Prediction of breath‐holding spells based on electrocardiographic parameters using machine‐learning model

Prediction of breath‐holding spells based on electrocardiographic parameters using machine‐learning model

Abstract Background Breath‐holding spells (BHS) are common in infancy and early childhood and may appear like seizures. Factors such as autonomic dysfunction and iron deficiency anemia are thought to contribute to the incidence of BHS. In this study, electrocardiographic (ECG) parameters of patients...

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Bibliographic Details
Main Authors: Mohammad Reza Khalilian, Saeed Tofighi, Elham Zohur Attar, Ali Nikkhah, Mahmoud Hajipour, Mohammad Ghazavi, Sahar Samimi
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:Annals of Noninvasive Electrocardiology
Subjects:
breath holdinig
electrocardiography
machine learning
Online Access:https://doi.org/10.1111/anec.13093

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