Identification using ECG Signals

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, Electr...

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Bibliographic Details
Main Authors: KILICER, Elif Cansu, AY, Sevval, AKSAHIN, ve Mehmet Feyzi
Format: Conference Proceeding
Language:English
Subjects:
Authentication
continuous wavelet transform
Continuous wavelet transforms
ECG
Electrocardiography
machine learning
Matlab
segmentation
Sensitivity
Signal processing
Time-frequency analysis
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Summary: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.
ISSN:2687-7783
DOI:10.1109/TIPTEKNO50054.2020.9299305