A Time-Series Approach to Predict Obstructive Sleep Apnea (OSA) Episodes

Abstract

– Sleep apnea is a common respiratory disorder during sleep. It is characterized by pauses in breathing or shallow breathing during sleep for longer than 10 seconds. Except the fact that not having a proper sleep and being rested for the next day, in some cases the apnea period (not breathing interval) may last more than 30 seconds and this situation can even be fatal. 14% of men and 5% of women suffer from Obstructive Sleep Apnea (OSA) in United States. Patients may experience apnea for more than 300 times in a single night sleep. Polysomnography (PSG) is a multi-parametric recording of biophysiological changes, containing EEG, ECG, SpO2, Nasal Airflow signals, performed during overnight sleep. In this study, a fully automatic apnea detection algorithm is developed and an early warning system is proposed to predict OSA episodes by extracting time-series features of OSA periods and regular respiration using nasal airflow signal. Extracted features are then reduced to improve the performance of the prediction. Support vector machines (SVM), one of the commonly used classification algorithms in medical applications, is implemented for learning and prediction of the OSA episodes. The results show that OSA episodes are predicted with 87.6% of accuracy and 91.3% of sensitivity, 30 seconds before patient faces apnea. By this approach, apnea related health risks can be minimized by foreknowledge.