Application of Machine Learning to Sleep Stage Classification

11/04/2021
by   Andrew Smith, et al.
0

Sleep studies are imperative to recapitulate phenotypes associated with sleep loss and uncover mechanisms contributing to psychopathology. Most often, investigators manually classify the polysomnography into vigilance states, which is time-consuming, requires extensive training, and is prone to inter-scorer variability. While many works have successfully developed automated vigilance state classifiers based on multiple EEG channels, we aim to produce an automated and open-access classifier that can reliably predict vigilance state based on a single cortical electroencephalogram (EEG) from rodents to minimize the disadvantages that accompany tethering small animals via wires to computer programs. Approximately 427 hours of continuously monitored EEG, electromyogram (EMG), and activity were labeled by a domain expert out of 571 hours of total data. Here we evaluate the performance of various machine learning techniques on classifying 10-second epochs into one of three discrete classes: paradoxical, slow-wave, or wake. Our investigations include Decision Trees, Random Forests, Naive Bayes Classifiers, Logistic Regression Classifiers, and Artificial Neural Networks. These methodologies have achieved accuracies ranging from approximately 74 Most notably, the Random Forest and the ANN achieved remarkable accuracies of 95.78 machine learning classifiers to automatically, accurately, and reliably classify vigilance states based on a single EEG reading and a single EMG reading.

READ FULL TEXT
research
03/05/2019

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

Electroencephalogram (EEG) is a common base signal used to monitor brain...
research
01/03/2017

Automatic sleep monitoring using ear-EEG

The monitoring of sleep patterns without patient's inconvenience or invo...
research
05/18/2018

Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference

Electroencephalographic (EEG) monitoring of neural activity is widely us...
research
11/12/2018

Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD...
research
07/22/2019

A-Phase classification using convolutional neural networks

A series of short events, called A-phases, can be observed in the human ...
research
03/06/2022

Integration of Facial Thermography in EEG-based Classification of ASD

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affectin...
research
08/06/2023

Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models

Epilepsy is a prevalent neurological disorder characterized by recurrent...

Please sign up or login with your details

Forgot password? Click here to reset