Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome From Single-Channel Airflow

Abstract

Goal: The purpose of this study is to evaluate the usefulness of the boosting algorithm AdaBoost (AB) in the context of the sleep apnea-hypopnea syndrome (SAHS) diagnosis. Methods: We characterize SAHS in single-channel airflow (AF) signals from 317 subjects by the extraction of spectral and nonlinear features. Relevancy and redundancy analyses are conducted through the fast correlation-based filter to derive the optimum set of features among them. These are used to feed classifiers based on linear discriminant analysis (LDA) and classification and regression trees (CART). LDA and CART models are sequentially obtained through AB, which combines their performances to reach higher diagnostic ability than each of them separately. Results: Our AB-LDA and AB-CART approaches showed high diagnostic performance when determining SAHS and its severity. The assessment of different apnea-hypopnea index cutoffs using an independent test set derived into high accuracy: 86.5% (5 events/h), 86.5% (10 events/h), 81.0% (15 events/h), and 83.3% (30 events/h). These results widely outperformed those from logistic regression and a conventional event-detection algorithm applied to the same database. Conclusion: Our results suggest that AB applied to data from single-channel AF can be useful to determine SAHS and its severity. Significance: SAHS detection might be simplified through the only use of single-channel AF data.