My query is regarding identification of interical and preictal stages in CHB-MIT Scalp EEG Database of epileptic seizures collected at Children’s Hospital Boston , in which Seizure intervals are given in annotations. Generally most of the literature use only two classes of seizures in this database, viz. SEIZURE and NON-SEIZURE. My question is about identification of INTER-ICTAL, PREICTAL and ICTAL classes, so that appropriate Machine Learning/Deep Learning algorithm can be adopted for prediction of such seizure classes. Thanks in advance .
Dear Mr.Singh,
predicting from scalp data a seizure coming, even 30 seconds in advance, would be great! To my Knowledge until now, all efforts in this direction have been less successful, like forecasting an earthquake from the gravitational time-series data.
If you got marked the start and the end of the clinical seizures on your time series data, you can extract from the time period inbetween repetitive hypersynchronic EEG-pattern of 0.2 to 1 sec Duration (Spikes, sharp waves etc.). These Patterns differ from Patient to Patient, seldom in the same Patient. You could Group and classify them and use them to detect interictal shorter periods of hypersynchronized activity with the same pattern, but NO clinical seizure.
But beware of the following pitfall: If the ictal EEG activity on the record is burried under EMG activity (because of the ictal movements of the Patient), you will get an EMG-pattern and not an EEG-pattern, so every cough of the Patient would give a false Alarm of your classifier. So you have to control for this (additional pre-classifier?)
BUT this interictal hypersynchronized activity is a bad predictor for a real forecast, just a hint for the enhanced probability (even worse: you might find the same pattern in family members of the patients, who will never get a seizure - These can be genetic markers)
So, efforts for pre-ictal EEG have concentrated more on subtle changes in the "normal" EEG inbetween in the last seconds before the start of a seizure - changes in frequency content of the Signal, in synchronity between the different traces of the EEG = the different parts of the brain convexitiy or inbetween a trace. You might use wavelet analysis, chaos Analysis, source Analysis.
There should be a big amount of literature about that.
You will have to extend this analysis to a time period of 30, 60 then even more seconds before the start of the seizure to challenge the forecast Problem with your deep learning Approach.
Good luck!
Yours,
Ulrich Jobst
Respected Dr. Ulrich Jobst sir, I am really grateful for enlightening me with your knowledge in this field. Now I got some new facts from it, about which I was not aware of earlier, which will definitely help me in my work. Thank you so much sir for sharing your expertise.
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