A Concept Learning-Based Patient-Adaptable Abnormal ECG Beat Detector for Long-Term Monitoring of Heart Patients
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
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.