Human identification by quantifying similarity and dissimilarity in electrocardiogram phase space

Authors:
Shih-Chin Fang;Hsiao-Lung Chan
Affiliations:
Department of Electrical Engineering, Chang Gung University, 259 Wenhwa 1st Road, Kweishan, Taoyuan 333, Taiwan and Department of Neurology, Cardinal Tien Hospital Yung Ho Branch, Taipei, Taiwan;Department of Electrical Engineering, Chang Gung University, 259 Wenhwa 1st Road, Kweishan, Taoyuan 333, Taiwan
Venue:
Pattern Recognition
Year:
2009

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Abstract

Specific patterns of electrocardiogram (ECG), along with other biometrics, have recently been used to recognize a person. Most ECG-based human identification methods rely on the reduced features derived from ECG characteristic points and supervised classification. However, detecting characteristic points is an arduous procedure, particularly at low signal-to-noise ratios. The supervised classifier requires retraining when a new person is included in the group. In the present study, we propose a novel unsupervised ECG-based identification method based on phase space reconstruction of one-lead or three-lead ECG, saving from picking up characteristic points. Identification is performed by inspecting similarity or dissimilarity measure between ECG phase space portraits. Our results in a 100-subject group showed that one-lead ECG reached identification rate at 93% accuracy and three-lead ECG acquired 99% accuracy.