RT info:eu-repo/semantics/article
T1 Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome From Single-Channel Airflow
A1 Gutiérrez Tobal, Gonzalo César
A1 Álvarez González, Daniel
A1 Campo Matias, Félix del
A1 Hornero Sánchez, Roberto
K1 AdaBoost
AB 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 non-linear features. Relevancy and redundancy analyses are conducted through the fast correlation-based filter (FCBF) 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.
PB Institute of Electrical and Electronics Engineers (IEEE)
SN 0018-9294
YR 2016
FD 2016
LK http://uvadoc.uva.es/handle/10324/21679
UL http://uvadoc.uva.es/handle/10324/21679
LA eng
NO IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 63, NO. 3, MARCH 2016
NO Producción Científica
DS UVaDOC
RD 28-nov-2024