Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification.
Mohamed I. OwisAhmed H. Abou-ZiedAbou Bakr M. YoussefYasser M. KadahPublished in: IEEE Trans. Biomed. Eng. (2002)
Keyphrases
- ecg signals
- classification accuracy
- feature extraction
- feature vectors
- classification method
- feature set
- mit bih arrhythmia database
- feature space
- pattern recognition
- feature extraction and classification
- false positives
- beat classification
- classification process
- support vector machine
- detection algorithm
- gabor filters
- extracted features
- svm classifier
- feature values
- image classification
- support vector
- heart rate variability
- extracting features
- features extraction
- mammogram images
- machine learning
- classification models
- adaboost classifier
- benchmark datasets
- training set
- image features
- object detection
- anomaly detection
- supervised learning
- nonlinear dynamical
- detection rate
- neural network
- decision trees
- neyman pearson
- support vector machine classifier
- support vector machine svm
- feature analysis
- machine learning algorithms
- discriminative features
- class labels
- classification algorithm
- high dimensionality