Abstract
Automatic classification of heartbeat is getting a significant value in today’s medical systems. By implementation of these methods in portable diagnosis devices, many mortal diseases can be realized and cured in primary steps. In this paper two separate classifiers are designed and compared for heartbeat classification. The first strategy profits principal component analysis for feature extraction and neural network for classification whereas the second strategy utilizes discrete wavelet transform (DWT) for feature extraction and neural network (NN) as classifier. The arrhythmias which are investigated here include: normal beats (N), right bundle branch block (RBBB), left bundle branch block (LBBB), ventricular premature contraction (VPC) and paced beat (P). In addition, an output for unspecified signals is considered which devotes to anonymous signals which are not in the above list. The results show that both methods could achieve above 98% accuracy on MIT-BIH database.
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This work was financed by the Islamic Azad University, Nour Branch, Nour, Iran, which is gratefully acknowledged.
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Mohseni, S.S., Khorsand, V. (2019). Design and Comparison of ECG Arrhythmias Classifiers Using Discrete Wavelet Transform, Neural Network and Principal Component Analysis. In: Hadjiski, M., Atanassov, K. (eds) Intuitionistic Fuzziness and Other Intelligent Theories and Their Applications. Studies in Computational Intelligence, vol 757. Springer, Cham. https://doi.org/10.1007/978-3-319-78931-6_12
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DOI: https://doi.org/10.1007/978-3-319-78931-6_12
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