Full training convolutional neural network for ECG signals classification

Karboub, Kaouter; Mohamed, Tabaa; Dellagi, Sofiène; Abbas, Dandache; Moutaouakkil, Fouad

doi:10.1063/1.5138541

Cited by 19 publications

(11 citation statements)

References 42 publications

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“…It can be observed that the Acc of the proposed approach is better than the methods of [18, 19, 21-23, 48-51, 89-93] and is nearly the same as the technique of [24]. Also, the Sp of the proposed approach is better than the systems of [18,[21][22][23][24][48][49][50], and comparable with the methods of [19,51].…”

Section: Overall Classification Performancementioning

confidence: 75%

“…This is more challenging than classifying only CHF cases from NSR subjects but very useful in several clinical applications. Few studies [49,[89][90][91][92][93] focused on the three-class ECG classification problem and reported classification results for the three classes (CHF, ARR, and NSR). The proposed approach outperforms all these techniques.…”

Section: Overall Classification Performancementioning

confidence: 99%

“…Various ML techniques and pre-trained CNN architectures were investigated for medical imaging classification [44][45][46]. In contrast, few DL models have reported robust performance for ECG classification of cardiac diseases [47][48][49][50][51]. A pre-trained CNN model with a distance distribution matrix in entropy calculation was proposed in [48] for CHF diagnosis with an accuracy of 81.9% and sensitivity of 80.99%.…”

Section: Introductionmentioning

confidence: 99%

“…A pre-trained CNN model with a distance distribution matrix in entropy calculation was proposed in [48] for CHF diagnosis with an accuracy of 81.9% and sensitivity of 80.99%. In [49], a DL model based on simple convolutional units and time-frequency characteristics was investigated to classify CHF and ARR with an accuracy of 93.75%. In [51], an ensemble approach based on short-term HRV data and deep NN was developed for CHF detection.…”

Section: Introductionmentioning

confidence: 99%

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Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Eltrass

Tayel

Ammar

2022

Neural Comput & Applic

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Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several types of heart disorders. In this study, a novel hybrid approach of deep neural network combined with linear and nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-class classification. The proposed system enhances the ECG diagnosis performance by combining optimized deep learning features with an effective aggregation of ECG features and HRV measures using chaos theory and fragmentation analysis. The constant-Q non-stationary Gabor transform technique is employed to convert the 1-D ECG signal into 2-D image which is sent to a pre-trained convolutional neural network structure, called AlexNet. The pair-wise feature proximity algorithm is employed to select the optimal features from the AlexNet output feature vector to be concatenated with the ECG and HRV measures. The concatenated features are sent to different types of classifiers to distinguish three distinct subjects, namely congestive heart failure, arrhythmia, and normal sinus rhythm (NSR). The results reveal that the linear discriminant analysis classifier has the highest accuracy compared to the other classifiers. The proposed system is investigated with real ECG data taken from well-known databases, and the experimental results show that the proposed diagnosis system outperforms other recent state-of-the-art systems in terms of accuracy 98.75%, specificity 99.00%, sensitivity of 98.18%, and computational time 0.15 s. This demonstrates that the proposed system can be used to assist cardiologists in enhancing the accuracy of ECG diagnosis in real-time clinical setting.

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Section: Overall Classification Performancementioning

confidence: 75%

Section: Overall Classification Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Eltrass

Tayel

Ammar

2022

Neural Comput & Applic

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“…The performance achieved was 93.4% accuracy for arrhythmia classification, and 95.9% accuracy for MI prediction on the PTBDB. A CNN model was proposed by Kaouter et al (Kaouter et al 2019) for ECG classification task. The model was compared with Ensemble of Fine-Tuned CNN, VGG Net-16 and dataset and kept the noise on the other dataset.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram

Musa

Gital

Aljojo

et al. 2022

J Ambient Intell Human Comput

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The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG.The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm.

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Transcending Boundaries: Assessing Transfer Learning’s Effectiveness in ECG-Based Heart Disease Prediction

Nag,

Mondal,

Hassan

et al. 2024

Soft Computing in Industry 5.0 for Sustainability

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Full training convolutional neural network for ECG signals classification

Cited by 19 publications

References 42 publications

Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram

Transcending Boundaries: Assessing Transfer Learning’s Effectiveness in ECG-Based Heart Disease Prediction

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