Identification using ECG Signals

Abstract

Systems that determine identity with individual features are called biometric systems. Today, voice, fingerprint, retina/iris, and facial recognition systems are some of the biometric identification methods. These methods have become replicable with the advancement of technology. Accordingly, Electrocardiogram (ECG) signals are universal, unique, easy to measure, and can only be obtained from living people. For this reason, it can be accepted that ECG is an effective method that can be used to prevent counterfeiting among biometric identification methods. In this study, an algorithm that can make identification via ECG is proposed. Within the scope of the study, the time and time-frequency domain analyzes of the ECG signals obtained from the PhsiyoNet database are performed then various features are determined. These determined features were classified using machine learning methods. The performance of the developed algorithm has been calculated as 100% accuracy, 100% specificity, and 100% sensitivity.