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• W4313202085 abstract "Cardiac arrhythmias are a significant cause of morbidity and mortality in patients with cardiovascular disease. Accurate rhythm diagnosis is critical in patients presenting with wide QRS complex tachycardia (WCT). Real-time visual interpretation of electrocardiograms (ECG) of complex arrhythmias is difficult and requires expertise. We designed a convolutional neural network (CNN) that could accurately classify WCT into those that are ventricular in origin (ventricular tachycardia (VT)) or supraventricular tachycardia with aberrancy (SVT). A total of 3065 patients with wide complex ECGs were screened (415 with VT and 2650 with SVT). A CNN model was designed through a Neural Architecture Search (NAS) method. This CNN consisted of a stem convolution layer and five cells, each cell containing separable-convolution and dilated-separable-convolution layers. Using 5-fold cross-validation and executing algorithm for five independent runs (with five different seeds), the proposed CNN model achieved a detection accuracy of 87.5 ± 0.0025 and 91.7 %±0.0004 for VT and SVT, respectively. The total sensitivity, specificity, positive predictive value, negative predictive value and F1-score of the CNN model were 88.50 %, 88.50 %, 88.54 %, 88.54 %, and 88.49 %, respectively. In a cohort of patients presenting with a WCT, our CNN model achieved an accuracy of 87.5% and 91.7% to correctly diagnose VT and SVT, respectively. This model has the potential of being used in real-time settings and to assist physicians with interpretation and decision making." @default.
• W4313202085 created "2023-01-06" @default.
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• W4313202085 date "2023-03-01" @default.
• W4313202085 modified "2023-10-16" @default.
• W4313202085 title "A novel convolutional neural network structure for differential diagnosis of wide QRS complex tachycardia" @default.
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• W4313202085 doi "https://doi.org/10.1016/j.bspc.2022.104506" @default.
• W4313202085 hasPublicationYear "2023" @default.
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