I have been working on a personal project to extract features from seizure EEG recordings that I thought I would share, with the goal to use this data to build a novel seizure detection model I have in mind,
The dataset can be found on Kaggle: Feature Extract – Siena Scalp + CHB MIT EEG Files
The features were extracted from publicly available EEG files in these two databases:
– Siena Scalp: https://physionet.org/content/siena-scalp-eeg/1.0.0/
– CHB MIT: https://physionet.org/content/chbmit/1.0.0/
I have tried to include as much as possible on how the features were calculated in the dataset description, but in general, the features were extracted based on these categories:
- Differential Entropy
- Sample, Permutation, and Approximate Entropy
- PSD Features
- Seizure Propagation Speeds
- Wavelet
- Time Domain
- Connectivity
- Phase-Amplitude Coupling (PAC)
- Rhythmic
A word of caution, however, is that I have not been able to have these calculations reviewed or verified by another human but I hope to have someone review it soon. It therefore should only be taken with a grain of salt at the moment but hope it is still useful in some way. I have been also going through the data to see if I can essentially prove what has already been proven, which is how I have been iteratively testing and verifying the data up to this point.
submitted by /u/bonesclarke84
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