Learning translation invariant recognition in massively parallel networks
Volume I: Parallel architectures on PARLE: Parallel Architectures and Languages Europe
Multilayer feedforward networks are universal approximators
Neural Networks
A practical Bayesian framework for backpropagation networks
Neural Computation
Treatment of obstructive sleep apnea syndrome by monitoring patients airflow signals
Pattern Recognition Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
An Auto-learning System for the Classification of Fetal Heart Rate Decelerative Patterns
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
The evidence framework applied to classification networks
Neural Computation
Kolmogorov's theorem is relevant
Neural Computation
Energy based feature extraction for classification of sleep apnea syndrome
Computers in Biology and Medicine
Classıfıcation of sleep apnea by using wavelet transform and artificial neural networks
Expert Systems with Applications: An International Journal
Classifying sleep apneas using neural networks and a combination of experts
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Algorithms for the analysis of polysomnographic recordings with customizable criteria
Expert Systems with Applications: An International Journal
A mixture of experts for classifying sleep apneas
Expert Systems with Applications: An International Journal
Artificial Apnea Classification with Quantitative Sleep EEG Synchronization
Journal of Medical Systems
Computers in Biology and Medicine
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Objectives:: This paper presents a novel approach for sleep 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.