Identifying Obstructive, Central and Mixed Apnea Syndrome Using Discrete Wavelet Transform

  • Ch. Usha KumariEmail author
  • G. Mounika
  • S. Jeevan Prasad
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)


This paper presents feature extraction of Electroencephalogram (EEG) signal and identifying the Obstructive Sleep Syndrome (OSS), Central Sleep Syndrome (CSS) and Mixed Sleep Syndrome (MSS) using Daubechies order 2 wavelet transform. Wavelet transform is the powerful tool for feature extraction and classification. The EEG signal is decomposed into sub-bands and features are extracted. Based on the features the EEG signal is correlated with subjects abdomen movements, nasal air flow and ribcage movements. Then OSS, CSS and MSS are identified. The frequency of EEG signals goes high to low when event occurs. The signal amplitude of abdomen movements, nasal air flow and ribcage movements reduces and reaches zero level when event occurs. Recognizing the thresholds of all the artifacts leading to OSS, CSS, and MSS reduces the diagnosis time and saves life.


Obstructive Sleep Syndrome (OSS) Central Sleep Syndrome (CSS) Mixed Sleep Syndrome (MSS) 



The authors wish to thank the publicly available physio bank database


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of ECEGRIETHyderabadIndia

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