A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform

Eltrass, Ahmed S.; Tayel, Mazhar B.; Ammar, Abeer I.

doi:10.1016/j.bspc.2020.102326

Cited by 68 publications

(20 citation statements)

References 37 publications

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“…In the training process, the maximum epoch was set at 20 (Eltrass et al, 2021), the mini-batch size was set at 20 (Tian et al, 2020), maximum iteration at 1200, 60 iterations per epoch, momentum 0.9, and loss function used binary cross-entropy. Thenmozhi and Reddy (2019) research obtained the best learning rate in the CNN model at the value of 0.0001 and 0.00005, so the initial learning rate was set at 0.0001 and 0.00005 in this research.…”

Section: Methodsmentioning

confidence: 99%

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2022

JST

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Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world.In order to lower production costs, Indonesian producers and retailers often mix highpriced Luwak coffee with regular coffee green beans. However, the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing method's development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories, very low (0-25%), low (25-50%), medium (50-75%), and high (75-100%). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then, the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet, GoogLeNet, and AlexNet. At the same time, the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm, Adam, RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001, which resulted in 89.65% accuracy. Furthermore, the testing process using confusion matrix from different sample data obtained the best

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Section: Methodsmentioning

confidence: 99%

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2022

JST

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“…Some of these studies [ [45] , [46] , [47] ] employed 1-D ECG signals to classify the anomalies. Other research articles [ [48] , [49] , [50] , [51] , [52] ] transformed ECG signals to 2-D images using numerous methods such as wavelet transform and short-term frequency transform to detect abnormalities. Although the previous methods achieved significant success using public ECG signals-based datasets, it would be difficult to apply them in the real-world medical environment.…”

Section: Related Workmentioning

confidence: 99%

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

Attallah

2022

Computers in Biology and Medicine

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The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of the virus, alleviate lockdown constraints and diminish the burden on health organizations. Currently, the methods used to diagnose COVID-19 have several limitations, thus new techniques need to be investigated to improve the diagnosis and overcome these limitations. Taking into consideration the great benefits of electrocardiogram (ECG) application, this paper proposes a new pipeline called ECG-BiCoNet to investigate the potential of using ECG data for diagnosing COVID-19. ECG-BiCoNet employs five deep learning models of distinct structural design. ECG-BiCoNet extracts two levels of features from two different layers of each deep learning technique. Features mined from higher layers are fused using discrete wavelet transform and then integrated with lower-layers features. Afterward, a feature selection approach is utilized. Finally, an ensemble classification system is built to merge predictions of three machine learning classifiers. ECG-BiCoNet accomplishes two classification categories, binary and multiclass. The results of ECG-BiCoNet present a promising COVID-19 performance with an accuracy of 98.8% and 91.73% for binary and multiclass classification categories. These results verify that ECG data may be used to diagnose COVID-19 which can help clinicians in the automatic diagnosis and overcome limitations of manual diagnosis.

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“…In the current study, the CQ-NSGT technique [60][61][62] is proposed to extract the changes in the ECG signal and transform it into time-frequency representation. Conventional algorithms, including short-time Fourier transform (STFT) and continuous wavelet transform (CWT) [63][64][65][66] suffer from the fixed frequency resolution over the entire operating frequency and the high computational time, respectively.…”

Section: Qrs Detection and Ecg Transformationmentioning

confidence: 99%

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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A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform

Cited by 68 publications

References 37 publications

Untitled

Untitled

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

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

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