Multimodal Fusion of Deep Convolution Neural Network to Screen Abnormalities |
Author(s): |
| N.Pavan , R.M.D Engineering College; N.Chanikya, R.M.D Engineering College; N.Harish, R.M.D Engineering College; Mr. M . Jyothi Prasad, R.M.D Engineering College |
Keywords: |
| Electrocardiogram, Intelligence Bedside Monitor, Convolutional Neural Network, Feature Extraction, Heart Arrhythmia |
Abstract |
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Cardiac arrhythmia indicates abnormal electrical activity of heart that can be a great threat to human. So it needs to be identified for clinical diagnosis and treatment. Analysis of ECG signal plays an important role in diagnosing cardiac diseases. An efficient method of analyzing ECG signal fusion and predicting heart abnormalities have been proposed in this paper. In the proposed scheme, at first the signal components have been extracted from the noisy ECG signal by rejecting the background noise. The final task is to classify the heart abnormalities according to previous extracted features. The Neural Network trained feed-forward neural network has been selected for this research. Here, data used for the analysis of ECG signal are from database. |
Other Details |
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Paper ID: IJSRDV8I70267 Published in: Volume : 8, Issue : 7 Publication Date: 01/10/2020 Page(s): 390-394 |
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