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Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

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Towards automatic EEG cyclic alternating pattern analysis: a systematic review.

Fábio Mendonça^1,2, Sheikh Shanawaz Mostafa², Fernando Morgado-Dias^1,2

¹University of Madeira, Funchal, Portugal.

Biomedical Engineering Letters

|July 31, 2023

Summary

This summary is machine-generated.

Automatic analysis of Cyclic Alternating Pattern (CAP) is feasible for clinical use. This systematic review found that machine and deep learning models show promise for reliable CAP and A-phase detection in sleep studies.

Keywords:

A phase Automatic classification CAP EEG

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Area of Science:

Sleep Medicine
Computational Neuroscience
Biomedical Engineering

Background:

Cyclic Alternating Pattern (CAP) is a key indicator of sleep quality and instability.
Manual CAP analysis is time-consuming and subjective.
Automating CAP analysis could improve diagnostic efficiency and consistency.

Purpose of the Study:

To systematically review the feasibility of automatic Cyclic Alternating Pattern (CAP) analysis for clinical application.
To identify trends in methodologies for automatic CAP analysis.
To assess the challenges and future directions in automated sleep analysis.

Main Methods:

Systematic literature review following PRISMA 2020 guidelines.
Inclusion of 35 studies from 1,280 identified articles on automatic CAP analysis.
Analysis of methods for A phase classification, subtype identification, and CAP cycle detection.

Main Results:

Observed evolution in A phase classification from mathematical models to machine and deep learning.
Prevalence of finite state machines for CAP cycle detection, dependent on A phase classifiers.
Challenges in A-phase subtype assessment due to diverse detection approaches.

Conclusions:

Automatic CAP analysis is viable and can be reliably performed for clinical applications.
Further validation on larger datasets with diverse sleep disorders is recommended.
Sharing source code is crucial for independent verification and advancement.