Assessment of Outliers and Detection of Artifactual Network Segments Using Univariate and Multivariate Dispersion Entropy on Physiological Signals.
Assessment of Outliers and Detection of Artifactual Network Segments Using Univariate and Multivariate Dispersion Entropy on Physiological Signals.
复制标题
DOI：
10.3390/e23020244
复制
发表时间：
2021-02-20
期刊：
影响因子：
--
通讯作者：
Escudero J
中科院分区：
文献类型：
--
作者：
Kafantaris E;Piper I;Lo TM;Escudero J
网络生理学已经成为一个有前途的范例，从生理信号中提取临床相关信息，从单变量到多变量分析，允许检查器官系统之间的相互依赖性。然而，为了成功实施，必须研究、量化和解决在生理记录中常见的人为异常值的破坏性影响。在本研究的范围内，我们利用分散熵（DisEn）来初步量化离群样本的能力，以破坏DisEn从生理网络片段（包括同步，脑电图，鼻呼吸，血压和心电图信号）中提取的单变量和多变量特征的值。选择DisEn算法是因为它在检测单变量和多变量时间序列的信号变化方面具有高效的计算和良好的性能。然后，提取的特征被用于在单变量和多变量配置中训练和测试逻辑回归分类器，以部分自动检测伪网络段。我们的研究结果表明，离群样本导致显着中断的值提取的功能与多变量功能显示一定程度的鲁棒性的基础上，制定的网络段，它们被提取的信号的数量。此外，部署的分类器实现了显著的性能，其中正确的网段分类的百分比在许多实验设置中超过95%，每个配置的有效性受到异常值所在信号的影响。最后，由于在网络生理学的框架内提取的特征数量的增加和观察到的影响，其值的准确性的人为样本，能够有效的特征选择的算法步骤的实施被强调为未来研究的一个重要领域。
Network physiology has emerged as a promising paradigm for the extraction of clinically relevant information from physiological signals by moving from univariate to multivariate analysis, allowing for the inspection of interdependencies between organ systems. However, for its successful implementation, the disruptive effects of artifactual outliers, which are a common occurrence in physiological recordings, have to be studied, quantified, and addressed. Within the scope of this study, we utilize Dispersion Entropy (DisEn) to initially quantify the capacity of outlier samples to disrupt the values of univariate and multivariate features extracted with DisEn from physiological network segments consisting of synchronised, electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The DisEn algorithm is selected due to its efficient computation and good performance in the detection of changes in signals for both univariate and multivariate time-series. The extracted features are then utilised for the training and testing of a logistic regression classifier in univariate and multivariate configurations in an effort to partially automate the detection of artifactual network segments. Our results indicate that outlier samples cause significant disruption in the values of extracted features with multivariate features displaying a certain level of robustness based on the number of signals formulating the network segments from which they are extracted. Furthermore, the deployed classifiers achieve noteworthy performance, where the percentage of correct network segment classification surpasses 95% in a number of experimental setups, with the effectiveness of each configuration being affected by the signal in which outliers are located. Finally, due to the increase in the number of features extracted within the framework of network physiology and the observed impact of artifactual samples in the accuracy of their values, the implementation of algorithmic steps capable of effective feature selection is highlighted as an important area for future research.
登录
查看更多内容
基于涨落的逆色散熵及其在信号分类中的应用
影响因子：
3.4
作者：
Jiao, Shangbin;Geng, Bo;Wang, Qing
通讯作者：
Wang, Qing
影响因子：
9.8
作者：
Dressler, O;Schneider, G;Kochs, EF
通讯作者：
Kochs, EF
影响因子：
3.9
作者：
Li, Yuxing;Gao, Xiang;Wang, Long
通讯作者：
Wang, Long
DOI：
10.3390/e22030319
发表时间：
2020-03-11
期刊：
Entropy (Basel, Switzerland)
影响因子：
--
作者：
Kafantaris E;Piper I;Lo TM;Escudero J
通讯作者：
Escudero J
影响因子：
11.9
作者：
Ichimaru, Y;Moody, GB
通讯作者：
Moody, GB