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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
Published on: December 11, 2019
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Explainable artificial intelligence in electrocardiography: A systematic review
Amirsajjad Taleban1, Rodney Sparapani2, Patrick Noffke3
1Health Informatics Program, Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Biomedical Signal Processing and Control
|April 9, 2026
Summary
Explainable AI (Artificial Intelligence) methods for electrocardiography (ECG) show promise but require further validation. Techniques highlighting model attention to physiological ECG intervals are most effective for clinical trust and adoption.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Signal Processing
Background:
- Electrocardiography (ECG) is crucial for diagnosing heart conditions.
- Deep learning models achieve high accuracy in ECG interpretation but lack transparency.
- Explainable AI (XAI) is essential for clinical trust and regulatory approval of AI in cardiology.
Purpose of the Study:
- To systematically review and evaluate explainable AI techniques specifically for ECG interpretation.
- To identify the most effective XAI methods for enhancing trust and clinical adoption of AI in cardiology.
Main Methods:
- Systematic literature review following PRISMA guidelines.
- Screened 380 records across six databases, including 45 peer-reviewed studies.
- Analyzed diverse XAI methods: perturbation-based, gradient-based, intrinsically interpretable, sequence-aware, and counterfactual.
Main Results:
- Perturbation-based XAI methods are suboptimal for ECG due to their inability to capture temporal dependencies.
- Sequence-aware methods revealing model attention to physiological ECG intervals (P wave, QRS complex, ST segment) show superior performance.
- Current XAI methods for ECG are fragmented, lack robust validation, and face challenges in stability and efficiency.
Conclusions:
- XAI methods that align with physiological ECG features are most promising for clinical translation.
- Further development requires open, multi-institutional benchmarks and clinician-in-the-loop validation.
- Accelerating clinical integration of XAI in ECG interpretation necessitates addressing stability, efficiency, and regulatory readiness.
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