ElectroCardioGuard: Preventing Patient Misidentification in Electrocardiogram Databases through Neural Networks
This addresses a critical issue for cardiologists and healthcare providers by preventing diagnostic errors due to misassigned ECG recordings, though it is an incremental improvement in patient identification methods.
The paper tackles the problem of patient misidentification in electrocardiogram (ECG) databases by proposing a neural network model that determines if two ECGs are from the same patient, achieving state-of-the-art performance on PTB-XL with 760x fewer parameters.
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.