Apnea Recognition with Wavelet Neural Networks

Cristina Zaniol, Maria Cristina Varriale, Evandro Manica

Abstract


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.

Keywords


Neural Network; Sleep Disorder Syndrome; Apnea

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References


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DOI: https://doi.org/10.5540/tema.2018.019.02.277

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TEMA - Trends in Applied and Computational Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)
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