Automated Prediction of Apnea and Hypopnea, Using a LAMSTAR Artificial Neural Network

Rationale: The prediction of individual episodes of apnea and hypopnea in people with obstructive sleep apnea syndrome has not been thoroughly investigated. Accurate prediction of these events could improve clinical management of this prevalent disease. Objectives: To evaluate the performance of a system developed to predict episodes of obstructive apnea and hypopnea in individuals with obstructive sleep apnea; to determine the most important signals for making accurate and reliable predictions. Methods: We employed LArge Memory STorage And Retrieval (LAM STAR) artificial neural networks to predict apnea and hypopnea. Wavelet transform based preprocessing was applied to six physiological signals obtained from a set of polysomnography studies and used to train and test the networks. Measurements and Main Results: We tested prediction performance during non-REM and REM sleep as a function of data segment duration and prediction lead time. Measurements included average sensitivities, specificities, positive predictive values, and negative predictive values. Prediction performed best during non-REM sleep, using 30-second segments to predict events up to 30 seconds into the future. Most events were correctly predicted up to 60 seconds in the future. Apnea prediction achieved a sensitivity and specificity up to 80.6 +/- 5.6 and 72.8 +/- 6.6%, respectively. Hypopnea prediction achieved a sensitivity and specificity up to 74.4 +/- 5.9 and 68.8 +/- 7.0%., respectively. Conclusions: We report, to our knowledge, the first system to predict individual episodes of apnea and hypopnea. The most important signal for apnea prediction was submental electromyography. The most important signals for hypopnea prediction were submental electromyography and heart rate variability. This prediction system may facilitate improved therapies for obstructive sleep apnea.