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首页> 外文期刊>Informatics in Medicine Unlocked >A novel noise-robust stacked ensemble of deep and conventional machine learning classifiers (NRSE-DCML) for human biometric identification from electrocardiogram signals
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A novel noise-robust stacked ensemble of deep and conventional machine learning classifiers (NRSE-DCML) for human biometric identification from electrocardiogram signals

机译：一种新型和传统机器学习分类器（NRSE-DCML）的迅速稳健的堆叠集合，用于从心电图信号进行人体生物识别

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BackgroundBiometric identification is advantageous over traditional authentication methods such as password, PIN (Personal Identification Number), and/or a token-based card. Electrocardiogram (ECG) signals show unique behavioral characteristics for persons due to their heart morphology and structure which make them more appropriate for human identification. ECGs are safe and more reliable. Related previous models for human identification from ECG signals can be divided into conventional machine learning and deep learning models. In this study, a novel noise-robust stacked ensemble of deep and conventional machine learning models (NRSE-DCML) is proposed for human identification from ECG signals.MethodsNRSE-DCML includes an ensemble of deep convolutional neural networks in the first layer, an ensemble of support vector machines in the second layer and a perceptron classifier with Softmax activation function in the third layer. This study takes advantages of both of conventional machine learning models and deep neural networks by combining them in NRSE-DCML. All heart beats are used to train the first, the second and the third layers of the proposed stacked ensemble classifier. The first and the second layer try to identify noisy heart beats and increase their weights to reduce their misclassification error. PTB-Diagnostics ECG signals for 152 healthy and patient persons from PhysioNet database are used for evaluating and validating NRSE-DCML.ResultsExperimental results show that NRSE-DCML achieves the Accuracy of 99.02, FAR of 0.95 and FRR of 1.02 using 5-fold Cross-Validation strategy using 1-second segments which is comparable with other state-of-the art methods.ConclusionsThe main advantages of our proposed method is its ability to detect unknown persons as unauthorized class and considering both healthy and patient groups. Finally, our proposed model enhances the accuracy of the biometric identification for noisy heart beats.
机译：背景技术识别对于传统认证方法（例如密码，引脚（个人识别号）和/或基于令牌的卡）是有利的。心电图（ECG）信号显示出由于其心脏形态和结构而具有独特的行为特征，使其更适合人类鉴定。 ECG安全且更可靠。相关以前的ECG信号人类识别模型可分为传统的机器学习和深度学习模型。在本研究中，提出了一种深度和传统机器学习模型（NRSE-DCML）的新型噪声稳健堆叠集合（NRSE-DCML），用于从ECG信号的人体识别..Hethodsnrse-DCML包括第一层中的深度卷积神经网络的集合在第二层和Perceptron分类器中的支持向量机，在第三层中具有Softmax激活功能。本研究通过将它们组合在NRSE-DCML中来利用传统的机器学习模型和深神经网络的优点。所有心跳都用于训练所提出的堆叠集合分类器的第一层，第二层和第三层。第一层和第二层试图识别嘈杂的心跳并增加其权重，以减少错误分类错误。 PTB-DIPATORICS用于152个来自物理体数据库的健康和患者的ECG信号用于评估和验证NRSE-DCML.RESULTSEXPREMICEXTOMEXTOMEXTEMS，结果表明，NRSE-DCML实现了99.02的准确性，远远超过1.02的0.95和FRR使用5倍使用与其他最新方法相当的1秒段的验证策略。结论我们所提出的方法的主要优点是其能够将未知人员视为未经授权的课程，并考虑健康和患者群体。最后，我们提出的模型提高了对嘈杂心跳的生物识别识别的准确性。

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