Abstract
We present a novel method to characterize the electrocardiogram (ECG) for individual recognition. The method works on analytical and appearance features of the heartbeats. The features are analyzed using the principle of Fisher’s linear discriminant that produces well separated classes in a lower dimension subspace, under the presence of noise and muscle flexure. The biometric experiment is benchmarked using ECG recordings of the publically available QT database. The proposed method achieves the recognition accuracy of 98.9% on the evaluated subjects which is found optimum than the other best known methods.
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Singh, Y.N. (2013). Discriminant Analysis for Identifying Individuals of Electrocardiogram. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2013. Lecture Notes in Computer Science, vol 8251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45062-4_12
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DOI: https://doi.org/10.1007/978-3-642-45062-4_12
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