ConvNet: 1D-Convolutional Neural Networks for Cardiac Arrhythmia Recognition Using ECG Signals

Slama, Amine Ben; Sahli, Hanene; Maalmi, Ramzi; Trabelsi, H.

doi:10.18280/ts.380617

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…The doctor later arranged for the patient to have a 24-hour ambulatory electrocardiogram and a cardiogram and finally deduced that the patient had unintentionally developed symptoms of coronary artery blockage but eventually got better after the corresponding treatment. Through the above case study, it is known that in real life, many diseases are likely to be related to the heart; so, regular cardiac examination is worthy of attention as well as essential [6].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Liang

2022

Journal of Sensors

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In recent years, China’s economy has been developing rapidly, and people’s life and quality of life have been improving; more importantly, people’s habits and living habits have also developed from the previous “unhygienic” and “not very careful” to the current “Healthier, more hygienic, greener and more sophisticated” direction. In the process of this development, due to the rapid development of the economy and the industrialization of cities, the incidence of heart disease is also increasing year by year. According to relevant studies, China’s urbanization process has been unprecedented, the number of urbanized people in China has exploded in recent years, and the ratio of urban to rural population has increased from about 20 percent to about 75 percent today. The effects of the population and the urbanization of urban architecture are affecting people’s physical and mental health, both consciously and unconsciously, causing both positive and negative physical and mental effects on the psychological and physical levels. In this paper, the concept of deep learning is fully utilized to train the CNN neural network model and apply it to the ECG abnormality recognition and prediction examination. In order to fully validate the application and significance of deep learning in ECG abnormality identification and prediction, the whole project was completed through subjective and objective experiments. The experimental results show that, from the subjective aspect, the ECG examination has been accepted by most people for different age groups, and the analysis results of the ECG examination with the deep learning model in this paper are more satisfactory; from the objective aspect, the CNN-ECG abnormality recognition prediction network model trained in this paper has high. The accuracy of the model for ECG abnormality recognition prediction can reach 86% when the learning rate is set to 0.0001 and the batch size is set to 120, and the model can reduce the burden of medical personnel to a certain extent.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Liang

2022

Journal of Sensors

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“…( 9) and Eq. ( 10) respectively [14]. The results obtained using the RF classifier, as shown in the confusion matrix depicted in Figure 5, indicate that the classifier has achieved an impressive level of performance, with accuracy, sensitivity, and specificity values of 99.77%, 99.82%, and 99.92%, respectively.…”

Section: Resultsmentioning

confidence: 93%

“…This deep learning-ECG signal correlation illuminates the potential for advanced analysis and accurate results in cardiac diagnostics [12]. A 1D-CNN-based classification method for classifying ECG arrhythmias post ECG signal preprocessing through DWT was presented by Slama et al [14]. Yıldırım et al [15] presented a CNN-based approach to ECG arrhythmia classification, and then employed it to enhance classification accuracy by combining SVM.…”

Section: Related Workmentioning

confidence: 99%

Enhancing ECG Signal Classification Accuracy Through Gaussian Modeling Method

Fadhel,

Hasan

2023

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The automatic classification of ECG signals, also known as computer-based classification, has become critically important in the diagnostic landscape of E-health. This study presents an innovative approach that employs Gaussian Modeling to enhance the accuracy of Beat signal approximation prior to training and classification. Previous methodologies largely relied on Artificial Intelligence (AI) and were contingent on the quality of collected field data sets, which often contained numerous artifacts and noise. This study demonstrates that addressing and eliminating these issues before training can significantly improve classification outcomes. The performance of the proposed approach was rigorously evaluated through several classifiers, including Support Vector Machine (SVM), Decision Trees (DT), K-Nearest Neighbours (KNN), Random Forest (RF), Naive Bayes (NB), Quadratic Discriminant Analysis (QDL), and Convolutional Neural Networks (CNN). These classifiers were applied to the MIT-BIH arrhythmia database, revealing a significant enhancement in results compared to conventional methods. Our findings underscore the efficacy of the Gaussian function in modeling ECG signals, improving the accuracy of various classifiers. Remarkable levels of accuracy, sensitivity, and specificity were achieved across classifiers, with some reaching an accuracy rate of 100%. Notably, the CNN classifier exhibited exceptional performance, demonstrating an accuracy rate of 99.65%, sensitivity of 99.64%, and specificity of 99.88%. This study contributes to the ongoing efforts in the Ehealth domain to improve diagnostic procedures through AI, offering a significant advancement in ECG signal classification.

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“…For feature selection, the authors employed the recursive features eliminator algorithm, and they integrated the effectiveness of Random Forest (RF), Functional Linear Discriminant Analysis (FLDA), SVM, and DL methods. 8 popular ML methods were created for 21 features from the open-source database at UCI, and their performances were then assessed [16][17][18].…”

Section: Related Workmentioning

confidence: 99%

Fast Mask Recurrent Convolutional Neural Network for IoT-Based Maternal and Fetal Monitoring in High-Risk Pregnancies

Ettiyan¹,

Geetha²

2023

RIA

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Ensuring the well-being of fetuses and their timely diagnosis for potential abnormalities is a critical aspect of healthcare. Early identification of intrauterine growth restriction can facilitate appropriate interventions and improve neonatal outcomes. This study presents a novel approach incorporating the Internet of Things (IoT) and Artificial Intelligence (AI) in the medical domain for the automatic detection of fetal abnormalities. IoT sensors were employed to gather maternal clinical data, including temperature, blood pressure, oxygen saturation levels, and fetal heart rate. A Fast Mask Recurrent Convolutional Neural Network (FMRCNN) was proposed to predict and accurately classify a range of conditions affecting pregnant women and their unborn children. The developed FMRCNN model learns, segments, and classifies fetal abdominal images to identify abnormalities. Additionally, a unified fetal abnormality prediction model was established to process and classify both fetal abdomen and brain ultrasound images. Comparative performance analysis was conducted using Convolutional Neural Networks (CNN), Random Forest (RF), and Support Vector Machine (SVM) algorithms. Evaluation metrics, such as F1score, accuracy, precision, recall, and sensitivity, were employed to assess the effectiveness of the proposed approach. The results indicate that the presented FMRCNN model holds promise for IoT-based maternal and fetal monitoring in high-risk pregnancies.

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ConvNet: 1D-Convolutional Neural Networks for Cardiac Arrhythmia Recognition Using ECG Signals

Cited by 4 publications

References 29 publications

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Enhancing ECG Signal Classification Accuracy Through Gaussian Modeling Method

Fast Mask Recurrent Convolutional Neural Network for IoT-Based Maternal and Fetal Monitoring in High-Risk Pregnancies

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