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Sleep Apnea Detection by Means of Analyzing Electrocardiographic Signal

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Human-Computer Systems Interaction: Backgrounds and Applications 3

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 300))

Abstract

Obstructive sleep apnea (OSA) is a condition of cyclic, periodic obstruction (stenosis) of the upper respiratory tract. OSA could be associated with serious cardiovascular problems, such as hypertension, arrhythmias, hearth failure or peripheral vascular disease. Understanding the way of connection between OSA and cardiovascular diseases is important to choose proper treatment strategy. In this paper, we present a method for integrated measurements of biosignals for automatic OSA detection. The proposed method was implemented using a portable device with the application of the Support Vector Machine (SVM) classifier. The specific objective of this work is to analyze the minimum set of features for the ECG signal that could produce acceptable classification results. Those features can be further expanded using other biosignals, measured by the portable SleAp device. Additionally, the influence of the body movements and positions on measurement results with SleAp system are presented. The proposed system could help to determine the influence of OSA on the state of the cardiovascular system.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdańsk, Poland

    P. Przystup, A. Bujnowski, A. Poliński, J. Rumiński & J. Wtorek

Authors
  1. P. Przystup
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  2. A. Bujnowski
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  3. A. Poliński
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  4. J. Rumiński
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  5. J. Wtorek
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Corresponding author

Correspondence to P. Przystup .

Editor information

Editors and Affiliations

  1. Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, Rzeszów, Poland

    Zdzisław S. Hippe

  2. Polish Academy of Sciences, M. Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Poland

    Juliusz L. Kulikowski

  3. University of Information Technology and Management, Rzeszów, Poland

    Teresa Mroczek

  4. Gdańsk University of Technology, Gdańsk-Wrzeszcz, Poland

    Jerzy Wtorek

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Przystup, P., Bujnowski, A., Poliński, A., Rumiński, J., Wtorek, J. (2014). Sleep Apnea Detection by Means of Analyzing Electrocardiographic Signal. In: Hippe, Z., Kulikowski, J., Mroczek, T., Wtorek, J. (eds) Human-Computer Systems Interaction: Backgrounds and Applications 3. Advances in Intelligent Systems and Computing, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-319-08491-6_15

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  • DOI: https://doi.org/10.1007/978-3-319-08491-6_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08490-9

  • Online ISBN: 978-3-319-08491-6

  • eBook Packages: EngineeringEngineering (R0)

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