KSII Transactions on Internet and Information Systems (TIIS)

  • 제7권1호
  • /
  • Pages.99-118
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  • 2013
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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Real-Time Automated Cardiac Health Monitoring by Combination of Active Learning and Adaptive Feature Selection

  • Bashir, Mohamed Ezzeldin A. (Faculty of Computer Science & Information Technology, University of Medical Sciences & Technology) ;
  • Shon, Ho Sun (School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Lee, Dong Gyu (School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Kim, Hyeongsoo (School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Ryu, Keun Ho (School of Electrical & Computer Engineering, Chungbuk National University)
  • 투고 : 2012.05.13
  • 심사 : 2012.11.19
  • 발행 : 2013.01.31
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초록

Electrocardiograms (ECGs) are widely used by clinicians to identify the functional status of the heart. Thus, there is considerable interest in automated systems for real-time monitoring of arrhythmia. However, intra- and inter-patient variability as well as the computational limits of real-time monitoring poses significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is therefore a promising new intelligent diagnostic tool.

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