Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals
Journal of Medical Systems
Estimating vigilance level by using EEG and EMG signals
Neural Computing and Applications
A new method for sleep apnea classification using wavelets and feedforward neural networks
Artificial Intelligence in Medicine
Energy based feature extraction for classification of sleep apnea syndrome
Computers in Biology and Medicine
Artificial Apnea Classification with Quantitative Sleep EEG Synchronization
Journal of Medical Systems
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Detection and classification of sleep apnea syndrome (SAS) is a critical problem. In this study an efficient method for classification sleep apnea through sub-band energy of abdominal effort using a particularly designed hybrid classifier as Wavelets + Neural Network is proposed. The Abdominal respiration signals were separated into spectral sub-band energy components with multi-resolution Discrete Wavelet Transform (DWT). The energy content of these spectral components was applied to the input of the artificial neural network (ANN). The ANN was configured to give three outputs dedicated to SAS cases; obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). Through the network, satisfactory results that rewarding 85.62% mean accuracy in classifying SAS were obtained.