Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques.
José Luis Rodríguez-SoteloAlejandro Osorio-ForeroAlejandro Jiménez-RodríguezDavid Cuesta-FrauEva M. Cirugeda-RoldánDiego Hernán Peluffo-OrdóñezPublished in: Entropy (2014)
Keyphrases
- pattern analysis
- eeg signals
- pattern recognition
- sleep stage
- feature extraction
- pattern classification
- feature vectors
- classification accuracy
- image analysis
- feature set
- features extraction
- classification method
- classification process
- computational intelligence
- unsupervised learning
- benchmark datasets
- signal processing
- svm classifier
- feature values
- supervised learning
- image features
- feature space
- data analysis
- unsupervised feature selection
- spectral features
- neural network
- artificial intelligence
- extracted features
- extracting features
- physiological signals
- class labels
- eeg data
- supervised training
- brain activity
- feature analysis
- obstructive sleep apnea
- supervised classification
- feature selection algorithms
- information theory
- false positives
- support vector machine svm
- semi supervised
- artificial neural networks
- training data
- feature selection
- learning algorithm
- machine learning