Superiority real-time cardiac arrhythmias detection using trigger learning method

Authors:
Mohamed Ezzeldin A. Bashir;Kwang Sun Ryu;Soo Ho Park;Dong Gyu Lee;Jang-Whan Bae;Ho Sun Shon;Keun Ho Ryu
Affiliations:
Database/Bioinformatics Laboratory, Chungbuk National University, Korea;Database/Bioinformatics Laboratory, Chungbuk National University, Korea;Database/Bioinformatics Laboratory, Chungbuk National University, Korea;Database/Bioinformatics Laboratory, Chungbuk National University, Korea;College of Medicine, Chungbuk National University, Cheongju City, South Korea;Database/Bioinformatics Laboratory, Chungbuk National University, Korea;Database/Bioinformatics Laboratory, Chungbuk National University, Korea
Venue:
ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics
Year:
2011

Citing 7
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Abstract

The Electrocardiogram (ECG) signal uses by Clinicians to extract very useful information about the functional status of the heart, accurate and computationally efficient means of classifying cardiac arrhythmias has been the subject of considerable research efforts in recent years. The contradicting considerations on the unique characteristics of patient's activities and the inherent requirements of real-time heart monitoring pose challenges for practical implementation. That is due to susceptibility to potentially changing morphology not only between different patients or patient cluster, but also within the same patient. As a result, the model constructed using an old training data no longer needs to be adapt with the new concepts. Consequently, developing one classifier model to satisfy all patients in different situation using static training datasets is unsuccessful. Our proposed methodology automatically trains the classifier model by up-to-date training data, so as to be identifying with the new concepts. The performance of the trigger method is evaluated using various approaches. The results demonstrate the effectiveness of our proposed technique, and they suggest that it can be used to enhance the performance of new intelligent assistance diagnosis systems.