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Sleep Apnea Diagnosis Using Complexity Features of EEG Signals

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Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications (IWINAC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13258))

Abstract

Sleep apnea syndrome is one the most prevalent sleep disorders. The accurate diagnosis and treatment of apnea by physicians can help to avoid its destructive effects in the long term. Electroencephalogram (EEG) records activity of the brain from different areas of scalp and can be an appropriate method to diagnose sleep apnea. In this work, we proposed a Computer Aided Diagnosis System (CADS) for sleep apnea based on complexity features of EEG. At first, EEG time series of 20 participants were decomposed into six frequency bands (delta, theta, alpha, sigma, beta, and gamma) by using bandpass Finite Impulse Response (FIR) filters. Then, complexity features such as fractals, Lempel-Ziv Complexity (LZC), entropies, and generalized Hurst exponent that was used for the first time to detect sleep apnea from EEG signals, were extracted from each frequency band. The minimum-redundancy maximum-relevance (mRMR) algorithm was applied to sort 120 features of three EEG channels. Finally, two popular classifiers, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), were used to detect sleep apnea. \( 99.33\%\) accuracy was obtained using the SVM classifier and generalized hurst exponent had an effective contribution to detect apnea.

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Author information

Authors and Affiliations

  1. Faculty of Science, Department of Physical Chemistry, K. N. Toosi University of Technology, Tehran, Iran

    Behnam Gholami

  2. School of Behavioral and Brain Science, University of Texas, Texas, USA

    Mohammad Hossein Behboudi

  3. Faculty of Electrical Engineering, Department of Biomedical Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Ali Khadem

  4. Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran

    Afshin Shoeibi

  5. Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain

    Juan M. Gorriz

Authors
  1. Behnam Gholami
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  2. Mohammad Hossein Behboudi
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  3. Ali Khadem
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  4. Afshin Shoeibi
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  5. Juan M. Gorriz
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Corresponding author

Correspondence to Afshin Shoeibi .

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Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Universidad Nacional de Educación a Distancia, Madrid, Spain

    Félix de la Paz López

  4. Ohio State University, Columbus, OH, USA

    Hojjat Adeli

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Gholami, B., Behboudi, M.H., Khadem, A., Shoeibi, A., Gorriz, J.M. (2022). Sleep Apnea Diagnosis Using Complexity Features of EEG Signals. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-06242-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06241-4

  • Online ISBN: 978-3-031-06242-1

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