Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Fast and accurate text classification via multiple linear discriminant projections
The VLDB Journal — The International Journal on Very Large Data Bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Enhanced Fisher Linear Discriminant Models for Face Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Computers in Biology and Medicine
Bayesian network classifiers versus selective k-NN classifier
Pattern Recognition
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Classifying Epilepsy Diseases Using Artificial Neural Networks and Genetic Algorithm
Journal of Medical Systems
Artificial Intelligence: The Basics
Artificial Intelligence: The Basics
Expert Systems with Applications: An International Journal
Discrimination Power of Short-Term Heart Rate Variability Measures for CHF Assessment
IEEE Transactions on Information Technology in Biomedicine
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
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In this study, the best combination of short-term heart rate variability (HRV) measures was investigated to distinguish 29 patients with congestive heart failure from 54 healthy subjects in the control group. In the analysis performed, wavelet packet transform based frequency-domain measures and several non-linear parameters were used in addition to standard HRV measures. The backward elimination and unpaired statistical analysis methods were used to select the best one among all possible combinations of these measures. Five distinct typical classifiers with different parameters were evaluated in discriminating these two groups using the leave-one-out cross validation method. Each algorithm was tested 30 times to determine the repeatability of the results. The results imply that the backward elimination method gives better performance when compared to the statistical significance method in the feature selection stage. The best performance (82.75%, 96.29%, and 91.56% for the sensitivity, specificity, and accuracy) was obtained by using the SVM classifier with 27 selected features including non-linear and wavelet-based measures.