Deep-Learning-Based Detection of Paroxysmal Supraventricular Tachycardia Using Sinus-Rhythm Electrocardiograms

Wang, Lei; Dang, Song; Chen, Shuangxiong; Sun, Jin‐Yu; Wang, Ruxing; Pan, Feng

doi:10.3390/jcm11154578

Cited by 2 publications

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References 24 publications

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“…In a previous study, we trained a CNN with normal sinus rhythm ECGs of negative control patients and PSVT procedural patients. This model demonstrated well performance to identify individuals with a high likelihood of PSVT and might have useful implications for PSVT screening and diagnosis (Wang et al, 2022 ). In the present study, we trained deep learning models with ECGs of healthy patients and concealed AP patients confirmed by electrophysiological study and radiofrequency ablation.…”

Section: Discussionmentioning

confidence: 99%

Deep learning‐mediated prediction of concealed accessory pathway based on sinus rhythmic electrocardiograms

Wang,

Yang,

Bao

et al. 2023

Noninvasive Electrocardiol

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BackgroundConcealed accessory pathway (AP) may cause atrial ventricular reentrant tachycardia impacting the health of patients. However, it is asymptomatic and undetectable during sinus rhythm.MethodsTo detect concealed AP with electrocardiography (ECG) images, we collected normal sinus rhythmic ECG images of concealed AP patients and healthy subjects. All ECG images were randomly allocated to the training and testing datasets, and were used to train and test six popular convolutional neural networks from ImageNet pre‐training and random initialization, respectively.ResultsWe screened 152 ECG recordings in concealed AP group and 600 ECG recordings in control group. There were no statistically significant differences in ECG characteristics between control group and concealed AP group in terms of PR interval and QRS interval. However, the QT interval and QTc were slightly higher in control group than in concealed AP group. In the testing set, ResNet26, SE‐ResNet50, MobileNetV3_large_100, and DenseNet169 achieved a sensitivity rate more than 87.0% with a specificity rate above 98.0%. And models trained from random initialization showed similar performance and convergence with models trained from ImageNet pre‐training.ConclusionOur study suggests that deep learning could be an effective way to predict concealed AP with normal sinus rhythmic ECG images. And our results might encourage people to rethink the possibility of training from random initialization on ECG image tasks.

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Section: Discussionmentioning

confidence: 99%

Deep learning‐mediated prediction of concealed accessory pathway based on sinus rhythmic electrocardiograms

Wang,

Yang,

Bao

et al. 2023

Noninvasive Electrocardiol

Self Cite

View full text Add to dashboard Cite

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Deciphering the Complexities of COVID‐19‐Related Cardiac Complications: Enhancing Classification Accuracy With an Advanced Deep Learning Framework

Benameur,

Sassi,

Ouarda

et al. 2024

Int J Imaging Syst Tech

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The literature has widely described the interaction between cardiac complications and COVID‐19. However, the diagnosis of cardiac complications caused by COVID‐19 using Computed Tomography (CT) images remains a challenge due to the diverse clinical manifestations. To address this issue, this study proposes a novel configuration of Convolutional Neural Network (CNN) for detecting cardiac complications derived from COVID‐19 using CT images. The main contribution of this work lies in the use of CNN techniques in combination with Long Short‐Term Memory (LSTM) for cardiac complication detection. To explore two‐class classification (COVID‐19 without cardiac complications vs. COVID‐19 with cardiac complications), 10 650 CT images were collected from COVID‐19 patients with and without myocardial infarction, myocarditis, and arrhythmia. The information was annotated by two radiology specialists. A total of 0.926 was found to be the accuracy, 0.84 was the recall, 0.82 was the precision, 0.82 was the F1‐score, and 0.830 was the g‐mean of the suggested model. These results show that the suggested approach can identify cardiac problems from COVID‐19 in CT scans. Patients with COVID‐19 may benefit from the proposed LSTM‐CNN architecture's enhanced ability to identify cardiac problems.

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Deep-Learning-Based Detection of Paroxysmal Supraventricular Tachycardia Using Sinus-Rhythm Electrocardiograms

Cited by 2 publications

References 24 publications

Deep learning‐mediated prediction of concealed accessory pathway based on sinus rhythmic electrocardiograms

Deep learning‐mediated prediction of concealed accessory pathway based on sinus rhythmic electrocardiograms

Deciphering the Complexities of COVID‐19‐Related Cardiac Complications: Enhancing Classification Accuracy With an Advanced Deep Learning Framework

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