Apnea-Hypopnea Index Prediction Using Electrocardiogram Acquired During the Sleep-Onset Period.

Abstract

The most widely used methods for predicting obstructive sleep apnea are based on clinical or anatomico-functional features. To improve exactitude in obstructive sleep apnea screening, this study aimed to devise a new predictor of apnea-hypopnea index. We hypothesized that less irregular respiration cycles would be observed in the patients with more severe obstructive sleep apnea during the sleep-onset period. From each of the 156 and 70 single-lead electrocardiograms collected from the internal polysomnographic database and from the Physionet Apnea-ECG database, respectively, the 150-s sleep-onset period was determined and the respiration cycles during this period were detected. Using the coefficient of variation of the respiration cycles, obtained from the internal dataset, as a predictor, the apnea-hypopnea index predictive model was developed through regression analyses and k-fold cross-validations. The apnea-hypopnea index predictability of the regression model was tested with the Physionet Apnea-ECG database. The regression model trained and validated from the 143 and 13 data, respectively, produced an absolute error (mean ± SD) of 3.65 ±2.98 events/h and a Pearson's correlation coefficient of 0.97 (P < 0.01) between the apnea-hypopnea index predictive values and the reference values for the 70 test data. The new predictor of apnea-hypopnea index has the potential to be utilized in making more reasoned clinical decisions on the need for formal diagnosis and treatment of obstructive sleep apnea. Our study is the first study that presented the strategy for providing a reliable apnea-hypopnea index without overnight recording.