Machine learning for prediction of sleep stage
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Hello,
I have two sets of physiologic recordings. One is a scored sleep study and the other is recordings from the brain. I have the sleep study findings loaded in to Matlab so that I can index using it, and the recordings from the brain analyzed using pwelch and plotted with spectrogram. When I overlay the two plots, there is an increase in the frequency band 15-30Hz seen in the brain recordings whenever the patient is in an "awake" period.
I have another 9 sets of brain recordings from other patients, but their respective sleep studies have yet to be analyzed. While I wait for that to happen, I want to predict what that output will look like solely from the brain recordings.
I believe I should be able to take the first half of the brain recordings from the patient with a scored sleep study, train a machine learning algorithm on this, and then test it on the second half. The data sets are very large. Fs = 1024, brain recording = [33000000, 1].
Any advice on how to proceed is much appreciated, happy to provide more information regarding state of the data as well. Thank you
6 comentarios
Star Strider
el 29 de Abr. de 2016
You, too!
I wouldn’t eliminate the 'outliers'. Include them, and let your spectrogram (STFT) sort them, unless you know they are sampling artefacts. (These can occur in some instances, usually in recording pulse stimuli or from environmental contamination, but rarely in the physiological signals themselves.) You have to take a closer look at the signal to determine that (although the spike on the right in the left plot could qualify if you know that what generated it was not the EEG). Otherwise, determining what are EEG signals and what are artefacts isn’t possible from the resolution of the image you attached. It would also help to have units (sec, µV) on the axes.
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