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Full-Text Articles in Other Computer Sciences
Cardiogpt: An Ecg Interpretation Generation Model, Guohua Fu, Jianwei Zheng, Islam Abudayyeh, Chizobam Ani, Cyril Rakovski, Louis Ehwerhemuepha, Hongxia Lu, Yongjuan Guo, Shenglin Liu, Huimin Chu, Bing Yang
Cardiogpt: An Ecg Interpretation Generation Model, Guohua Fu, Jianwei Zheng, Islam Abudayyeh, Chizobam Ani, Cyril Rakovski, Louis Ehwerhemuepha, Hongxia Lu, Yongjuan Guo, Shenglin Liu, Huimin Chu, Bing Yang
Mathematics, Physics, and Computer Science Faculty Articles and Research
Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai …
A High-Precision Machine Learning Algorithm To Classify Left And Right Outflow Tract Ventricular Tachycardia, Jianwei Zhang, Guohua Fu, Islam Abudayyeh, Magdi Yacoub, Anthony Chang, William Feaster, Louis Ehwerhemuepha, Hesham El-Askary, Xianfeng Du, Bin He, Mingjun Feng, Yibo Yu, Binhao Wang, Jing Liu, Hai Yao, Hulmin Chu, Cyril Rakovski
A High-Precision Machine Learning Algorithm To Classify Left And Right Outflow Tract Ventricular Tachycardia, Jianwei Zhang, Guohua Fu, Islam Abudayyeh, Magdi Yacoub, Anthony Chang, William Feaster, Louis Ehwerhemuepha, Hesham El-Askary, Xianfeng Du, Bin He, Mingjun Feng, Yibo Yu, Binhao Wang, Jing Liu, Hai Yao, Hulmin Chu, Cyril Rakovski
Mathematics, Physics, and Computer Science Faculty Articles and Research
Introduction: Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.
Methods: We randomly sampled training, validation, and testing …