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  2. Volume 7, Issue 4
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Online ISSN: 2515-8260

Volume7, Issue4

ARRYTHMIA RECOGNITION AND CLASSIFICATION USING ECG MORPHOLOGY AND SEGMETATION

    S. Nithyaselvakumari M.C. Jobin Christ B A Gowri Shankar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2144-2155

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Abstract

Cardiac arrhythmia can be identified using abnormal electrical activity of heart, this is a great menace to humans. In order to diagnose cardiac problems ECG signal is widely used. When the background noise is rejected from the ECG signal we obtain a QRS component. This QRS component consists of high frequency and high energy waves that are very easy to detect and study. Once QRS component is obtained, it is further spited into various classes that can aid in diagnosing the abnormalities. Previously extracted features are compared to find the heart abnormalities. In this paper Feed-Forward neural network is selected and data base are used to store and analyze the data.
Keywords:
    neural network ECG signal Feature Extraction Classification Test and Training
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(2020). ARRYTHMIA RECOGNITION AND CLASSIFICATION USING ECG MORPHOLOGY AND SEGMETATION. European Journal of Molecular & Clinical Medicine, 7(4), 2144-2155.
S. Nithyaselvakumari; M.C. Jobin Christ; B A Gowri Shankar. "ARRYTHMIA RECOGNITION AND CLASSIFICATION USING ECG MORPHOLOGY AND SEGMETATION". European Journal of Molecular & Clinical Medicine, 7, 4, 2020, 2144-2155.
(2020). 'ARRYTHMIA RECOGNITION AND CLASSIFICATION USING ECG MORPHOLOGY AND SEGMETATION', European Journal of Molecular & Clinical Medicine, 7(4), pp. 2144-2155.
ARRYTHMIA RECOGNITION AND CLASSIFICATION USING ECG MORPHOLOGY AND SEGMETATION. European Journal of Molecular & Clinical Medicine, 2020; 7(4): 2144-2155.
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