Dependence of sleep apnea detection efficiency on the length of ECG recording

  • Agata PietrzakEmail author
  • Gerard Cybulski
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 393)


Our computer program allows the calculations of commonly accepted six heart rate variability (HRV) parameters in time domain. Those parameters, obtained from long-time one-channel ECG signal recordings, were used for detection of sleep apnea. The classification model was based on the Support Vector Machines (SVM) method using the discriminative Radial Basis Function (RBF) kernel. The aim of study was to check how the length of analyzed single channel ECG overnight recording influences on accuracy of sleep apnea detection.


Sleep apnea detection Support Vector Machines ECG respiratory disorders 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Mechatronics, Institute of Precision and Biomedical EngineeringWarsaw University of TechnologyWarsawPoland

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