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Published on: December 11, 2019
Development of an AI based automated analysis of pediatric Apple Watch iECGs
L Teich1, D Franke2, A Michaelis1
1Department for Pediatric Cardiology, University of Leipzig - Heart Center, Leipzig, Germany.
Insights
An artificial intelligence (AI) algorithm was developed to automatically interpret pediatric electrocardiograms (iECGs) from the Apple Watch. This AI shows promise for improving pediatric iECG analysis, though further training is needed for complex cases.
Area of Science:
- Cardiology
- Artificial Intelligence
- Pediatric Health
Background:
- Apple Watch can record event-based electrocardiograms (iECG) in children.
- Automatic heart rhythm classification by Apple Watch software is inaccurate for pediatric iECGs.
- Current iECG analysis in children requires interpretation by a pediatric cardiologist.
Purpose of the Study:
- To develop an artificial intelligence (AI) based algorithm for automatic interpretation of pediatric Apple Watch iECGs.
- To improve the accuracy and efficiency of pediatric iECG analysis.
Main Methods:
- An AI algorithm was designed and trained using prerecorded, manually classified iECGs.
- The algorithm was evaluated on a prospective cohort of 48 pediatric patients at Leipzig Heart Center.
- AI iECG evaluation was compared against the gold standard of 12-lead ECG interpretation by a pediatric cardiologist.
Main Results:
- The AI algorithm achieved a specificity of 96.7% and a sensitivity of 66.7% for classifying normal sinus rhythm.
- This study presents the first AI algorithm for automatic heart rhythm classification of pediatric iECGs.
Conclusions:
- The developed AI algorithm provides a foundation for future advancements in AI-based pediatric iECG analysis.
- Further AI training with more data is essential to establish the AI-based iECG analysis as a clinical tool for complex pediatric patients.
Introduction:
The Apple Watch valuably records event-based electrocardiograms (iECG) in children, as shown in recent studies by Paech et al. In contrast to adults, though, the automatic heart rhythm classification of the Apple Watch did not provide satisfactory results in children. Therefore, ECG analysis is limited to interpretation by a pediatric cardiologist. To surmount this difficulty, an artificial intelligence (AI) based algorithm for the automatic interpretation of pediatric Apple Watch iECGs was developed in this study.
Methods:
A first AI-based algorithm was designed and trained based on prerecorded and manually classified i.e., labeled iECGs. Afterward the algorithm was evaluated in a prospectively recruited cohort of children at the Leipzig Heart Center. iECG evaluation by the algorithm was compared to the 12-lead-ECG evaluation by a pediatric cardiologist (gold standard). The outcomes were then used to calculate the sensitivity and specificity of the Apple Software and the self-developed AI.
Results:
The main features of the newly developed AI algorithm and the rapid development cycle are presented. Forty-eight pediatric patients were enrolled in this study. The AI reached a specificity of 96.7% and a sensitivity of 66.7% for classifying a normal sinus rhythm.
Conclusion:
The current study presents a first AI-based algorithm for the automatic heart rhythm classification of pediatric iECGs, and therefore provides the basis for further development of the AI-based iECG analysis in children as soon as more training data are available. More training in the AI algorithm is inevitable to enable the AI-based iECG analysis to work as a medical tool in complex patients.
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