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New Automated Detection Method of OSA Based on Artificial Neural Networks Using P-Wave Shape and Time Changes

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Abstract

This paper describes a new method for automatic detection of obstructive sleep apnea (OSA) based on artificial neural networks (ANN) using regular electrocardiogram (ECG) recordings. ECG signals were pre-processed and segmented to extract the P-waves; then three P-wave features were extracted: the P-wave duration (T p ), the P-wave dispersion (P d ), and the time interval from the peak of the P-wave to the R-wave (T pr ). Combinations of the three features were used as features for classification using ANN. For each feature combination studied, 70% of the input data was used for training the ANN, 15% for validating, and 15% for testing the results. Perfect agreement between expert’s scores and the ANN scores was achieved when the ANN was applied on T p , P d , and T pr taken together, while substantial agreements were achieved when applying the ANN on the feature combinations T p and P d , and T p and T pr .

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Acknowledgement

This work was supported by the German Research Foundation-DFG (Deutsche Forschungsgemeinschaft).

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Authors and Affiliations

  1. Faculty of Engineering, Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, 22110, Jordan

    Khaldon Lweesy & Luay Fraiwan

  2. Computer Engineering Department, Jordan University of Science and Technology, Irbid, Jordan

    Natheer Khasawneh

  3. Department of Medical Informatics, University of Heidelberg, Heidelberg, Germany

    Hartmut Dickhaus

Authors
  1. Khaldon Lweesy
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  2. Luay Fraiwan
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  3. Natheer Khasawneh
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  4. Hartmut Dickhaus
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Correspondence to Khaldon Lweesy.

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Lweesy, K., Fraiwan, L., Khasawneh, N. et al. New Automated Detection Method of OSA Based on Artificial Neural Networks Using P-Wave Shape and Time Changes. J Med Syst 35, 723–734 (2011). https://doi.org/10.1007/s10916-009-9409-z

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  • DOI: https://doi.org/10.1007/s10916-009-9409-z

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