Real-Time Automatic Apneic Event Detection Using Nocturnal Pulse Oximetry

Objective: Nocturnal pulse oximetry has been proposed as a simpler alternative to polysomnography in diagnosing sleep apnea. However, existing techniques are limited in terms of inability to provide time information on sleep apnea occurrence. This study aimed to propose a new strategy for near real-time automatic detection of apneic events and reliable estimation of apnea–hypopnea index using nocturnal pulse oximetry. Methods: Among 230 polysomnographic recordings with apnea–hypopnea index values ranging from 0 to 86.5 events/h, 138 (60%) and the remaining 92 recordings (40%) were categorized as training and test sets, respectively. By extracting the quantitative characteristics caused by the apneic event for the amount and duration of the change in blood oxygen saturation value, we established the criteria to determine the occurrence of apneic event. Regression modeling was used to estimate the apnea–hypopnea index from the apneic event detection results. Results: The minute-by-minute apneic segment detection exhibited an average accuracy of 91.0% and an average Cohen's kappa coefficient of 0.71. Between the apnea–hypopnea index estimations and reference values, the mean absolute error was 2.30 events/h. The average accuracy of our diagnosis of sleep apnea was 96.7% for apnea–hypopnea index cutoff values of ≥5, 10, 15, and 30 events/h. Conclusion: We developed an effective strategy to detect apneic events by using morphometric characteristics in the fluctuation of blood oxygen saturation values. Significance: Our study could be potentially useful in home-based multinight apneic event monitoring for purposes of therapeutic intervention and follow-up study on sleep apnea.