A machine learning model for identifying cyclic alternating patterns in the sleeping brain

TL;DR

This paper presents a machine learning approach that uses feature engineering to detect Cyclic Alternating Pattern sequences in EEG data, aiding the understanding of sleep disorders.

Contribution

It introduces a novel machine learning model with feature engineering for identifying CAP sequences in EEG data during sleep.

Findings

Effective detection of CAP sequences in EEG data

Enhanced understanding of sleep disorder markers

Potential for improved sleep disorder diagnosis

Abstract

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.