ECG Arrhythmia Classification Algorithms
Ashish Nainwal1, Yatindra Kumar2, Bhola Jha3
1Ashish Nainwal, ECE department, Faculty of Engineering & Technology, Gurukul Kangri Vishwavidyalaya, Haridwar, India.
2Yatindra Kumar, Electrical Department, G B Pant Institute of Technology, Pauri Garhwal, India.
3Bhola Jha, Electrical Department, G B Pant Institute of Technology, Pauri Garhwal, India.
Manuscript received on 01 August 2019. | Revised Manuscript received on 08 August 2019. | Manuscript published on 30 September 2019. | PP: 6667-6674 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5414098319/2019©BEIESP | DOI: 10.35940/ijrte.C5414.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In last two decades lot of work is introduced on ECG classification. Authors took different database, features of ECG, class of data, learning and training algorithms to classify ECG signal. Normally class of data mentioned in source of database. Mainly three classification techniques which are discussed in this paper, these are support vectored machine (SVM), artificial neural network (ANN) and linear discriminate (LD). In this paper all the ECG classification based papers are analyzed and try to find out loophole and future challenges. This paper also discusses the different database of ECG signal.
Keywords: ECG, ANN, SVM, LD, MIT-BIH.
Scope of the Article: Classification