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ECG-Based Patient Identification: A Comprehensive Evaluation Across Health and Activity Conditions

Caterina Fuster-Barceló, Carmen Cámara, Pedro Peris-López

TL;DR

A convolutional neural network is employed to classify users based on electrocardiomatrices, a specific type of image derived from ECG signals, which demonstrates the method’s significant advancement in subject identification within healthcare systems.

Abstract

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.

ECG-Based Patient Identification: A Comprehensive Evaluation Across Health and Activity Conditions

TL;DR

A convolutional neural network is employed to classify users based on electrocardiomatrices, a specific type of image derived from ECG signals, which demonstrates the method’s significant advancement in subject identification within healthcare systems.

Abstract

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.
Paper Structure (14 sections, 1 equation, 4 figures, 11 tables, 2 algorithms)

This paper contains 14 sections, 1 equation, 4 figures, 11 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Materials and Methods
  4. Data
  5. Patient identification
  6. Creation of the EKM
  7. Convolutional Neural Network for patient identification
  8. Results
  9. Healthy Subjects
  10. Patients with Arrhythmia and healthy subjects
  11. Patients with various CVD and healthy subjects
  12. Subjects performing activities
  13. Discussions & Conclusions
  14. State of the art regarding the identification of users with ECGs

Figures (4)

  • Figure 1: Elektrokardiomatrix or EKM, image used to perform patient identification
  • Figure 2: EKMs fed into the CNN depending on the bpf.
  • Figure 3: Pipeline of the methodology followed in ELEKTRA2022 from EKG processing (noise reduction, peak detection, etc) to the creation of the EKM Database, training and validation of a CNN and final Patient Identification.
  • Figure 4: CNN Architecture. C being the number of categories or classes.