A novel solution of enhanced loss function using deep learning in sleep stage classification: Predict and diagnose patients with sleep disorders

Keyphrases

• Loss Function 100%
• Deep Learning 100%
• Sleep Disorders 100%
• Sleep Stage Classification 100%
• Novel Solutions 100%
• Processing Time 50%
• Deep Learning Methods 50%
• Classification Model 50%
• EEG Signals 50%
• Feature Extraction 25%
• Training Time 25%
• Training Intensity 25%
• Neural Network Model 25%
• Multiple Datasets 25%
• Main Components 25%
• Feature Classification 25%
• Adaptive Filter 25%
• Normalization Method 25%
• Sleep Stages 25%
• Pooling Layer 25%
• Convolutional Layer 25%
• Fully Connected Layer 25%
• Multiple Pooling 25%
• Sleep Classification 25%
• Batch Normalization 25%
• Softmax Activation 25%
• Deep Learning Classification 25%
• 1-dimensional Convolutional Neural Network (1D-CNN) 25%

Engineering

• Loss Function 100%
• Deep Learning Method 100%
• Learning Approach 50%
• Processing Time 50%
• One Dimensional 25%
• Feature Extraction 25%
• Millisecond 25%
• Main Component 25%
• Limitations 25%
• Data Sample 25%
• Network Model 25%
• Adaptive Filter 25%
• Convolutional Neural Network 25%

Computer Science

• Adaptive Filter 25%