RT info:eu-repo/semantics/article
T1 Linear and nonlinear analysis of airflow recordings to help in sleep apnoea–hypopnoea syndrome diagnosis
A1 Gutiérrez Tobal, Gonzalo César
A1 Hornero Sánchez, Roberto
A1 Álvarez, Daniel
A1 Marcos, José Víctor
A1 Campo Matias, Félix del
AB This paper focuses on the analysis of single-channel airflow (AF) signal to help in sleep apnoea–hypopnoea syndrome (SAHS) diagnosis. The respiratory rate variability (RRV) series is derived from AF by measuring time between consecutive breathings. A set of statistical, spectral and nonlinear features are extracted from both signals. Then, the forward stepwise logistic regression (FSLR) procedure is used in order to perform feature selection and classification. Three logistic regression (LR) models are obtained by applying FSLR to features from AF, RRV and both signals simultaneously. The diagnostic performance of single features and LR models is assessed and compared in terms of sensitivity, specificity, accuracy and area under the receiver-operating characteristics curve (AROC). The highest accuracy (82.43%) and AROC (0.903) are reached by the LR model derived from the combination of AF and RRV features. This result suggests that AF and RRV provide useful information to detect SAHS.
PB IOP
SN 0967-3334
YR 2012
FD 2012
LK https://uvadoc.uva.es/handle/10324/65879
UL https://uvadoc.uva.es/handle/10324/65879
LA eng
NO Physiological Measurement, Julio, 2012, vol. 33, n 7, pp. 1261-1275
NO Producción Científica
DS UVaDOC
RD 27-dic-2024