基于深度神经网络的心电信号识别

Rudresh T. K., M. S. H., Shameem Banu L
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摘要

近年来,生物识别技术被广泛应用于民用领域,主要用于用户的身份验证或识别。大多数电子系统已经提出采用不同的行为或生理的人类签名来自动识别或验证用户。目前,面向心电图(ECG)的生物识别系统还处于探索阶段。每个人的心电信号的行为都是不同的。由于ECG是一种只存在于活人身上的独特生理信号,因此它被用于新的生物识别系统中,用于识别人,并对抗欺诈和伪造攻击。传统的心电信号检测方法大都局限于对心电信号中几个点的检测。本文的贡献在于利用心电信号增强了新的人物识别模型结构。首先,对三个基准源采集的心电信号进行预处理,利用低通滤波(LPF)方法去除噪声。进一步,采用经验模态分解(EMD)对信号进行分解。特征选择是分类增强的重要组成部分,采用主成分分析(PCA)作为有效的特征提取方法,从信号中提取出最重要的特征。最后,采用深度神经网络(DNN)作为深度学习模型,从给定的心电信号中识别准确的人。该方法的有效性在基准数据集上得到了广泛的验证,并检索了结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Neural Network-based Person Identification using ECG Signals
In recent times, biometrics is mostly utilized for the authentication or identification of a user for a vast civilian application. Most of the electronic systems have been proposed that employed distinct behavioral or physiological human beings signature for identifying or verifying the user in an automatic manner. Nowadays, Electro Cardio Gram (ECG)-oriented biometric systems are in the exploration stage. The behavior of the ECG signal is distinctive to every person. As ECG is an exclusive physiological signal that is present only in the live people, it is utilized in the new biometric systems for recognizing the people and to counter the fraud as well as the forge attacks. Majority of the traditional techniques limits from the restriction in several points detection in the ECG signal. The contribution of this paper is the enhancement of the novel structure of person identification model by ECG signal. At first, the ECG signal collected from the three benchmark source is subjected for pre-processing, in which the noise is removed by Low Pass Filter (LPF) approach. Further, the Empirical Mode Decomposition (EMD) is adopted for the decomposition of signal. As feature selection is the significant part of classification enhancement, Principle Component Analysis (PCA) is used as the effective feature extraction that takes the most important features from the signal. Finally, the adoption of Deep Neural Network (DNN) is performed as the deep learning model that could identify the exact person from the given ECG signal. The effectiveness of the method is extensively validated on benchmark datasets and retrieves the outcome.
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