Evidence map›Paper›PMID 38862442›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2024

[An intelligent model for classifying supraventricular tachycardia mechanisms based on 12-lead wearable electrocardiogram devices].

H Wang, L Mi, Y Zhang, L Ge, J Lai, T Chen, J Li, X Shi, J Xiu, M Tang and 2 more

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Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

H WangDepartment of Cardiovascular Medicine, Sixth Medical Center of Chinese PLA General Hospital, Beijing 100048, China.
L MiArrhythmia Center of Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China.
Y ZhangSchool of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
L GeDepartment of Cardiovascular Medicine, Sixth Medical Center of Chinese PLA General Hospital, Beijing 100048, China.
J LaiSchool of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
T ChenDepartment of Cardiovascular Medicine, Sixth Medical Center of Chinese PLA General Hospital, Beijing 100048, China.
J LiDepartment of Cardiovascular Medicine, Sixth Medical Center of Chinese PLA General Hospital, Beijing 100048, China.
X ShiDepartment of Cardiovascular Medicine, Sixth Medical Center of Chinese PLA General Hospital, Beijing 100048, China.
J XiuDepartment of Cardiovascular, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
M TangArrhythmia Center of Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China.
W YangSchool of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
J GuoDepartment of Cardiovascular Medicine, Sixth Medical Center of Chinese PLA General Hospital, Beijing 100048, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia (AVNRT) and atrioventricular re-entrant tachycardia (AVRT) using 12-lead wearable electrocardiogram devices.

methodsA total of 356 samples of 12-lead supraventricular tachycardia (SVT) electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model, and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October, 2021 to March, 2023 were selected as the testing set. The changes in electrocardiogram parameters before and during induced tachycardia were compared. Based on multiscale deep neural network, an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated. The 3-lead electrocardiogram signals from Ⅱ, Ⅲ, and Ⅴ

resultsOf the 101 patients with SVT in the testing set, 68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study. The pre-trained model achieved a high area under the precision-recall curve (0.9492) and F1 score (0.8195) for identifying AVNRT in the validation set. The total F1 scores of the lead Ⅱ, Ⅲ, Ⅴ

conclusionThe intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Indexed as

ElectrocardiographyTachycardia, SupraventricularWearable Electronic DevicesDiagnosis, DifferentialFemaleHumansMaleNeural Networks, ComputerTachycardia, Atrioventricular Nodal Reentry12-lead electrocardiogramatrioventricular nodal re-entrant tachycardiaatrioventricular re-entrant tachycardiamultiscale deep neural networksupraventricular tachycardiawearable electrocardiogram

Identifiers

PMID38862442
PMCPMC11166714

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.