An Early Warning Algorithm to Predict Obstructive Sleep Apnea (OSA) Episodes

Abstract

Sleep apnea is a common respiratory disorder during sleep. It is characterized by shallow or no breathing during sleep for at least 10 seconds. Decrease in sleep quality may effect the next day daily routine unfavorably. In some cases apnea period (not breathing interval) can last more than 30 seconds causing fatal outcomes. 14% of men and 5% of women suffer from Obstructive Sleep Apnea (OSA) in United States. Patients may face apnea for more than 300 times in a single overnight sleep. Polysomnography (PSG) is a multi-parametric recording of biophysiological changes, having Snorring, SpO2, Nasal Airflow EEG, EMG, ECG signals, performed in sleep study laboratories. In this study, a fully automatic apnea detection algorithm is mentinoed and an early warning system is proposed to predict OSA episodes by extracting time-series features of pre-OSA periods and regular respiration using nasal airflow signal. Extracted features are then reduced by RANSAC and entropy based approaches to improve the performance of prediction algorithm. Support vector machines (SVM), one of the commonly used classification algorithms in medical applications, k-Nearest Neighbor and a modified Linear Regression are implemented for learning and classification of nasal airflow signal episodes. The results show that OSA episodes are predicted with 86.9% of accuracy and 91.5% of sensitivity, 30 seconds before patient faces apnea. By the use of predicting an apnea episode before happening, it is possible to prevent patient to face apnea by early warning which can minimize the possible health risks.