Apnea Recognition with Wavelet Neural Networks


  • Cristina Zaniol Universidade Federal do Rio Grande do Sul - UFRGS
  • Maria Cristina Varriale Universidade Federal do Rio Grande do Sul - UFRGS
  • Evandro Manica Universidade Federal do Rio Grande do Sul - UFRGS




Neural Network, Sleep Disorder Syndrome, Apnea


Apnea is a Sleep Disorder Syndrome characterized by an interruption or reduction of air flow for at least 10 seconds. Polysomnography is a test used to apnea diagnosis. Several signals, including Electrocardiogram (ECG), Electroencephalogram (EEG) and Oxygen Saturation (SpO_2) are obtained in this diagnostic test. Since most tests for apnea are uncomfortable, there is an increase search for alternative methods to reduce cost and improve patient well-being.
In this work, we use only SpO_2 data from 25 patients of the St Vincent's University Hospital, Dublin, to extract parameters connected to a Neural Network attempting to classify patients with apnea or non-apnea. Results confirm that our alternative method can be used as an auxiliary tool for diagnosis by using exclusively SpO_2 signal.


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How to Cite

Zaniol, C., Varriale, M. C., & Manica, E. (2018). Apnea Recognition with Wavelet Neural Networks. Trends in Computational and Applied Mathematics, 19(2), 277. https://doi.org/10.5540/tema.2018.019.02.277



Original Article