Biometric Individual Authentication System using High Performance ECG Fiducial Features

A. Benabdallah, A. Djebbari
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Abstract

The Electrocardiographic (ECG) recording is a reliable human heart vital status measurement. Automatic processing of these signals through several computational approaches such as machine learning tools has recently emerged in modern biometric systems. Evaluating ECG potential for biometrical applications has been the purpose of several research papers. In this paper, we developed a new model for individual authentication by detecting high-performance fiducial features of ECG signals. We used SVM and Naive Bayes classifiers to study the impact of high-order statistical features of QRS complexes and R-R intervals within ECG-ID and MIT-BIH Arrhythmia Databases. We integrated these features into a biometric model that we developed. The system reaches an accuracy of 96% up to 99% for the ECG-ID and MIT-BIH databases, respectively. The obtained results approve the reliability of the developed model for robust biometric recognition.
基于高性能心电基准特征的生物识别个人认证系统
心电图(ECG)记录是一种可靠的人体心脏生命状态测量方法。通过机器学习工具等几种计算方法自动处理这些信号最近出现在现代生物识别系统中。评估生物识别应用的ECG电位已成为几篇研究论文的目的。本文通过检测心电信号的高性能基准特征,提出了一种新的个体身份认证模型。我们使用SVM和朴素贝叶斯分类器研究了ECG-ID和MIT-BIH心律失常数据库中QRS复合体和R-R区间的高阶统计特征的影响。我们将这些特征整合到我们开发的生物识别模型中。对于ECG-ID和MIT-BIH数据库,该系统的准确率分别达到96%至99%。实验结果验证了该模型在鲁棒生物特征识别中的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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