Abstract
The electrocardiogram (ECG) signal expresses unique cardiac features among individuals. This paper proposes a novel method to human identification using ECG. The proposed method utilizes a band-pass filter for quality check and autocorrelation (AC) for feature extraction. Furthermore, the Walsh–Hadamard transform (WHT) is used for feature transformation. To get cost- and time-efficient classification performance, the dimensionality of feature vector is reduced using linear discriminant analysis (LDA). Experimental results show the best identification rate of 95 and 97% over MIT-BIH arrhythmia database and QT database, respectively.
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The authors would like to thank the anonymous reviewers and the editor for their feedback and useful suggestions.
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Srivastva, R., Singh, Y.N. (2018). ECG Biometric Analysis Using Walsh–Hadamard Transform. In: Kolhe, M., Trivedi, M., Tiwari, S., Singh, V. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 38. Springer, Singapore. https://doi.org/10.1007/978-981-10-8360-0_19
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DOI: https://doi.org/10.1007/978-981-10-8360-0_19
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