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Combining General Multi-class and Specific Two-class Classifiers for Improved Customized ECG Heartbeat Classification

Can, Y. ; Bhagavatula, B.V.K. Vijaya Kumar ; Coimbra, M.

Combining General Multi-class and Specific Two-class Classifiers for Improved Customized ECG Heartbeat Classification, Proc International Conf. on Pattern Recognition - ICPR, Tsukuba, Japan, Vol. 1, pp. 1 - 4, November, 2012.

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Abstract
We present an approach for customized heartbeat classification of electrocardiogram (ECG) signals, based on the construction of one general multi-class classifier and one specific two-class classifier. The general classifier is trained on a global training dataset, containing examples of all possible classes and patterns. On the other hand, the individual-specific classifier is built using a small amount of individual data, which is a binary one-against-the-rest classifier, providing discrimination between normal and abnormal patterns from that individual. Such an individual-specific classifier can be a two-class classifier or a one-class classifier, depending on the availability of abnormal patterns in the individual training dataset. The classifications from the two classifiers are fused to obtain a final decision. The proposed approach is applied to the study of ECG heartbeat classification problem, significantly outperforming state-of-the-art methods. The proposed method can also be useful in anomaly detection of other biomedical signals.