Biomedical digital signal processing: C-language examples and laboratory experiments for the IBM PC
Biomedical digital signal processing: C-language examples and laboratory experiments for the IBM PC
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Computing and Applications
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm
EMS '09 Proceedings of the 2009 Third UKSim European Symposium on Computer Modeling and Simulation
Arrhythmia Beat Classification Using Pruned Fuzzy K-Nearest Neighbor Classifier
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
IEEE Transactions on Information Technology in Biomedicine
An arrhythmia classification system based on the RR-interval signal
Artificial Intelligence in Medicine
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Real-time CHF detection from ECG signals using a novel discretization method
Computers in Biology and Medicine
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This paper describes an automatic classification system based on combination of diverse features for the purpose of automatic heartbeat recognition. The method consists of three stages. At the first stage, heartbeats are classified into 5 main groups defined by AAMI using optimal feature sets for each main group. At the second stage, main groups are classified into subgroups using optimal features for each subgroup. Then the third stage is added to the system for classifying beats that are labeled as unclassified beats in the first two classification stages. A diverse set of features including higher order statistics, morphological features, Fourier transform coefficients, and higher order statistics of the wavelet package coefficients are extracted for each different type of ECG beat. At the first stage, optimal features for main groups are determined by using a wrapper type feature selection algorithm. At the second stage, optimal features are similarly selected for discriminating each subgroup of the main groups. Then at the third stage, only raw data is used for classifying beats. In all stages, the classifiers are based on the k-nearest neighbor algorithm. ECG records used in this study are obtained from the MIT-BIH arrhythmia database. The classification accuracy of the proposed system is measured by sensitivity, selectivity, and specificity measures. The system is classified 16 heartbeat types. The measures of proposed system are 85.59%, 95.46%, and 99.56%, for average sensitivity, average selectivity, and average specificity, respectively.