Open AccessDetecting Excessive Daytime Sleepiness With CNN And Commercial Grade EEG
Author(s) -
I Putu Agus Eka Darma Udayana,
Made Sudarma,
Ni Wayan Sri Ariyani
Publication year - 2021
Publication title -
lontar komputer/lontar komputer
Language(s) - English
Resource type - Journals
eISSN - 2541-5832
pISSN - 2088-1541
DOI - 10.24843/lkjiti.2021.v12.i03.p06
Subject(s) - epworth sleepiness scale , electroencephalography , computer science , artificial intelligence , psychology , polysomnography , psychiatry
Epworth sleepiness scale is a self-assessment method in sleep medicine that has been proven to be a good predictor of obstructive sleep apnea. However, the over-reliance of the method making the process not socially distancing friendly enough in response to a global covid-19 pandemic. A study states that the Epworth sleepiness scale is correlated with the brainwave signal that commercial-grade EEG can capture. This study tried to train a classifier powered by CNN and deep learning that could perform as well as the Epworth with the objectiveness of brainwave signal. We test the classifier using the 20 university student using the Epworth sleepiness test beforehand. Then, we put the participant in 10 minutes EEG session, downsampling the data for normalization purposes and trying to predict the outcome of the ESS in respect of their brainwave state. The AI predict the reaching 65% of accuracy and 81% of sensitivity with just under 100.000 dataset which is excellent considering small dataset although this still have plenty room for improvement.