIT
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Last pushed
Mar 24, 2022
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