Classification of ECG Signal Using Deep Convolutional Neural
DOI:
https://doi.org/10.32792/jeps.v12i2.190Keywords:
Electrocardiogram (ECG),, Deep learning, , convolutional neural network (CNN).Abstract
In the last ten years, the characterization and expectation of cardiac illnesses based on ECG signals have become increasingly important for doctors and patients. This paper delivers a deep learning technique that has recently been created for the order of ECG information with Normal Sinus Rhythm (NSR), Abnormal Arrhythmia (ARR), and Congestive Heart Failure (CHF). A sum of 162 ECG signals is open, including 96 arrhythmias, 30 Congestive Heart failures, and 36 Normal Sinus Rhythm signals. To exhibit the classification performance of deep learning architectures, this paper studied ECG using the two CNN models GoogleNet and DenseNet201. The proposed study's classification accuracy is 91% and 100% respectively. The outcomes uncover that the proposed profound learning architecture is more precise than traditional machine learning classifiers at classifying ECG signals.
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