An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Classifier ensembles: Select real-world applications
Information Fusion
Detecting ECG abnormalities via transductive transfer learning
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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In this paper, a novel hybrid kernel machine ensemble is proposed for abnormal ECG beat detection to facilitate long-term monitoring of heart patients. A binary SVM is trained using ECG beats from different patients to adapt to the reference values based on the general patient population. A one-class SVM is trained using only normal ECG beats from a specific patient to adapt to the specific reference value of the patient. Trained using different data sets, these two SVMs usually perform differently in classifying ECG beats of that specific patient. Therefore, integration of the two types of SVMs is expected to perform better than using either of them separately and that improving the generalization. Experimental results using MIT/BIH arrhythmia ECG database show good performance of our proposed ensemble and support its feasibility in practical clinical application.