Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series
We present a framework for the estimation of transfer entropy (TE) under the conditions typical of physiological system analysis, featuring short multivariate time series and the presence of instantaneous causality (IC). The framework is based on recognizing that TE can be interpreted as the differe...
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| Format: | Article |
| Sprog: | English |
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MDPI AG
2013-01-01
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| Serier: | Entropy |
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| Online adgang: | http://www.mdpi.com/1099-4300/15/1/198 |
| _version_ | 1828120628957282304 |
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| author | Alberto Porta Giandomenico Nollo Luca Faes |
| author_facet | Alberto Porta Giandomenico Nollo Luca Faes |
| author_sort | Alberto Porta |
| collection | DOAJ |
| description | We present a framework for the estimation of transfer entropy (TE) under the conditions typical of physiological system analysis, featuring short multivariate time series and the presence of instantaneous causality (IC). The framework is based on recognizing that TE can be interpreted as the difference between two conditional entropy (CE) terms, and builds on an efficient CE estimator that compensates for the bias occurring for high dimensional conditioning vectors and follows a sequential embedding procedure whereby the conditioning vectors are formed progressively according to a criterion for CE minimization. The issue of IC is faced accounting for zero-lag interactions according to two alternative empirical strategies: if IC is deemed as physiologically meaningful, zero-lag effects are assimilated to lagged effects to make them causally relevant; if not, zero-lag effects are incorporated in both CE terms to obtain a compensation. The resulting compensated TE (cTE) estimator is tested on simulated time series, showing that its utilization improves sensitivity (from 61% to 96%) and specificity (from 5/6 to 0/6 false positives) in the detection of information transfer respectively when instantaneous effect are causally meaningful and non-meaningful. Then, it is evaluated on examples of cardiovascular and neurological time series, supporting the feasibility of the proposed framework for the investigation of physiological mechanisms. |
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| format | Article |
| id | doaj.art-5c99f5b8a61f4d2bbbe666c079e28b62 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T14:07:58Z |
| publishDate | 2013-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-5c99f5b8a61f4d2bbbe666c079e28b622022-12-22T04:19:49ZengMDPI AGEntropy1099-43002013-01-0115119821910.3390/e15010198Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time SeriesAlberto PortaGiandomenico NolloLuca FaesWe present a framework for the estimation of transfer entropy (TE) under the conditions typical of physiological system analysis, featuring short multivariate time series and the presence of instantaneous causality (IC). The framework is based on recognizing that TE can be interpreted as the difference between two conditional entropy (CE) terms, and builds on an efficient CE estimator that compensates for the bias occurring for high dimensional conditioning vectors and follows a sequential embedding procedure whereby the conditioning vectors are formed progressively according to a criterion for CE minimization. The issue of IC is faced accounting for zero-lag interactions according to two alternative empirical strategies: if IC is deemed as physiologically meaningful, zero-lag effects are assimilated to lagged effects to make them causally relevant; if not, zero-lag effects are incorporated in both CE terms to obtain a compensation. The resulting compensated TE (cTE) estimator is tested on simulated time series, showing that its utilization improves sensitivity (from 61% to 96%) and specificity (from 5/6 to 0/6 false positives) in the detection of information transfer respectively when instantaneous effect are causally meaningful and non-meaningful. Then, it is evaluated on examples of cardiovascular and neurological time series, supporting the feasibility of the proposed framework for the investigation of physiological mechanisms.http://www.mdpi.com/1099-4300/15/1/198cardiovascular variabilityconditional entropyinstantaneous causalitymagnetoencephalographytime delay embedding |
| spellingShingle | Alberto Porta Giandomenico Nollo Luca Faes Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series Entropy cardiovascular variability conditional entropy instantaneous causality magnetoencephalography time delay embedding |
| title | Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series |
| title_full | Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series |
| title_fullStr | Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series |
| title_full_unstemmed | Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series |
| title_short | Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series |
| title_sort | compensated transfer entropy as a tool for reliably estimating information transfer in physiological time series |
| topic | cardiovascular variability conditional entropy instantaneous causality magnetoencephalography time delay embedding |
| url | http://www.mdpi.com/1099-4300/15/1/198 |
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