Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals.
Fatin A. ElhajNaomie SalimArief R. HarrisTan Tian SweeTaqwa AhmedPublished in: Comput. Methods Programs Biomed. (2016)
Keyphrases
- ecg signals
- extracting features
- feature extraction
- mit bih arrhythmia database
- classification accuracy
- feature vectors
- pattern recognition
- feature space
- classification method
- feature analysis
- classification process
- beat classification
- features extraction
- benchmark datasets
- svm classifier
- feature set
- extracted features
- heart rate variability
- visual object recognition
- image classification
- classification models
- object classification
- support vector machine
- recognition rate
- automatic recognition
- decision trees
- highly discriminative
- atrial fibrillation
- feature extraction and classification
- handwritten digits
- power line
- object class recognition
- training set
- support vector
- recognition process
- object recognition
- class labels
- feature reduction
- individual features
- image features
- input features
- eeg signals
- medical images
- classification algorithm
- critical care
- support vector machine svm