ELEKTRA: ELEKTRokardiomatrix Application to biometric identification with Convolutional Neural Networks

ELEKTRA: ELEKTRokardiomatrix Application to biometric identification with Convolutional Neural Networks

Biometric systems are an uprising technique of identification in today’s world. Many different systems have been used in everyone’s daily life in the past years, such as fingerprint, face scan, and others. We propose a new identification method using Elektrokardiogramms (EKGs) converted into a heatmap of a set of aligned R-peaks (heartbeats), forming a matrix called an Elektrokardiomatrix (EKM). We can build a one-against-many identification system using a Convolutional Neural Network (CNN). We have tested our proposal with one main database (the Normal Sinus Rhythm Database (NSRDB)) and two other databases, which are the MIT-BIH Arrhythmia Database (MIT-BIHDB) and the Physikalisch-Technische Bundesanstalt (PTB) Database. With the NSRDB, we have achieved an accuracy of 99.53% and offered a False Acceptance Rate (FAR) of 0.02% and a False Rejection Rate (FRR) of 0.05%. Very similar results were also obtained with the MIT-BIH and PTB databases. We have performed in-depth experimentation to test the efficiency and feasibility of our novel biometric solution. It is remarkable that with a simple CNN, which has only one convolutional layer, a max-pooling operation, and some regularisation, we can identify users with very high performance and low error rates. Consequently, our model does not need very complex architectures to offer high-performance metrics.

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