ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 2; p. 937
• Main Authors: Camara, Carmen, Peris-Lopez, Pedro, Safkhani, Masoumeh, Bagheri, Nasour
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.01.2023; MDPI
• Subjects: Algorithms; Arrhythmias, Cardiac; Biometric Identification - methods; Biometrics; Biometry; Cardiology; Cardiovascular disease; Datasets; deep learning; ECG; Electrocardiography; Electrocardiography - methods; gramian angular field; Humans; Identification systems; Medical equipment; Medical research; Neural Networks, Computer; Smartwatches; Time series; Wavelet transforms; Wearable computers; wearables; ECG; biometrics; deep learning; gramian angular field; wearables
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23020937

In the last decade, biosignals have attracted the attention of many researchers when designing novel biometrics systems. Many of these works use cardiac signals and their representation as electrocardiograms (ECGs). Nowadays, these solutions are even more realistic since we can acquire reliable ECG records by using wearable devices. This paper moves in that direction and proposes a novel approach for an ECG identification system. For that, we transform the ECG recordings into Gramian Angular Field (GAF) images, a time series encoding technique well-known in other domains but not very common with biosignals. Specifically, the time series is transformed using polar coordinates, and then, the cosine sum of the angles is computed for each pair of points. We present a proof-of-concept identification system built on a tuned VGG19 convolutional neural network using this approach. We confirm our proposal’s feasibility through experimentation using two well-known public datasets: MIT-BIH Normal Sinus Rhythm Database (subjects at a resting state) and ECG-GUDB (individuals under four specific activities). In both scenarios, the identification system reaches an accuracy of 91%, and the False Acceptance Rate (FAR) is eight times higher than the False Rejection Rate (FRR).