Electroencephalography (EEG) is a method to record the electrical signals in the brain. Recognizing the EEG patterns in the sleeping brain gives insights into the understanding of sleeping disorders. The dataset under consideration contains EEG data points associated with various physiological conditions. This study attempts to generalize the detection of particular patterns associated with the Non-Rapid Eye Movement (NREM) sleep cycle of the brain using a machine learning model. The proposed model uses additional feature engineering to incorporate sequential information for training a classifier to predict the occurrence of Cyclic Alternating Pattern (CAP) sequences in the sleep cycle, which are often associated with sleep disorders.
@article{arxiv.1804.08750,
title = {A machine learning model for identifying cyclic alternating patterns in the sleeping brain},
author = {Aditya Chindhade and Abhijeet Alshi and Aakash Bhatia and Kedar Dabhadkar and Pranav Sivadas Menon},
journal= {arXiv preprint arXiv:1804.08750},
year = {2018}
}
Comments
Presented at HackAuton, Auton Lab, Carnegie Mellon University. Problem credits: Philips