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A Concept Learning-Based Patient-Adaptable Abnormal ECG Beat Detector for Long-Term Monitoring of Heart Patients
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Author(s): Peng Li (Nanyang Technological University, Singapore), Kap L. Chan (Nanyang Technological University, Singapore), Sheng Fu (Nanyang Technological University, Singapore)and Shankar M. Krishnan (Nanyang Technological University, Singapore)
Copyright: 2006
Pages: 25
Source title:
Neural Networks in Healthcare: Potential and Challenges
Source Author(s)/Editor(s): Rezaul Begg (Victoria University, Australia), Joarder Kamruzzaman (Monash University, Australia)and Ruhul Sarker (University of New South Wales, Australia)
DOI: 10.4018/978-1-59140-848-2.ch005
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Abstract
n this chapter, a new concept learning-based approach is presented for abnormal ECG beat detection to facilitate long-term monitoring of heart patients. The novelty in our approach is the use of complementary concept—“normal” for the learning task. The concept “normal” can be learned by a v-support vector classifier (v-SVC) using only normal ECG beats from aspecific patient to relieve the doctors from annotating the training data beat by beat to train a classifier. The learned model can then be used to detect abnormal beats in the long-term ECG recording of the same patient. We have compared with other methods, including multilayer feedforward neural networks, binary support vector machines, and so forth. Experimental results on MIT/BIH arrhythmia ECG database demonstrate that such a patient-adaptable concept learning model outperforms these classifiers even though they are trained using tens of thousands of ECG beats from a large group of patients.
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