The Supervised Network Self-Organizing Map for Classification of Large Data Sets
Applied Intelligence
Principal component analysis in ECG signal processing
EURASIP Journal on Applied Signal Processing
WSEAS Transactions on Computer Research
Dimensionality reduction oriented toward the feature visualization for ischemia detection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
A statistical approach for determination of time plane features from digitized ECG
Computers in Biology and Medicine
An ischemia detection method based on artificial neural networks
Artificial Intelligence in Medicine
An improved procedure for detection of heart arrhythmias with novel pre-processing techniques
Expert Systems: The Journal of Knowledge Engineering
A new non-exact aho-corasick framework for ECG classification
ACM SIGARCH Computer Architecture News
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The detection of ischemic cardiac beats from a patient's electrocardiogram (EGG) signal is based on the characteristics of a specific part of the beat called the ST segment. The correct classification of the beats relies heavily on the efficient and accurate extraction of the ST segment features. An algorithm is developed for this feature extraction based on nonlinear principal component analysis (NLPCA). NLPCA is a method for nonlinear feature extraction that is usually implemented by a multilayer neural network. It has been observed to have better performance, compared with linear principal component analysis (PCA), in complex problems where the relationships between the variables are not linear. In this paper, the NLPCA techniques are used to classify each segment into one of two classes: normal and abnormal (ST+, ST-, or artifact). During the algorithm training phase, only normal patterns are used, and for classification purposes, we use only two nonlinear features for each ST segment. The distribution of these features is modeled using a radial basis function network (RBFN). Test results using the European ST-T database show that using only two nonlinear components and a training set of 1000 normal samples from each file produce a correct classification rate of approximately 80% for the normal beats and higher than 90% for the ischemic beats