6533b82cfe1ef96bd128ffbf
RESEARCH PRODUCT
Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardio-Respiratory Nonstationary Dynamics
Michele OriniRiccardo BarbieriLuca CitiGaetano ValenzaLuca Faessubject
AdultMaleInformation transferHistoryHeartbeatDatabases FactualPhysiologyEntropy0206 medical engineeringComplex systemBiomedical EngineeringHeart Rate VariabilityProbability density function02 engineering and technology01 natural sciencesPoint processStatistics NonparametricElectrocardiographyYoung Adult0103 physical sciencesProbability density functionEntropy (information theory)HumansStatistical physicsTransfer Entropy010306 general physicsBiomedical measurementMathematicsbusiness.industryHemodynamicsModels CardiovascularHeart beatSignal Processing Computer-AssistedComplexityBaroreflex020601 biomedical engineeringKolmogorov-Smirnov DistanceRespiratory Sinus ArrhythmiaBaroreflex; Biomedical measurement; Complexity; Entropy; Heart beat; Heart rate variability; Heart Rate Variability; History; Kolmogorov-Smirnov Distance; Physiology; Point Process; Probability density function; Respiratory Sinus Arrhythmia; Transfer Entropy; Biomedical EngineeringDiscrete time and continuous timePoint ProceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaPoint ProcessTransfer entropyFemaleArtificial intelligencebusinessdescription
Objective: Measures of transfer entropy (TE) quantify the direction and strength of coupling between two complex systems. Standard approaches assume stationarity of the observations, and therefore are unable to track time-varying changes in nonlinear information transfer with high temporal resolution. In this study, we aim to define and validate novel instantaneous measures of TE to provide an improved assessment of complex nonstationary cardiorespiratory interactions. Methods: We here propose a novel instantaneous point-process TE (ipTE) and validate its assessment as applied to cardiovascular and cardiorespiratory dynamics. In particular, heartbeat and respiratory dynamics are characterized through discrete time series, and modeled with probability density functions predicting the time of the next physiological event as a function of the past history. Likewise, nonstationary interactions between heartbeat and blood pressure dynamics are characterized as well. Furthermore, we propose a new measure of information transfer, the instantaneous point-process information transfer (ipInfTr), which is directly derived from point-process-based definitions of the Kolmogorov–Smirnov distance. Results and Conclusion: Analysis on synthetic data, as well as on experimental data gathered from healthy subjects undergoing postural changes confirms that ipTE, as well as ipInfTr measures are able to dynamically track changes in physiological systems coupling. Significance: This novel approach opens new avenues in the study of hidden, transient, nonstationary physiological states involving multivariate autonomic dynamics in cardiovascular health and disease. The proposed method can also be tailored for the study of complex multisystem physiology (e.g., brain–heart or, more in general, brain–body interactions).
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-01 |