misleep 0.2.13

MiSleep: Mice Sleep EEG/EMG visualization, scoring and analysis.

MiSleep

MiSleep is for EEG/EMG signal processing and visualization

The name 'MiSleep' is from 'Mice Sleep' and sounds like 'my sleep'.

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Get start

pip install misleep

Find the directory where you installed misleep, run

python -m misleep

If you use the miniconda or anaconda, the path will be like D:/miniconda3/envs/misleep/Lib/site-packages.

See https://bryanwang.cn/MiSleep/ for a simple documentation.

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Some features

1. Free self-define data structure

You can organize your data with matlab structure like this:

data.EEG = AN_ARRAY_OF_EEG_DATA;
data.EMG_DIFF = AN_ARRAY_OF_EMG_DIFFERENTIAL_DATA;
% Channel name must be the same with you defined above
data.channels = {'EEG' 'EMG_DIFF'};
% Sampling frequency for each channel of data
data.sf = {256 256};
% Acquisition time of your data
data.time = {'20240409-18:00:00'}; 

Or if your data format is .edf, misleep will also support well.

2. Event Detection

For sleep spindle and sleep slow-wave activities detection, you can check the tools menu for event detection. The auto stage will coming soon.

3. Self-define config.ini

There is a config.ini in the root directory of MiSleep source package, multiple parameters can be self define there, check config.ini for detail.

4. Auto stage

Finally, now we have the auto stage function! Check it in the tool. The overall test accuracy of NREM and Wake is higher than 90%, and for REM, the accuracy is higher than 80%. Currently the model's result is rather fragmented in the state transition segments, we will add some constraints to fix this.

Future: Open for suggestions :).

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Cite this work

If you use this software, please cite it as below. Xueqiang Wang. (2024). BryanWang0702/MiSleep. Zenodo. https://doi.org/10.5281/zenodo.14511905