Paper Info
Title
Apnea-Hypopnea Index Estimation From Spectral Analysis Of Airflow Recordings
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 (MLRfull) and the second one consisting of features from a new frequency band of interest (MLRband). 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 (rho). MLRband reached the highest rho (0.809). Four different AHI decision thresholds were used to evaluate MLRband 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.
Year
DOI
Venue
2012
10.1109/EMBC.2012.6346706
2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Keywords
Field
DocType
electroencephalography,regression analysis,sleep
Frequency band,Regression analysis,Computer science,Correlation,Airflow,Spectral analysis,Apnea–hypopnea index,Statistics,Linear regression
Conference
Volume
ISSN
Citations 
2012
1557-170X
1
PageRank 
References 
Authors
0.40
2
6
Name
Order
Citations
PageRank
Gonzalo C. Gutiérrez-Tobal13110.29
Hornero, R.2627.33
Daniel Alvarez3131.84
J. Victor Marcos4282.97
Carlos Gómez58615.72
Del Campo Félix611.07