ElectroCardioGuard: Preventing Patient Misidentification in Electrocardiogram Databases through Neural Networks

9 Jun 2023  ·  Michal Seják, Jakub Sido, David Žahour ·

Electrocardiograms (ECGs) are commonly used by cardiologists to detect heart-related pathological conditions. Reliable collections of ECGs are crucial for precise diagnosis. However, in clinical practice, the assignment of captured ECG recordings to incorrect patients can occur inadvertently. In collaboration with a clinical and research facility which recognized this challenge and reached out to us, we present a study that addresses this issue. In this work, we propose a small and efficient neural-network based model for determining whether two ECGs originate from the same patient. Our model demonstrates great generalization capabilities and achieves state-of-the-art performance in gallery-probe patient identification on PTB-XL while utilizing 760x fewer parameters. Furthermore, we present a technique leveraging our model for detection of recording-assignment mistakes, showcasing its applicability in a realistic scenario. Finally, we evaluate our model on a newly collected ECG dataset specifically curated for this study, and make it public for the research community.

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Results from the Paper

 Ranked #1 on ECG Patient Identification (gallery-probe) on PTB-XL (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
ECG Patient Identification (gallery-probe) CODE-15% ElectroCardioGuard Accuracy 60.3% # 1
ECG Patient Identification (gallery-probe) PTB Diagnostic ECG Database ElectroCardioGuard Accuracy 77.0% # 1
ECG Patient Identification (gallery-probe) PTB-XL ElectroCardioGuard Accuracy 58.3% # 1