Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection |
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Authors: | Felipe Alonso-Atienza José Luis Rojo-ÁlvarezAlfredo Rosado-Muñoz Juan J VinagreArcadi García-Alberola Gustavo Camps-Valls |
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Affiliation: | a Departamento de Teoría de la Señal y Comunicaciones, Universidad Rey Juan Carlos, Camino del Molino s/n, 28943 Fuenlabrada, Madrid, Spain b Departament de Enginyeria Electrónica, Universitat de Valéncia, Doctor Moliner 50, 46100 Burjassot, Valéncia, Spain c Unidad de Arritmias, Hospital Universitario Virgen de la Arrixaca, Ct. Madrid-Cartagena s/n, 30120 El Palmar, Murcia, Spain |
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Abstract: | 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. |
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Keywords: | Feature selection Support vector machines Bootstrap Arrhythmia classification Ventricular fibrillation detection |
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