A Self-attention-based Ensemble Convolution Neural Network Approach for Sleep Stage Classification with Merged Spectrogram.
Chih-En KuoPo-Yu LiaoYu-Syuan LinPublished in: APSIPA ASC (2021)
Keyphrases
- sleep stage
- neural network
- pattern recognition
- learning vector quantization
- sleep apnea
- pattern analysis
- classification accuracy
- learning algorithm
- feature selection
- decision trees
- final classification
- feature vectors
- classification algorithm
- multi layer perceptron
- image classification
- feature extraction
- artificial neural networks
- machine learning algorithms
- image processing
- concept drifting data streams
- generalization ability
- ensemble learning
- ensemble classifier
- multiple classifiers
- majority voting
- binary classification problems
- machine learning
- training set
- individual classifiers
- classifier ensemble
- nearest neighbour
- support vector machine
- neural network model
- feature set
- support vector machine svm