Asymmetric Back Propagation Neural Network-Based Automatic Cardiac Disease Detection Using Electrocardiogram Signal

Early detection of unusual heart conditions is of vital importance to recognize heart disappointment and maintain a strategic distance from unexpected death. The humans with similar heart conditions have been practically identical using electrocardiogram (ECG) signals. By reviewing the ECG signal mo...

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Bibliographic Details
Published in:Turkish journal of computer and mathematics education 2021-01, Vol.12 (10), p.2621-2631
Main Authors: Suhail, M Mohamed, Razak, T Abdul
Format: Article
Language:English
Subjects:
Asymmetry
Back propagation
Back propagation networks
Cardiovascular disease
Classification
Electrocardiography
Heart
Heart diseases
Heart Disorders
Networks
Neural networks
Recurrent neural networks
Research facilities
Signal classification
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Summary:Early detection of unusual heart conditions is of vital importance to recognize heart disappointment and maintain a strategic distance from unexpected death. The humans with similar heart conditions have been practically identical using electrocardiogram (ECG) signals. By reviewing the ECG signal models, one can anticipate heart disease. Since the standard techniques for heart disease disclosure depend upon securing morphological features of the ECG signals, which are repetitious and tedious, the customized recognizable proof of cardiovascular disease is progressively perfect. Subsequently, in order to have the programmed identification of heart diseases, a satisfactory strategy is required. This could arrange the ECG signals with dark features as appeared by the similitudes among them and the ECG signals with known features. If this classifier can discover the similitudes, the likelihood of cardiovascular disease disclosure is broadened. This count can change into a significant procedure in research facilities during this examination work. Another classification technique is brought into the system. The Asymmetric Back Propagation Neural Network classification methodology, which all the more precisely orders ECG signals that rely upon a powerful model of the Electrocardiogram (ECG) signal classification. With this proposed method, a convolutional gated recurrent neural network is constructed, and its simulation results show that this classification can partition the ECG with 98.5% accuracy.
ISSN:1309-4653