Abstract
The majority of electrocardiogram (ECG) based biometric systems utilize fiducial based features, derived from 11 landmarks (three peaks, two valleys and six onsets and offsets) detected from each ECG heartbeat. The onsets & offsets landmarks may be obscured by a variety of noise sources. Hence, sophisticated algorithms are usually needed for the detection of these points, which in turn increase computational load and also the results may be suboptimal. This work proposes the utilization of a reduced set of 23 features named ’PV set’, which only requires the detection of the five major peaks/valleys instead of all the11 landmarks. The performance of the ’PV set’ is evaluated in comparison with a super set of 36 fiducial features (including PV set) that based on all the 11 landmarks, in addition to IG and RS sets which are subsets of the superset selected based on Rough sets (RS)and information gain (IG) criterion respectively. The evaluation was drawn based on measuring quantities, such as subject identification (SI) accuracy, heartbeat recognition (HR) accuracy and receiver operating characteristic (ROC) curves. The proposed PV set achieved comparable results to the other sets and better results at high noise levels, yielding a reliable and computationally cheaper solution.
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Tantawi, M., Salem, A., Tolba, M.F. (2014). Fiducial Based Approach to ECG Biometrics Using Limited Fiducial Points. In: Hassanien, A.E., Tolba, M.F., Taher Azar, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2014. Communications in Computer and Information Science, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-13461-1_20
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DOI: https://doi.org/10.1007/978-3-319-13461-1_20
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