Feature extraction and state identification in biomedical signals using hierarchical fuzzy clustering

Keyphrases

• EEG Signals 100%
• Epileptic Seizure 100%
• Feature Extraction 100%
• Hierarchical Fuzzy Clustering 100%
• State Identification 100%
• State Recognition 100%
• Biomedical Signals 100%
• Biological Systems 50%
• Stationary Process 50%
• Stationary States 50%
• Wide Dynamic Range 50%
• Temporal Patterns 50%
• Fuzzy Clustering 50%
• Biomedical Signal Processing 50%
• Falling Asleep 50%
• Validity Problem 50%
• Clustering Methods 50%
• One Data 50%
• Feasible Solution 50%
• Covariance Matrix 50%
• Number of Clusters 50%
• Psychotic 50%
• Recovery from Exercise 50%
• Cluster Validity Index 50%
• Recursive Algorithm 50%
• Vehicle Driver 50%
• Heart Sound Signal 50%
• Hierarchical Fuzzy 50%
• Hierarchical Concept 50%
• Fuzzy Concept 50%
• Fuzzy Partition 50%
• Event Prediction 50%
• Adaptive Selection 50%
• Tachycardia Detection 50%
• ECG Signal 50%
• Sampling Measurement 50%
• One-stationary 50%
• Nature-based Solutions 50%
• Attack Prediction 50%

Engineering

• Tasks 100%
• Stationary State 100%
• Dynamic Range 100%
• Biological System 100%
• Covariance Matrix 100%
• Recursive Algorithm 100%
• Feasible Solution 100%
• Feature Extraction 100%
• Natural Solution 100%
• Biomedical Signal Processing 100%

Computer Science

• And-States 100%
• Feature Extraction 100%
• Biomedical Signal 100%
• Fuzzy Clustering 100%
• Epileptic Seizure 100%
• Biological System 50%
• Feasible Solution 50%
• Cluster Validity 50%
• Clustering Method 50%
• Stationary Nature 50%
• Covariance Matrix 50%
• Biomedical Signal Processing 50%
• Stationary State 50%
• Heart Rate Signal 50%