On biometric systems: electrocardiogram Gaussianity and data synthesis.

Wael Louis, Shahad Abdulnour, Sahar Javaher Haghighi, Dimitrios Hatzinakos
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引用次数: 2

Abstract

Electrocardiogram is a slow signal to acquire, and it is prone to noise. It can be inconvenient to collect large number of ECG heartbeats in order to train a reliable biometric system; hence, this issue might result in a small sample size phenomenon which occurs when the number of samples is much smaller than the number of observations to model. In this paper, we study ECG heartbeat Gaussianity and we generate synthesized data to increase the number of observations. Data synthesis, in this paper, is based on our hypothesis, which we support, that ECG heartbeats exhibit a multivariate normal distribution; therefore, one can generate ECG heartbeats from such distribution. This distribution is deviated from Gaussianity due to internal and external factors that change ECG morphology such as noise, diet, physical and psychological changes, and other factors, but we attempt to capture the underlying Gaussianity of the heartbeats. When this method was implemented for a biometric system and was examined on the University of Toronto database of 1012 subjects, an equal error rate (EER) of 6.71% was achieved in comparison to 9.35% to the same system but without data synthesis. Dimensionality reduction is widely examined in the problem of small sample size; however, our results suggest that using the proposed data synthesis outperformed several dimensionality reduction techniques by at least 3.21% in EER. With small sample size, classifier instability becomes a bigger issue and we used a parallel classifier scheme to reduce it. Each classifier in the parallel classifier is trained with the same genuine dataset but different imposter datasets. The parallel classifier has reduced predictors' true acceptance rate instability from 6.52% standard deviation to 1.94% standard deviation.

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生物识别系统:心电图高斯性和数据合成。
心电图是一种缓慢的信号,而且容易产生噪声。为了训练可靠的生物识别系统,采集大量心电心跳数据不方便;因此,这个问题可能会导致小样本现象,当样本数量远远小于要建模的观测数量时,就会发生这种现象。在本文中,我们研究了心电心跳的高斯性,并生成了合成数据来增加观测的数量。本文的数据合成基于我们的假设,我们支持该假设,即心电心跳呈现多元正态分布;因此,人们可以从这种分布中产生心电心跳。由于改变心电图形态的内部和外部因素(如噪音、饮食、身体和心理变化以及其他因素),这种分布偏离了高斯性,但我们试图捕捉心跳的潜在高斯性。将该方法应用于一个生物识别系统,并在多伦多大学1012名受试者的数据库中进行了测试,结果表明,该方法的错误率(EER)为6.71%,而在没有数据合成的情况下,该系统的错误率为9.35%。在小样本量问题中,降维问题被广泛研究;然而,我们的研究结果表明,使用所提出的数据合成技术在EER方面的表现至少优于几种降维技术3.21%。在小样本量的情况下,分类器的不稳定性成为一个更大的问题,我们使用并行分类器方案来减少它。并行分类器中的每个分类器都使用相同的真实数据集和不同的冒名顶替数据集进行训练。并行分类器将预测器的真实接受率不稳定性从6.52%标准差降低到1.94%标准差。
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