  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 percent 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.
