Arrhythmia Detection from ECG Signals using CNN Model

Kumar, Gaurav; Pandey, Saroj Kumar; Varshney, Neeraj; Janghel, Rekh Ram; Singh, Kamred Udham; Kumar, Ankit

doi:10.1109/iscon57294.2023.10112173

Cited by 3 publications

(2 citation statements)

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“…In [9], two Neural Networks architecture have been used such as RNN and CNN in which the area under receiver operating characteristic curves of the 2 DL classifiers was 0.998%. In [10], a multi-layer perceptron network is used to classify arrhythmia ECG signals, the method is trained, and evaluated using MIT-BIH data set, and an average accuracy of 98.72% has been achieved. In [11], a proposed Google LeNet DNN architecture has been used to classify five types of ECG signals.…”

Section: State Of the Artmentioning

confidence: 99%

“…Accordingly, 1D-CNN for ECG signal classification is well suited for real-time and low-cost applications, thanks to their low computational requirements. Besides, varieties of DL techniques are used to automatically classify heartbeat signals [10]. Results demonstrate that CNN algorithms are among the top best algorithms in data classification and identification problems.…”

Section: State Of the Artmentioning

confidence: 99%

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Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks

et al. 2021

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Early heart disease class detection is of great interest to reduce the mortality rate. In this context, computational techniques have been proposed to solve this issue. Thus, here, a deep learning architecture is proposed to automatically classify the patient’s Electrocardiogram (ECG) signal into a specific class according to the ANSI–AAMI standards. The proposed methodology is a multi‐stage technique. The first stage combines an R–R peak extraction with a low‐pass filter applied on the ECG data for noise removal. The second stage shows the proposed convolutional neural network‐based fully connected layers architecture, using different network optimizers. Different ECG databases, including challenging tasks, have been used for validation purpose. The whole system is implemented on both CPU and GPU for complexity analysis. For the predicted improved PTB data set, the classification accuracy results achieve 99.37%, 99.15% and 99.31% for training, validation and testing, respectively. Besides, for the MIT‐BIH‐database, the training, validation and testing accuracies are 99.5%, 99.06% and 99.34%, respectively. A top F1‐score of 0.99 is obtained. Experimental results show a high achievement compared to the state‐of‐the‐art models where the implementation of GPU confirms the low computational complexity of the system.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%