The nature of statistical learning theory
The nature of statistical learning theory
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
An introduction to variable and feature selection
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Combined SVM-Based Feature Selection and Classification
Machine Learning
Kernel Methods in Bioengineering, Signal And Image Processing
Kernel Methods in Bioengineering, Signal And Image Processing
Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats
Digital Signal Processing
A review of feature selection techniques in bioinformatics
Bioinformatics
Discrimination of ventricular arrhythmias using NEWFM
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Enhancing genetic feature selection through restricted search and Walsh analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Expert Systems with Applications: An International Journal
Visual data mining with self-organising maps for ventricular fibrillation analysis
Computer Methods and Programs in Biomedicine
Texture classification using kernel-based techniques
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Self-advising support vector machine
Knowledge-Based Systems
A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
Early detection of ventricular fibrillation (VF) is crucial for the success of the defibrillation therapy in automatic devices. A high number of detectors have been proposed based on temporal, spectral, and time-frequency parameters extracted from the surface electrocardiogram (ECG), showing always a limited performance. The combination ECG parameters on different domain (time, frequency, and time-frequency) using machine learning algorithms has been used to improve detection efficiency. However, the potential utilization of a wide number of parameters benefiting machine learning schemes has raised the need of efficient feature selection (FS) procedures. In this study, we propose a novel FS algorithm based on support vector machines (SVM) classifiers and bootstrap resampling (BR) techniques. We define a backward FS procedure that relies on evaluating changes in SVM performance when removing features from the input space. This evaluation is achieved according to a nonparametric statistic based on BR. After simulation studies, we benchmark the performance of our FS algorithm in AHA and MIT-BIH ECG databases. Our results show that the proposed FS algorithm outperforms the recursive feature elimination method in synthetic examples, and that the VF detector performance improves with the reduced feature set.