aspg-3-665-1791417643 20261008000043 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2021 3 1 Rozin Majeed Abdullah Master Student at ICT Department, Duhok Polytechnic University, Duhok-Kurdistan Region, Iraq Adnan Mohsin Abdulazeez Duhok Polytechnic University, Duhok-Kurdistan Region, Iraq, 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. 2021 43 53 665 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.030103 https://www.americaspg.com/journal/3/article/665 https://www.americaspg.com/storage/5670.pdf