System identification: theory for the user
System identification: theory for the user
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Probabilistic model-based clustering of complex data
Probabilistic model-based clustering of complex data
Digital Signal Processing (4th Edition)
Digital Signal Processing (4th Edition)
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
Pattern Recognition Letters
Integration of independent component analysis and neural networks for ECG beat classification
Expert Systems with Applications: An International Journal
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Superiority real-time cardiac arrhythmias detection using trigger learning method
ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics
Automated Screening of Arrhythmia Using Wavelet Based Machine Learning Techniques
Journal of Medical Systems
Application of principal component analysis to ECG signals for automated diagnosis of cardiac health
Expert Systems with Applications: An International Journal
Fuzzy expert system approach for coronary artery disease screening using clinical parameters
Knowledge-Based Systems
UMPCA based feature extraction for ECG
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Automated detection of atrial fibrillation using Bayesian paradigm
Knowledge-Based Systems
Heartbeat classification using disease-specific feature selection
Computers in Biology and Medicine
International Journal of Mobile Learning and Organisation
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An automatic classifier for electrocardiogram (ECG) based cardiac abnormality detection using Gaussian mixture model (GMM) is presented here. In first stage, pre-processing that includes re-sampling, QRS detection, linear prediction (LP) model estimation, residual error signal computation and principal component analysis (PCA) has been used for registration of linearly independent ECG features. GMM is here used for classification based on the registered features in a two-class pattern classification problem using 730 ECG segments from MIT-BIH Arrhythmia and European ST-T Ischemia datasets. A set of 12 features explaining 99.7% of the data variability is obtained using PCA from residual error signals for GMM based classification. Sixty percent of the data is used for training the classifier and 40% for validating. It is observed that the overall accuracy of the proposed strategy is 94.29%. As an advantage, it is also verified that Chernoff bound and Bhattacharya bounds lead to minimum error for GMM based classifier. In addition, a comparative study is done with the standard classification techniques with respect to its overall accuracy.