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Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
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EEG based classification of sleep cyclic alternating patterns using frequency driven forward ternary encoding.

Megha Agarwal1, Amit Singhal2

  • 1Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, India.

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Summary
This summary is machine-generated.

This study presents a novel method for classifying cyclic alternating patterns (CAP) in sleep EEG signals. The system accurately distinguishes between healthy individuals and those with insomnia, offering potential for real-time sleep analysis.

Keywords:
CAPEEGForward ternary encoding (FTE)Gaussian filtersHistogram

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Biomedical Engineering

Background:

  • Cyclic Alternating Patterns (CAP) in sleep EEG are crucial for understanding sleep abnormalities.
  • CAP consists of two phases (A and B) reflecting distinct brain responses to stimuli.

Purpose of the Study:

  • To develop an efficient and accurate system for segregating CAP phases in EEG signals.
  • To differentiate between sleep patterns of healthy individuals and those with insomnia.

Main Methods:

  • EEG signals segmented into sequences and processed with Gaussian filters for frequency band (FB) components.
  • Forward Ternary Encoding (FTE) applied to FB components, with histograms capturing signal patterns.
  • Feature vectors constructed from combined histograms; Kruskal-Wallis test for feature selection.

Main Results:

  • Evaluated four machine learning classifiers for CAP phase segregation.
  • Bagged Tree (BT) classifier achieved 80.16% accuracy for healthy and 81.12% for insomnia datasets.

Conclusions:

  • The proposed method surpasses existing studies in CAP classification accuracy.
  • The system is accurate, easy to implement, and suitable for real-time deployment in sleep analysis.