Key result
Wavelet-radial basis neural network ECG modeling accurately enables biometric human identification in healthy subjects.
Why the study?
Human identification using normal ECGs can be improved by focusing on the most significant parameters extracted from ECG models.
ECG signals can potentially be modeled using wavelet networks and radial basis neural networks for biometric human identification.
No immediate clinical use for ECG identification; leaves open prospective validation of wavelet network parameters in diverse populations.
This paper deals with human identification using normal ECGs. Precisely, we would like to highlight how one can achieve human identification by considering only the most significant parameters extracted from a model. In this work, parameters are extracted by modelling the ECG using wavelet networks. The radial basis neural network method is then used to classify these parameters. Thus, a useful analysis is performed to evaluate the robustness of the identification. For each recording condition, the proposed technique has been evaluated on a set of ECG signals corresponding to normal subjects. Consequently, very encouraging results have been obtained.
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Chantaf et al. (2010) studied Human identification (normal subjects). ECG modelling using wavelet networks and radial basis neural network classification was evaluated on Human identification robustness. ECG modelling using wavelet networks combined with radial basis neural network classification yielded very encouraging results for human identification in normal subjects.
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