Key result
The proposed ECG-based biometric authentication system using a decision tree regression model achieved up to 92.7% identification accuracy when applying higher data quality criteria.
Why the study?
Traditional authentication systems risk forgetfulness, loss, and theft, prompting the development of biometric methods such as ECG-based authentication.
An ECG-based biometric authentication system using machine learning can achieve up to 92% identification accuracy, offering a potential alternative to traditional authentication methods.
ECG biometrics may support secure authentication alternatives; leaves open prospective clinical validation and security testing.
Traditional authentication systems use alphanumeric or graphical passwords, or token-based techniques that require “something you know and something you have”. The disadvantages of these systems include the risks of forgetfulness, loss, and theft. To address these shortcomings, biometric authentication is rapidly replacing traditional authentication methods and is becoming a part of everyday life. The electrocardiogram (ECG) is one of the most recent traits considered for biometric purposes. In this work we describe an ECG-based authentication system suitable for security checks and hospital environments. The proposed system will help investigators studying ECG-based biometric authentication techniques to define dataset boundaries and to acquire high-quality training data. We evaluated the performance of the proposed system and found that it could achieve up to the 92% identification accuracy. In addition, by applying the Amang ECG (amgecg) toolbox within MATLAB, we investigated the two parameters that directly affect the accuracy of authentication: the ECG slicing time (sliding window) and the sampling time period, and found their optimal values.
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Alkeem et al. (2019) studied Biometric Authentication (n=100). Decision Tree regression-based ECG authentication system was evaluated on Identification accuracy. The proposed ECG-based biometric authentication system using a decision tree regression model achieved up to 92.7% identification accuracy when applying higher data quality criteria.
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