Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification.
Payongkit LakhanApiwat DitthapronNannapas BanluesombatkulTheerawit WilaiprasitpornPublished in: TENCON (2018)
Keyphrases
- artificial neural networks
- sleep stage
- neural network
- multi layer perceptron
- apnea hypopnea
- sleep apnea
- classification accuracy
- feature vectors
- pattern recognition
- feature set
- genetic algorithm
- classification method
- feature extraction
- feature space
- multilayer perceptron
- classification models
- eeg signals
- benchmark datasets
- pattern classification problems
- pattern classification
- classification process
- feature analysis
- extracted features
- feature representation
- class labels
- gender classification
- high dimensionality
- activation function
- svm classifier
- feature maps
- k nearest neighbour
- irrelevant features
- learning vector quantization
- feature selection
- image features
- decision trees
- feature selection algorithms
- training data
- image classification
- supervised learning
- fuzzy logic
- radial basis function neural network
- false positives
- machine learning algorithms
- support vector machine svm
- text classification
- feature extraction and classification
- knn
- obstructive sleep apnea
- machine learning