ECG Signal Classification Based on Neural Network

Al-Saffar, Bashar; Ali, Yaseen Hadi; Muslim, Ali M.; Ali, Haider Abdullah

doi:10.1007/978-3-031-20429-6_1

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…The ANN method is widely used at the identification and classification stages. According to [3] an effective technique uses Artificial Neural Network (ANN) in performing classification. Similarly [7] states that Artificial Neural Networks are an invaluable tool when classifying ECG in patients.…”

Section: Fig 1 Ecg Illustration [5]mentioning

confidence: 99%

“…From the explanation above, research related to ECG with an artificial neural network approach can be presented in Table 5. [68] ANN (Levenberg-Marquardt) PhysioNet 2021 [2] ANN 2021 [69] Multilayered Perceptron Accuracy: 98,89 % PhysioNet 2022 [3] ANN Accuracy: 92,47 % UCI 2022 [70] Backpropagation Accuracy: 87 % 2022 [71] Multi-Layer Perceptron PhysioNet 2022 [39] CNN Accuracy: 92 % PhysioNet 2022 [72] Convolution Neural Networks Cardiovascular Department of Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI) in Trieste, Italy…”

Section: Identification and Performancementioning

confidence: 99%

“…ECG can be used to identify abnormal heart rate speeds, heart rhythm disturbances, and heart muscle damage. The doctors find it difficult in diagnosing abnormal heart behavior, but early and precise detection of heart defects helps in providing proper treatment to the patients [3]. Manual extraction in ECG readings is very less efficient Computer systems can help identify the electrical condition of the heart [4].…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Neural Networks in the Identification of Heart Abnormalities Based on Ecg: Literature Review

2023

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In recent years, a lot of research on Biomedicine is mainly related to heart defects. The recognition and detection of heart defects using an electrocardiogram (ECG) is numerous. This article aims to explain ECG research trends using the artificial neural networks (ANN) approach in the last twelve years. We reviewed journals with the keyword title "ANN ECG" and published from 2011 to 2022. Articles classified by the most frequently discussed topics include: data sets, case studies, pre-processing, feature extraction, and classification/identification methods. Data collection from articles obtained some of them use data banks such as PhysioNet, European Society of Cardiology (ESC), UCI Repository, as well as data collection directly from patients. The pre-processing stage of the total number of journals used 23% uses this stage, while in characteristic extraction almost 25% of papers are used. This research is very interesting because only a few researchers focus on researching about it. This article will provide further explanation of the most widely used algorithms for ECG research with the ANN approach. At the end of this article, critical aspects of ECG research can be carried out in the future, and the use of deep learning becomes a huge opportunity for researchers.

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Section: Fig 1 Ecg Illustration [5]mentioning

confidence: 99%