ECG Based Biometric by Superposition Matrix in Unrestricted Status.

The paper proposed an Electrocardiogram (ECG) feature extraction method for biometric. It relied on ECG superposition number matrix built by several single heartbeat ECG data. The target of the study was to find stable features of the ECG signal under unrestricted status for biometric. By matrix segmentation and similarity comparison, the stable feature distribution was gotten, and stable feature sets were also constructed. 13 volunteers' ECG data collected by self-made ECG device in different status were gotten, the collecting period was lasting for half year; 28 healthy individuals' ECG data under calm status were also collected; Besides that, 14 subjects' ECG data in MIT-BIH were also involved in study. From the result of experiments, the average True Positive Rate (TPR) reached 83.21%, 83.93% and 80% on MIT data set, ECG data set in calm status and ECG data in different status respectively. It is also found that along with the increasing amount of ECG single heartbeat used to build superposition matrix, the stable features of one's ECG were gradually revealed and this helped ECG based biometric effectively.