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

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 with BHS...

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Bibliographic Details
Published in:Annals of noninvasive electrocardiology Vol. 29; no. 1; pp. e13093 - n/a
Main Authors: Khalilian, Mohammad Reza, Tofighi, Saeed, Attar, Elham Zohur, Nikkhah, Ali, Hajipour, Mahmoud, Ghazavi, Mohammad, Samimi, Sahar
Format: Journal Article
Language:English
Published: United States John Wiley & Sons, Inc 01.01.2024
John Wiley and Sons Inc
Wiley
Subjects:
Age
Algorithms
Anemia
Arrhythmia
Arrhythmias, Cardiac
Autonomic Nervous System Diseases
Breath Holding
breath holdinig
Case-Control Studies
Child
Child, Preschool
Children
Decision trees
EKG
Electrocardiography
Heart rate
Humans
Infant
Iron deficiency
Learning algorithms
Machine learning
Nutrient deficiency
Original
Parameters
Pediatrics
Programming languages
Regression analysis
Regression models
Seizures
Software
Statistical analysis
Variables
machine learning
breath holdinig
electrocardiography
ISSN:1082-720X, 1542-474X, 1542-474X
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Summary: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 with BHS were compared to those of healthy, normal children. Logistic regression and machine‐learning (ML) models were then created to predict these spells based on ECG characteristics. Methods In this case–control study, 52 BHS children have included as the case and 150 healthy children as the control group. ECG was taken from all children along with clinical examinations. Multivariate logistic regression model was used to predict BHS occurrence based on ECG parameters. ML model was trained and validated using the Gradient‐Boosting algorithm, in the R programming language. Results In BHS and control groups, the average age was 11.90 ± 6.63 and 11.33 ± 6.17 months, respectively (p = .58). Mean heart rate, PR interval, and QRS interval on ECGs did not differ significantly between the two groups. BHS patients had significantly higher QTc, QTd, TpTe, and TpTe/QT (all p‐values < .001). Evaluation of the ML model for prediction of BHS, fitting on the testing data showed AUC, specificity, and sensitivity of 0.94, 0.90, and 0.94 respectively. Conclusion There are repolarization changes in patients with BHS, as the QTc, QTd, TpTe, and TpTe/QT ratio were significantly higher in these patients, which might be noticeable for future arrhythmia occurrence. In this regard, we developed a successful ML model to predict the possibility of BHS in suspected subjects. In this case–control study, we initially compared different electrocardiographic (ECG) characteristics of children with a history of the breath‐holding spell (BHS) versus normal subjects, then we developed and evaluated a machine‐learning model to predict BHSs in children with such suspected attacks, based on their ECG parameters.
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ISSN:1082-720X
1542-474X
1542-474X
DOI:10.1111/anec.13093