Research on recognition model of vasovagal syncope by artificial intelligence technology based on positive reaction of electrocardiogram

CHANG Chao; LUO Jia-wei; TAN Jian; LIU Gui-zhi; ZHANG Jing-hua; FAN Hao-yi; FU Bing-chao; YAN Shu-mei; ZHANG Yang-hui

doi:10.3969/j.issn.1672-5301.2025.06.004

Abstract：Objective To develop a vascular vagal syncope(VVS)recognition model by artificial intelligence technology based on the VVS positive reaction of electrocardiogram.Methods A retrospective collection of case data was performed for 2278 patients who underwent head-up tilt tests at the First Affiliated Hospital of Zhengzhou University from January 1,2023,to June 30,2024.According to the results of head-up tilt tests and clinical history,the cases were divided into the VVS group(1,048 cases)and the control group(1,230 cases).Data such as the age,gender,baseline blood pressure,baseline heart rate and raw electrocardiogram data were collected.The raw electrocardiogram data were divided into the supine position,position changing,oblique position and VVS positive reaction.Two groups of patients were respectively divided into training set,validation set and test set according to the ratio of 3∶1∶1 by simple random method,and the VVS recognition model was trained.Results The mean age of the VVS group and the control group were(43.55±19.33)years and(41.78±19.84)years,with the male proportion 38.84％and 53.52％,the systolic blood pressure(125.87±18.06)mmHg and(129.14±18.28)mmHg,the diastolic blood pressure(78.29±11.00)mmHg and(79.74±11.48)mmHg,the heart rate(73.59±12.09)beats/min and(74.75±12.88)beats/min,respectively;and all the statistically significant differences(all P＜0.05).After training,validation and testing,the F1 score and AUC of VVS recognition models based on the electrocardiograms of supine position,position changing and oblique position were all 0.53 and 0.59,respectively.The F1 score and AUC of VVS recognition models based on electrocardiogram of VVS positive reaction were 0.78 and 0.89,respectively.Conclusions Based on the VVS positive reaction of the electrocardiogram,the VVS recognition model is successfully constructed using artificial intelligence technology,providing new methods and tools for the diagnosis and clinical intervention of VVS,which is expected to improve the diagnosis and treatment effectiveness of VVS patients.