Apnea Recognition with Wavelet Neural Networks

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 to the patients, 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 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.