ElectroCardioGuard: Preventing Patient Misidentification in Electrocardiogram Databases through Neural Networks

TL;DR

ElectroCardioGuard introduces an efficient neural network model that accurately verifies if two ECGs are from the same patient, helping prevent misidentification errors in clinical databases with minimal computational resources.

Contribution

This work presents a novel, lightweight neural network for patient verification in ECGs, achieving state-of-the-art accuracy and generalization with significantly fewer parameters than existing models.

Findings

Achieved state-of-the-art performance on PTB-XL dataset

Utilized 760x fewer parameters than comparable models

Demonstrated effective detection of recording-misassignment in real scenarios

Abstract

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.