A new method for steep apnea classification using wavelets and feedforward neural networks

被引:62
作者
Fontenla-Romero, O [1 ]
Guijarro-Berdiñas, B [1 ]
Alonso- Betanzos, A [1 ]
Moret-Bonillo, V [1 ]
机构
[1] Univ A Coruna, Dept Comp Sci, Fac Informat, La Coruna 15071, Spain
关键词
sleep apnea syndrome; detection and classification of apneas; supervised neural networks; discrete wavelet transformation;
D O I
10.1016/j.artmed.2004.07.014
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Objectives: This paper presents a novel approach for steep apnea classification. The goal is to classify each apnea in one of three basic types: obstructive, central and mixed. Materials and methods: Three different supervised Learning methods using a neural network were tested. The inputs of the neural network are the first level-5-detail coefficients obtained from a discrete wavelet transformation of the samples (previously detected as apnea) in the thoracic effort signal. In order to train and test the systems, 120 events from six different patients were used. The true error rate was estimated using a 10-fold cross validation. The results presented in this work were averaged over 100 different simulations and a multiple comparison procedure was used for model selection. Results: The method finally selected is based on a feedforward neural network trained using the Bayesian framework and a cross-entropy error function. The mean classification accuracy, obtained over the test set was 83.78 +/- 1.90%. Conclusion: The proposed classifier surpasses, up to the author's knowledge, other previous results. Finally, a scheme to maintain and improve this system during its clinical use is also proposed. (c) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码:65 / 76
页数:12
相关论文
共 33 条
[1]  
[Anonymous], 1992, 10 LECT WAVELETS
[2]  
Bishop C. M., 1996, Neural networks for pattern recognition
[3]  
Bridle J. S., 1990, Neurocomputing, Algorithms, Architectures and Applications. Proceedings of the NATO Advanced Research Workshop, P227
[4]   Intelligent diagnosis of sleep apnea syndrome [J].
Cabrero-Canosa, M ;
Hernandez-Pereira, E ;
Moret-Bonillo, V .
IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, 2004, 23 (02) :72-81
[5]   An intelligent system for the detection and interpretation of sleep apneas [J].
Cabrero-Canosa, M ;
Castro-Pereiro, M ;
Graña-Ramos, M ;
Hernandez-Pereira, E ;
Moret-Bonillo, V ;
Martin-Egaña, M ;
Verea-Hernando, H .
EXPERT SYSTEMS WITH APPLICATIONS, 2003, 24 (04) :335-349
[6]  
CLABIAN M, 1997, P EANN, P171
[7]  
CLABIAN M, 1996, P EANN, P601
[8]   Cadosa: A fuzzy expert system for differential diagnosis of obstructive sleep apnoea and related conditions [J].
Daniels, JE ;
Cayton, RM ;
Chappell, MJ ;
Tjahjadi, T .
EXPERT SYSTEMS WITH APPLICATIONS, 1997, 12 (02) :163-177
[9]  
Fleiss J. L., 1981, Statistical Methods for Rates and Proportions, V2nd
[10]   Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research [J].
Flemons, WW ;
Buysse, D ;
Redline, S ;
Pack, A ;
Strohl, K ;
Wheatley, J ;
Young, T ;
Douglas, N ;
Levy, P ;
McNicholas, W ;
Fleetham, J ;
White, D ;
Schmidt-Nowarra, W ;
Carley, D ;
Romaniuk, J .
SLEEP, 1999, 22 (05) :667-689