A Review Study for Electrocardiogram Signal Classification
DOI:
https://doi.org/10.21928/uhdjst.v4n1y2020.pp103-117Keywords:
Artificial neural network, Convolution neural network, Discrete wavelet transform, Support vector machine, K-nearest neighborAbstract
An electrocardiogram (ECG) signal is a recording of the electrical activity generated by the heart. The analysis of the ECG signal has been interested in more than a decade to build a model to make automatic ECG classification. The main goal of this work is to study and review an overview of utilizing the classification methods that have been recently used such as Artificial Neural Network, Convolution Neural Network (CNN), discrete wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor. Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects. The result also shows that the CNN has been most widely used for ECG classification as it can obtain a higher success rate than the rest of the classification approaches.
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[22] C. Kamalakannan, L. P. Suresh, S. S. Dash and B. K. Panigrahi. “Power Electronics and Renewable Energy Systems: Proceedings of ICPERES 2014. Vol. 326. Lecture Notes in Electrical Engineering, pp. 1537-1544, 2014.
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[27] G. H. Choi, E. S. Bak and S. B. Pan. “User identification system using 2D resized spectrogram features of ECG”. IEEE Access, vol.7, pp. 34862-34873, 2019.
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Published
2020-06-29
How to Cite
Abdulla, L. A., & Al-Ani, M. S. (2020). A Review Study for Electrocardiogram Signal Classification. UHD Journal of Science and Technology, 4(1), 103–117. https://doi.org/10.21928/uhdjst.v4n1y2020.pp103-117
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