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Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning

Manish Sharma1, Virendra Patel1, Jainendra Tiwari1

  • 1Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India.

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Summary

This study introduces an automated system to identify sleep instability using electroencephalogram (EEG) signals, aiding in the diagnosis of sleep disorders by analyzing cyclic alternating pattern (CAP) phases.

Keywords:
classificationcyclic alternating pattern (CAP)electroencephalogram (EEG)ensemble of bagged trees (EBagT)polysomnogram (PSG)sleepsleep macrostructuresleep microstructure

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Sleep Medicine

Background:

  • Sleep macrostructure analysis is insufficient for understanding sleep stability.
  • Cyclic Alternating Pattern (CAP) reflects sleep microstructure and instability.
  • Manual polysomnogram (PSG) analysis is time-consuming and burdensome.

Purpose of the Study:

  • To develop an automated system for identifying CAP phases A and B.
  • To analyze sleep microstructure for improved sleep disorder diagnosis.

Main Methods:

  • Utilized the CAP sleep database with single-channel EEG.
  • Applied wavelet decomposition, extracting entropy and Hjorth parameters.
  • Employed machine learning, specifically ensemble of bagged trees (EBagT), for classification.

Main Results:

  • Achieved high classification accuracy for discriminating CAP phases A and B across various sleep disorders and healthy subjects.
  • Reported average accuracies ranging from 72% to 84% for specific conditions.
  • Overall average accuracy of 78% when considering all subjects.

Conclusions:

  • The proposed automated system effectively analyzes sleep microstructure via CAP detection.
  • This system offers a more efficient and patient-friendly alternative to manual PSG analysis.
  • Potential to assist sleep specialists in diagnosing sleep disorders.