Abstract
From the last one and a half decades, the electrocardiogram (ECG) has emerged as a new modality for human identification. The research shows that the people heartbeats recorded using diagnostic method called ECG exhibit discriminatory features that can distinguish themselves. The ECG as a biometric inherently provides liveness detection and robustness against falsification. This paper presents a novel method of ECG analysis for human identification using Fourier and linear discriminant analysis, which does not require detection of fiducial points of ECG wave. The method utilizes autocorrelation coefficients of filtered ECG signal, to extract significant features of it. The performance of the proposed method is evaluated on MIT-BIH arrhythmia and QT database of physionet. The experimental results show the equal error rate (EER) of 0.17% and 0.03% on MIT-BIH arrhythmia and QT database, respectively that outperform the other methods on these databases.
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Srivastva, R., Singh, Y.N. (2018). Identifying Individuals Using Fourier and Discriminant Analysis of Electrocardiogram. In: Ghosh, D., Giri, D., Mohapatra, R., Savas, E., Sakurai, K., Singh, L. (eds) Mathematics and Computing. ICMC 2018. Communications in Computer and Information Science, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-0023-3_27
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DOI: https://doi.org/10.1007/978-981-13-0023-3_27
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