Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of transient ST segment episodes during ambulatory ECG monitoring
Computers and Biomedical Research
Non-linear dimensionality reduction techniques for classification and visualization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Feature Selection Using Hybrid Evaluation Approaches Based on Genetic Algorithms
CERMA '06 Proceedings of the Electronics, Robotics and Automotive Mechanics Conference - Volume 02
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Incremental HMM training applied to ECG signal analysis
Computers in Biology and Medicine
Coordinated graph and scatter-plot views for the visual exploration of microarray time-series data
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
ECG analysis using nonlinear PCA neural networks for ischemiadetection
IEEE Transactions on Signal Processing
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An ischemia detection method based on artificial neural networks
Artificial Intelligence in Medicine
Ischemia detection with a self-organizing map supplemented by supervised learning
IEEE Transactions on Neural Networks
Introduction to the special section on computationalintelligence in medical systems
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Relevance analysis of stochastic biosignals for identification of pathologies
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
| Hi-index | 0.00 |
An effective data representation methodology on high-dimension feature spaces is presented, which allows a better interpretation of subjacent physiological phenomena (namely, cardiac behavior related to cardiovascular diseases), and is based on search criteria over a feature set resulting in an increase in the detection capability of ischemic pathologies, but also connecting these features with the physiologic representation of the ECG. The proposed dimension reduction scheme consists of three levels: projection, interpretation, and visualization. First, a hybrid algorithm is described that projects the multidimensional data to a lower dimension space, gathering the features that contribute similarly in the meaning of the covariance reconstruction in order to find information of clinical relevance over the initial training space. Next, an algorithm of variable selection is provided that further reduces the dimension, taking into account only the variables that offer greater class separability, and finally, the selected feature set is projected to a 2-D space in order to verify the performance of the suggested dimension reduction algorithm in terms of the discrimination capability for ischemia detection. The ECG recordings used in this study are fromthe European ST-T database and from the Universidad Nacional de Colombia database. In both cases, over 99% feature reduction was obtained, and classification precision was over 99% using a five-nearest-neighbor classifier (5-NN).