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Stages of Sleep

Stages of Sleep

Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
Before sleep begins, in wakefulness, the brain exhibits primarily beta waves, which are high in frequency and low in amplitude, indicating alertness...

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Related Experiment Video

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How to Obtain Reliable Visual Event-related Potentials in Newborns

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Published on: October 24, 2019

Neonatal EEG sleep stage classification based on deep learning and HMM.

Hojat Ghimatgar^1,2, Kamran Kazemi¹, Mohammad Sadegh Helfroush¹

¹Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran.

Journal of Neural Engineering

|May 27, 2020

Summary

This study presents a novel deep learning and hidden Markov model (HMM) approach for accurate automatic sleep stage scoring in neonates. The method enhances infant sleep architecture analysis and aids in diagnosing brain abnormalities.

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Area of Science:

Neuroscience
Computational Biology
Medical Informatics

Background:

Investigating sleep architecture in infants is crucial for understanding neurodevelopment.
Accurate sleep stage classification in neonates is challenging but essential for early diagnosis of brain abnormalities.

Purpose of the Study:

To introduce a novel multichannel deep learning and hidden Markov model (HMM) approach for improved automatic sleep stage classification in term neonates.
To enhance the accuracy of sleep stage scoring for studying infant sleep architecture.

Main Methods:

Utilized multichannel EEG data from 16 neonates (postmenstrual age 38-40 weeks).
Extracted linear and nonlinear features, reduced dimensionality using Modified Graph Clustering Ant Colony Optimization (MGCACO).
Employed a bi-directional long-short time memory (BiLSTM) network for classification and HMM for postprocessing.

Main Results:

Achieved mean kappa of 0.71-0.76 and overall accuracy of 78.9%-82.4% using K-fold cross-validation (KFCV) and leave-one-out cross-validation (LOOCV).
The method demonstrated robust performance with six bipolar EEG channels.

Conclusions:

The developed automatic sleep stage scoring method offers a valuable tool for studying neurodevelopmental processes in neonates.
This approach can aid in the early diagnosis of brain abnormalities in term infants.