Journal of Medical Systems

, 43:36 | Cite as

An Intelligent Sleep Apnea Classification System Based on EEG Signals

  • V. VimalaEmail author
  • K. Ramar
  • M. Ettappan
Image & Signal Processing
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health


Sleep Apnea is a sleep disorder which causes stop in breathing for a short duration of time that happens to human beings and animals during sleep. Electroencephalogram (EEG) plays a vital role in detecting the sleep apnea by sensing and recording the brain’s activities. The EEG signal dataset is subjected to filtering by using Infinite Impulse Response Butterworth Band Pass Filter and Hilbert Huang Transform. After pre-processing, the filtered EEG signal is manipulated for sub-band separation and it is fissioned into five frequency bands such as Gamma, Beta, Alpha, Theta, and Delta. This work employs features such as energy, entropy, and variance which are computed for each frequency band obtained from the decomposed EEG signals. The selected features are imported for the classification process by using machine learning classifiers including Support Vector Machine (SVM) with Kernel Functions, K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). The performance measures such as accuracy, sensitivity, and specificity are computed and analyzed for each classifier and it is inferred that the Support Vector Machine based classification of sleep apnea produces promising results.


Classification of sleep apnea Electroencephalogram Hilbert Huang transform Infinite Impulse Response Butterworth Band pass filter K-Nearest Neighbors Support Vector Machine 


Compliance with Ethical Standards

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringChennai Institute of TechnologyChennaiIndia
  2. 2.Department of Computer Science and EngineeringEinstein College of EngineeringTirunelveliIndia
  3. 3.Department of Electrical and Electronics EngineeringChennai Institute of TechnologyChennaiIndia

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