Earticle

초록

영어

Proper indication of emergent cardiac anomalies is essential to saving human lives. Electrocardiogram (ECG) signals are mainly considered for indicating cardiac status. This work proposes a model that discriminates emergent cardiac anomalies (e.g. ventricular tachyarrhythmia, congestive heart failure, malignant ventricular ectopy, supraventricular arrhythmia) from normal cardiac status using an artificial neural network. The histogram of gradient (HOG) and principal component analysis (PCA) are applied to extract generic features of the ECG signals. Five auto-associative multilayer perceptrons (AAMLP) concatenated in a cascade manner are proposed for classification of four emergent cardiac ECG signals and normal ECG signals, which was developed for implementing a primitive prototype for a mobile bio-healthcare system. Experimental results show that the proposed model successfully classifies emergent cardiac anomalies.

목차

Abstract
 1. Introduction
 2. Proposed ECG Signal Classification Model
  2.1. Feature extraction of ECG signals
  2.2. Classification of ECG signals using cascaded AAMLPs
  2.3. Bio-healthcare Monitoring System
 3. Experimental Results
 4. Conclusion and Future Works
 Acknowledgments
 References

키워드

저자

• Hye-Jin Lee [ Dept. of Information and Communication Engineering, Dongguk University ]
• Jaeho Oh [ Dept. of Information and Communication Engineering, Dongguk University ]
• Chang-Beom Kwon [ Dept. of Information and Communication Engineering, Dongguk University ]
• Sang-Woo Ban [ Dept. of Information and Communication Engineering, Dongguk University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605