Identification of Premature Ventricular Contraction (PVC) Based on ECG Using Convolutional Neural Network
Akanksha Kothari1, Sankeit Dighe2, Kedar Kale3, Shubhangi Kale4
1Akanksha Kothari*, School of Computer Engineering & Technology, MIT Academy of Engineering, Pune, India.
2Sankeit Dighe, School of Computer Engineering & Technology, MIT Academy of Engineering, Pune, India.
3Kedar Kale, School of Computer Engineering & Technology, MIT Academy of Engineering, Pune, India.
4Shubhangi Kale, School of Computer Engineering & Technology, MIT Academy of Engineering, Pune, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2173-2177 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2804059120/2020©BEIESP | DOI: 10.35940/ijrte.A2804.059120
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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: Premature Ventricular Contraction (PVC) arrhythmia patients are subjected to dangerous heart rhythms that can be chaotic, and possibly result in abrupt death. Therefore, early detection of arrhythmia with high accuracy is extremely important to detect cardiovascular diseases. The classification of heartbeats based on ECG signals plays a vital role it the field of cardiac sciences to identify arrhythmias. The use of Artificial Neural Networks (ANN) has proven to be the most effective technique for sole agenda of classification. The use of CNN is simple and more noise immune method in comparison to various other techniques. In this paper, a survey of numerous algorithms and classification techniques along with their performance measures are presented. This paper proposes the identification of PVC on the basis of heart beats by using CNN and the results obtained are compared to other traditional approaches.
Keywords: ECG, CNN, ANN, PVC.
Scope of the Article: Artificial Neural Networks