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Published on: December 11, 2019
Artificial intelligence in electrocardiogram signals for sudden cardiac death prediction: a systematic review and
Shuang He1, Ming Du1, Zhao Wang1
1Department of Cardiology, People's Hospital of Liaoning Province, Shenyang, Liaoning, China.
Purpose:
The study aimed to evaluate the diagnostic performance of artificial intelligence (AI) in detecting sudden cardiac death on electrocardiogram (ECG).
Methods:
We systematically searched PubMed, Web of Science, Embase, and IEEE Xplore for studies published through April 2025 evaluating AI models for ECG-based sudden cardiac death detection, using expert consensus or database records as the reference standard. A bivariate random-effects model generated pooled sensitivity and specificity estimates. Heterogeneity was quantified via I2 and τ2 statistics. Study quality was appraised using the revised QUADAS-2 tool, with evidence certainty graded via the GRADE assessment.
Results:
Out of 958 initially identified studies, 27 studies with 2613 patients and images were ultimately included for the final analysis. For heart rate variability, AI demonstrated a sensitivity of 0.90 (95% CI: 0.86-0.92) and specificity of 0.91 (95% CI: 0.83-0.96), with an AUC of 0.93 (95% CI: 0.91-0.95). For ECG signal segmentation, AI demonstrated a sensitivity of 0.96 (95% CI: 0.92-0.98) and specificity of 0.99 (95% CI: 0.94-1.00), with an AUC of 0.99 (95% CI: 0.98-1.00). For direct input of ECG lead signals, AI demonstrated a sensitivity of 0.87 (95% CI: 0.61-0.97) and specificity of 0.91 (95% CI: 0.75-0.97), with an AUC of 0.95 (95% CI: 0.93-0.97).
Conclusions:
This meta-analysis indicates that AI-based ECG analysis shows potential for SCD prediction. However, the summary estimates are derived from highly heterogeneous studies and should not be considered benchmarks for clinical performance. The current evidence remains preliminary and derived from idealized research settings, underscoring the need for prospective, multicenter studies with standardized methodologies to establish generalizability and clinical applicability.
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