Classification of Arrhythmia Conditions using Neural Networks
T.LathaMaheswari1, S.Anumitha2, R.Ajeetha3
1HT.Latha Maheswari (Associate Professor), Dept. Of CSE ,Sri Krishna College Of Engineering And Technology, Coimbatore, India.
2S.Anumitha, Dept. Of CSE ,Sri Krishna College Of Engineering And Technology, Coimbatore, India.
3R.Ajeetha, Dept. Of CSE ,Sri Krishna College Of Engineering And Technology, Coimbatore, India.
Manuscript received on May 16, 2020. | Revised Manuscript received on May 10, 2020. | Manuscript published on June 10, 2020. | PP: 421-424 | Volume-9 Issue-8, June 2020. | Retrieval Number: G5367059720/2020©BEIESP | DOI: 10.35940/ijitee.G3367.069820
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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 this paper, we are discussing about a heart disease called Arrhythmia and how it can be identified using the Electrocardiogram. Electrocardiogram (ECG) is a graphical form for electrical activity of cardiac muscle. A healthy human heart beats, 72 times per minute under normal conditions. For every heartbeat the cardiac muscle undergoes specific electrical activity which identifies the pattern in the ECG signal. It consists of PQRST wave which represents heart functions. The patterns of the ECG signal change due to the abnormalities in the heartbeat. The abnormality in the ECG is called Arrhythmia.
Keywords: Electrocardiogram (ECG) is a Graphical form for Electrical Activity of Cardiac Muscle.
Scope of the Article: Classification