Key result
ECG-based CNN achieves ~97% patient identification accuracy in patients with cardiovascular disease.
Why the study?
Although cardiac signals are viable biometric modalities, existing patient identification methods have gaps regarding performance across diverse cardiovascular conditions and physical activity levels.
Can a convolutional neural network using electrocardiomatrices accurately identify patients across different health and activity conditions?
Can a convolutional neural network using electrocardiomatrices accurately identify patients across different health and activity conditions?
A CNN-based approach using ECG-derived electrocardiomatrices provides highly accurate patient identification across healthy and cardiovascular disease populations, demonstrating potential for biometric use in healthcare systems.
ECG biometrics via CNN may aid secure patient ID in healthcare systems; leaves open large-scale prospective validation before adoption.
Over the course of the past two decades, a substantial body of research has substantiated the viability of utilising cardiac signals as a biometric modality. This paper presents a novel approach for patient identification in healthcare systems using electrocardiogram signals. A convolutional neural network (CNN) is employed to classify users based on electrocardiomatrices, a specific type of image derived from ECG signals. The proposed identification system is evaluated in multiple databases, achieving up to 99.84% accuracy on healthy subjects, 97.09% on patients with cardiovascular diseases, and 97.89% on mixed populations including both healthy and arrhythmic patients. The system also performs robustly under varying activity conditions, achieving 91.32% accuracy in scenarios involving different physical activities. These consistent and reliable results, with low error rates such as a FAR of 0.01% and FRR of 0.157% in the best cases, demonstrate the method’s significant advancement in subject identification within healthcare systems. By considering patients’ cardiovascular conditions and activity levels, the proposed approach addresses gaps in the existing literature, positioning it as a strong candidate for practical applications in real-world healthcare settings.
No takes yet. Share an insight, caveat, or question.
Fuster-Barceló et al. (2024) studied Healthy subjects and patients with cardiovascular diseases. Convolutional neural network (CNN) using electrocardiomatrices was evaluated on Patient identification accuracy. A convolutional neural network using electrocardiomatrices achieved up to 99.84% identification accuracy in healthy subjects and 97.09% in patients with cardiovascular diseases.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: