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- W2920349815 abstract "Developing a high-security expert and intelligent system that cannot be copied, stolen or lost is one of the challenging issues and led to the presence of the “biometric systems”. Electrocardiogram (ECG) becomes an emerging expert biometric modality which captured the attention of many researchers due to its resistant to counterfeiting. However, the low accuracy of ECG-based systems, compared to the other biometric methods, demands more accurate and novel algorithms. This study attempts addressing this issue by providing new information-based algorithms. First, it presents a novel feature extraction methodology based on information theory (IT) for ECG-based human authentication. To this end, we designate a composite feature set using the traditional ECG characteristic points and the IT-based indices, including Cauchy–Schwartz divergence (CSD), Euclidean distance (ED), Cauchy–Schwartz quadratic mutual information (CSQMI), Euclidean distance quadratic mutual information (EDQMI), and cross information potential (CIP). Second, an innovative procedure is proposed for selecting the appropriate features to increase the performance of k-nearest neighbor (kNN) classifier based on the information gain ratio (IGR). We evaluated the effect of changing the classifier parameter and the kernel size of features on classification accuracy. Experimental results show that the use of the CIP-based indices with 2NN leads to higher accuracy rates up to 100%. The highest average rate of accuracy was 97.62±1.92. These results over the 90 participants make our proposed framework a superior scheme compared to the state-of-the-art ECG authentication approaches." @default.
- W2920349815 created "2019-03-11" @default.
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- W2920349815 date "2019-08-01" @default.
- W2920349815 modified "2023-09-26" @default.
- W2920349815 title "Human identification using information theory-based indices of ECG characteristic points" @default.
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- W2920349815 doi "https://doi.org/10.1016/j.eswa.2019.02.038" @default.
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