Research square · 2025
Artificial Intelligence Enhanced Electrocardiogram Analysis for Age and Sex Classification in Youth.
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Zhang Honggen, Zaeri-Amirani Mohammad, Abolfazli Mojtaba, Santhanam Narayana P, Zhang June, Høst-Madsen Anders, Kimata Chieko, Perry James C, Bratincsak Andras
Аннотация
INTRODUCTION: Electrocardiogram (ECG) values vary significantly across age and sex, particularly during childhood and adolescence. While age- and sex-specific ECG standards exist, they often fail to capture complex multi-dimensional relationships and have not been applied in machine learning (ML) enhanced ECG analysis. Accuracy of automated ECG analysis in clinical practice improved significantly by applying ML models, however there is a paucity of such studies in the pediatric population. Our aim was to create age- and sex-specific standards for children by ML modeling.
METHODS: We analyzed 29,408 curated resting 12-lead ECGs from healthy subjects aged 0-21 years using 177 digitized ECG variables combined with various ML models including regression and classification analyses and semi-supervised neural networks. Primary outcome variables were age and sex. Model performance was evaluated using F1-score, AUROC, and confusion matrices across repeated train-test splits.
RESULTS: Support vector machine (SVM) achieved the highest accuracy in modeling both age and sex. Key predictive features included heart rate, PR interval, QRS duration, and T-wave amplitude. Age-group classification achieved an average true positive rate of 60% with SVM, improving to 94% when allowing one-group misclassification. Sex classification reached F1-scores of 0.91 and AUROC of 0.95 in adolescents and young adults, and moderate accuracy in younger children.
DISCUSSION: Traditional supervised ML models can accurately model physiologic ECG changes related to age and sex, outperforming semi-supervised models, particularly in smaller subgroups. These findings support the development of age- and sex-specific ML-enhanced ECG standards to aid future research and clinical applications in pediatric cardiology.
METHODS: We analyzed 29,408 curated resting 12-lead ECGs from healthy subjects aged 0-21 years using 177 digitized ECG variables combined with various ML models including regression and classification analyses and semi-supervised neural networks. Primary outcome variables were age and sex. Model performance was evaluated using F1-score, AUROC, and confusion matrices across repeated train-test splits.
RESULTS: Support vector machine (SVM) achieved the highest accuracy in modeling both age and sex. Key predictive features included heart rate, PR interval, QRS duration, and T-wave amplitude. Age-group classification achieved an average true positive rate of 60% with SVM, improving to 94% when allowing one-group misclassification. Sex classification reached F1-scores of 0.91 and AUROC of 0.95 in adolescents and young adults, and moderate accuracy in younger children.
DISCUSSION: Traditional supervised ML models can accurately model physiologic ECG changes related to age and sex, outperforming semi-supervised models, particularly in smaller subgroups. These findings support the development of age- and sex-specific ML-enhanced ECG standards to aid future research and clinical applications in pediatric cardiology.