Compensated Transfer Entropy as a Tool for Reliably Estimating Information Transfer in Physiological Time Series

Entropy

Luca Faes¹  Giandomenico Nollo¹ 

[1] Department of Physics and BIOtech Center, University of Trento, Via delle Regole 101, 38123 Mattarello, Trento, Italy; E-Mail:

关键词: cardiovascular variability;    conditional entropy;    instantaneous causality;    magnetoencephalography;    time delay embedding;   

DOI  :  10.3390/e15010198

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.