Abstract
Identification based on electrocardiogram (ECG) is an emerging hot spot in biometric identification. Feature selection is one of the key research points on it. In the paper, features are firstly calculated from fiducial points of ECG. Secondly, the initial feature set is composed of amplitude, interval, slope, area and some clinical indexes. Thirdly, a feature selection strategy is proposed. The strategy uses stepwise discriminant analysis to calculate the contribution (weight) of each feature for ECG identification. On the basis of contribution sorting, accumulative recognition rate is calculated. Furthermore, a key feature subset for ECG identification is acquired when accumulative recognition rate reaches a steady level. Fourthly, the identification procedure works on key feature subset. ECG data from both PTB and laboratory is used in experiments. Experimental results show that the identification accuracy of the two data sets is 99.7% and 94.8% respectively.
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Chen, C., Zheng, G., Dai, M. (2014). The Study of Feature Selection Strategy in Electrocardiogram Identification. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_56
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DOI: https://doi.org/10.1007/978-3-319-12484-1_56
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12483-4
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