Abstract
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.
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Li, P., Chan, K.L., Fu, S., Krishnan, S.M. (2005). An Abnormal ECG Beat Detection Approach for Long-Term Monitoring of Heart Patients Based on Hybrid Kernel Machine Ensemble. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_35
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DOI: https://doi.org/10.1007/11494683_35
Publisher Name: Springer, Berlin, Heidelberg
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