A novel convolutional neural network structure for differential diagnosis of wide QRS complex tachycardia. (March 2023) - Fayyazifar, Najmeh; Dwivedi, Girish; Suter, David; Ahderom, Selam; Maiorana, Andrew; Clarkin, Owen; Balamane, Saad; Saha, Nishita; King, Benjamin; Green, Martin S.; Golian, Mehrdad; Chow, Benjamin J.W.
Highlights: Accurate rhythm diagnosis on electrocardiograms (ECG) is critical in patients presenting with wide QRS complex tachycardia (WCT) arrhythmia. Real-time visual interpretation of ECG of complex arrhythmias is difficult and requires expertise. We designed a convolutional neural model through a neural architecture search that could accurately classify WCT into those that are ventricular in origin (87.5%) or supraventricular tachycardia (91.7%). Our model was also shown to be useful for arrhythmia diagnosis from ECG data generated by both 12 leads as well as single-lead devices. Our model can potentially be implemented in real clinical settings to assist physicians in more accurate and timely diagnosis of WCTs. Abstract: Background and objectives: 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). Methods: 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. Results: 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. Conclusions: 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.