ECG-based identity recognition via deterministic learning

ABSTRACT

In this paper, a novel method based on electrocardiogram (ECG) signals is proposed for identity recognition. A unique feature called dynamics, which is fundamentally different from features used in literature, is extracted from ECG signals and used for identity recognition. Deterministic learning, a recently proposed machine learning approach, is used to model the dynamics of training ECG signals. A set of estimators employing the modelling results of training ECG signals is constructed. Through comparing the test ECG signal (measured from a subject to be recognized) with the estimators, a set of errors can be obtained and used to measure the similarity between the test and the training ECG signals. The test ECG signal is recognized in accordance with the smallest error, and then the subject can be recognized rapidly. Experimental results indicate that the proposed method is reliable and efficient for identity recognition.

KEYWORDS:

• ECG
• identity recognition
• pattern recognition
• dynamics
• deterministic learning

Additional information

Funding

This work was supported by the Natural Science Foundation of Guangdong Province, China [grant number 2017A030310493]; the Science and Technology Program of Guangzhou, China, [grant number 201704020078]; the National R&D Program for Major Research Instruments [grant number 61527811]