ECG based biometric identification using one-dimensional local difference pattern
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Université de M'sila
Abstract
In this work, an enhanced version of 1D local binary pattern is proposed, for the derivation of the most relevant
features for ECG-based human recognition. Generally, ECG signal characteristics by nature impose some notable
challenges, mostly related to its sensitivity to noises, artifacts, behavioral and emotional disorders and other
variability factors. To deal with this critical issue, we use a One-dimensional Local Difference Pattern (1D-LDP)
operator to extract the discriminating statistical features from ECG by using the difference between consecutive
neighboring samples to capture both the micro and macro patterns information in the heartbeat activity while
reducing the local and global variation occurred in ECG over time. To verify its robustness, K-nearest neighbors
(KNN) linear support vector machine (SVM) and neural network were performed as the classifier models in this
work. Obtained results show that the 1D-LDP operator clearly outperforms existing 1D-LBP variants on MIT-BIH
Normal Sinus Rhythm and ECG-ID database