2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 3 , Issue 1 , PP: 43-53, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Rozin Majeed Abdullah 1 , Adnan Mohsin Abdulazeez 2
Doi: https://doi.org/10.54216/FPA.030103
Due to many new medical uses, the value of ECG classification is very demanding. There are some Machine Learning (ML) algorithms currently available that can be used for ECG data processing and classification. The key limitations of these ML studies, however, are the use of heuristic hand-crafted or engineered characteristics of shallow learning architectures. The difficulty lies in the probability of not having the most suitable functionality that will provide this ECG problem with good classification accuracy. One choice suggested is to use deep learning algorithms in which the first layer of CNN acts as a feature. This paper summarizes some of the key approaches of ECG classification in machine learning, assessing them in terms of the characteristics they use, the precision of classification important physiological keys ECG biomarkers derived from machine learning techniques, and statistical modeling and supported simulation.
ECG classification, deep learning, machine learning, convolutional neural networks.
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