Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
A practical Bayesian framework for backpropagation networks
Neural Computation
Machine Learning
Error reduction through learning multiple descriptions
Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
The evidence framework applied to classification networks
Neural Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Energy based feature extraction for classification of sleep apnea syndrome
Computers in Biology and Medicine
A new method for sleep apnea classification using wavelets and feedforward neural networks
Artificial Intelligence in Medicine
Signal selection for sleep apnea classification
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Hi-index | 12.05 |
This paper presents a novel approach for classifying sleep apneas into one of the three basic types: obstructive, central and mixed. The goal is to overcome the problems encountered in previous work and improve classification accuracy. The proposed model uses a new classification approach based on the characteristics that each type of apnea presents in different segments of the signal. The model is based on the error correcting output code and it is formed by a combination of artificial neural networks experts where their inputs are the coefficients obtained by a discrete wavelet decomposition applied to the raw samples of the apnea in the thoracic effort signal. The input coefficients received for each network were determined by a feature selection method (support vector machine recursive feature elimination). 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 10 different simulations and a multiple comparison procedure was used for model selection. The mean test accuracy obtained was 90.27%+/-0.79, and the values for each class apnea were 94.62% (obstructive), 95.47% (central) and 90.45% (mixed). Up to the authors' knowledge, the proposed classifier surpasses all previous results.