Abstract— In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Following the pre-processing step, the ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. After successful QRS detection, various temporal, amplitude and AR coefficients are extracted and used as an input to a classifier in order to identify the individuals. In this work, proposed system has been tested using records from three different publicly available ECG databases. Signal pre-processing techniques, applied parameter extraction methods and some intermediate and final classification results are presented in this paper.
Index Terms— ECG, AR model, biometric, extraction.
B. Vuksanovic and M. Alhamdiis are with the University of Portsmouth, Anglesea Building, Anglesea Road, Portsmouth PO1 3DJ, UK (e-mail: branislav.vuksanovic@port.ac.uk, mustafa.alhamdi@port.ac.uk).
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Cite: B. Vuksanovic and M. Alhamdi, " Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic and AR Modeling Extracted Parameters," International Journal of Information and Electronics Engineering vol. 4, no. 6, pp. 428-433, 2014.
