A Multiscale Autoregressive Model-Based Electrocardiogram Identification Method

With increasing security and privacy requirements, electrocardiogram (ECG)-based biometric human identification and authentication is gaining extensive attention. This paper aims to solve three major problems: stable identity feature is hard extracted from the inferior quality ECG, the performance of authentication system falls down when the size of registered sample set increases, and the authentication system needs to retrain when a new registered identity is added. To improve the robustness of identity feature, this paper proposed a multiscale feature extraction method using a multiscale autoregressive model (MSARM). First, the performance of multiscale feature was tested by simple matching method based on Chi-square distance in identification system. The test was performed on self-built SIAT-ECG and public PTB databases, which contain 146 and 100 (50 healthy volunteers and 50 patients with myocardial infarction) individuals, respectively. The recognition rate exceeded 93.15% for both databases in identification scenario. The results revealed that the MSARM has more excellent performance than other feature extraction methods. Then, this paper proposed a combination classifier method with one-to-one structure in authentication mode. It yielded a true rejection rate (TRR) of 98.99% and true acceptance rate (TAR) of 95.04% when registered sample set contains 140 individuals from SIAT-ECG database. Therefore, the proposed MSARM and combination classifier not only significantly improve the accuracy but also enhance the practicability of ECG-based biometric systems.