NDLI: Apnea-hypopnea index estimation from spectral analysis of airflow recordings

Content Provider	IEEE Xplore Digital Library
Author	Gutierrez-Tobal, G.C. Hornero, R. Alvarez, D. Marcos, J.V. Gomez, C. del Campo, F.
Copyright Year	2012
Description	Author affiliation: Sleep Unit, Hosp. Univ. Rio Hortega in Valladolid, Valladolid, Spain (del Campo, F.) \|\| Biomed. Eng. Group, Univ. of Valladolid, Valladolid, Spain (Gutierrez-Tobal, G.C.; Hornero, R.; Alvarez, D.; Marcos, J.V.; Gomez, C.)
Abstract	This study focuses on the analysis of airflow (AF) recordings to help in sleep apnea-hypopnea syndrome (SAHS) diagnosis. The objective is to estimate the apnea-hypopnea index (AHI) by means of spectral features from AF data. Multiple linear regression (MLR) was used for this purpose. A training group is used to obtain two MLR models: the first one consisting of features obtained from the full PSDs $(MLR_{full})$ and the second one consisting of features from a new frequency band of interest $(MLR_{band}).$ Then a test group is used to validate the final model. The correlation of spectral features and MLR models with AHI was compared by means of Pearson's coefficient (ρ). $MLR_{band}$ reached the highest ρ (0.809). Four different AHI decision thresholds were used to evaluate $MLR_{band}$ ability to distinguish the severity of SAHS. The accuracy achieved was higher as the threshold increased (69.7%, 75.3%, 80.9%, 87.6%) These results suggest that the automated estimation of AHI through spectral features can provide useful knowledge about SAHS severity.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	3444
Ending Page	3447
File Size	1230179
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6346706
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sleep apnea Feature extraction Estimation Accuracy Training Correlation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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