Open AccessECG analysis for human recognition using non‐fiducial methods
Author(s) -
Srivastva Ranjeet,
Singh Yogendra Narain
Publication year - 2019
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
ISSN - 2047-4946
DOI - 10.1049/iet-bmt.2018.5093
Subject(s) - discrete cosine transform , computer science , dimensionality reduction , pattern recognition (psychology) , biometrics , linear discriminant analysis , artificial intelligence , principal component analysis , discrete fourier transform (general) , robustness (evolution) , normalization (sociology) , hadamard transform , transformation (genetics) , fourier transform , mathematics , fourier analysis , fractional fourier transform , mathematical analysis , biochemistry , chemistry , sociology , anthropology , image (mathematics) , gene
The electrocardiogram (ECG) has emerged as a new biometric for human recognition due to its robustness against fraudulent attacks. This article presents a novel method of ECG biometric for human recognition using autocorrelation (AC) followed by one of the three transformation techniques, i.e. discrete cosine transform (DCT), discrete Fourier transform (DFT), and Walsh–Hadamard transform (WHT). The effectiveness of these transformations is evaluated on the dimensionality reduction techniques i.e. principal component analysis and linear discriminant analysis (LDA). Thus, the systems prepared by different combinations of transformations and dimensionality reduction techniques are evaluated on publically available databases of Physionet. The authentication and identification accuracies achieved by these systems are found the best on DFT and LDA combination. The authentication performance is reported to 99.98% (99.83%), whereas the average rank classification accuracy is reported to 100% (97%) on QT database (MIT‐BIH arrhythmia database) of Physionet.