Assessment of Outliers and Detection of Artifactual Network Segments Using Univariate and Multivariate Dispersion Entropy on Physiological Signals

Abstract

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.

Keywords: data quality; dispersion entropy; multivariate analysis; network physiology; outlier samples.

Figure A1

The $\mu$ and $\sigma$ of the percentage difference are shown for each artifactual feature distribution across experimental setups where the EEG signal of the network contains a percentage of outliers determined by the corresponding P-Factor. These results correspond to experimental setups with $outliermean=\pm 4\times max\left|amplitude\right|$.

Figure A2

The $\mu$ and $\sigma$ of the percentage difference are shown for each artifactual feature distribution across experimental setups where the RESP signal of the network contains a percentage of outliers determined by the corresponding P-Factor. These results correspond to experimental setups with $outliermean=\pm 4\times max\left|amplitude\right|$.

Figure A3

The $\mu$ and $\sigma$ of the percentage difference are shown for each artifactual feature distribution across experimental setups where the BP signal of the network contains a percentage of outliers determined by the corresponding P-Factor. These results correspond to experimental setups with $outliermean=\pm 4\times max\left|amplitude\right|$.

Figure A4

The $\mu$ and $\sigma$ of the percentage difference are shown for each artifactual feature distribution across experimental setups where the ECG signal of the network contains a percentage of outliers determined by the corresponding P-Factor. These results correspond to experimental setups with $outliermean=\pm 4\times max\left|amplitude\right|$.

Figure 1

The methodological steps of the study are presented, with the sections corresponding to each step indicated in each block within $\left\{\right\}$. The arrows between each block indicate the outputs of a step that are used as inputs by the next one.

Figure 2

The $\mu$ and $\sigma$ of the percentage difference are shown for each artifactual feature distribution across experimental setups where the EEG signal of the network contains a percentage of outliers determined by the corresponding P-factor.

Figure 3

The $\mu$ and $\sigma$ of the percentage difference are shown for each artifactual feature distribution across experimental setups where the RESP signal of the network contains a percentage of outliers determined by the corresponding P-factor.

Figure 4

The $\mu$ and $\sigma$ of the percentage difference are shown for each artifactual feature distribution across experimental setups, where the BP signal of the network contains a percentage of outliers determined by the corresponding P-factor.

Figure 5

The $\mu$ and $\sigma$ of the percentage difference are shown for each artifactual feature distribution across experimental setups where the ECG signal of the network contains a percentage of outliers determined by the corresponding P-factor.