CONDITIONAL JOINT TRANSFER ENTROPY OF CARDIOVASCULAR AND CEREBROVASCULAR CONTROL SYSTEMS IN SUBJECTS PRONE TO POSTURAL SYNCOPE

Transcription

1 CONDITIONAL JOINT TRANSFER ENTROPY OF CARDIOVASCULAR AND CEREBROVASCULAR CONTROL SYSTEMS IN SUBJECTS PRONE TO POSTURAL SYNCOPE Vlasta Bari 1, Andrea Marchi 2,3, Beatrice De Maria 2,4, Gianluca Rossato 5, Giandomenico Nollo 6,7, Luca Faes 6,7 and Alberto Porta 1,8 1 University of Milan, Department of Biomedical Sciences for Health, Milan, Italy 2 Politecnico di Milano, Department of Electronics Information and Bioengineering, Milan, Italy 3 San Gerardo Hospital, Department of Emergency and Intensive Care, Monza, Italy 4 IRCCS Fondazione Salvatore Maugeri, Milan, Italy 5 Department of Neurology, Sacro Cuore Hospital, Negrar, Verona, Italy 6 BIOtech, Department of Industrial Engineering, University of Trento, Trento, Italy 7 IRCS Program, PAT-FBK, Trento, Italy 8 IRCCS Policlinico San Donato, Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, Milan, Italy

2 Information transfer in multivariate systems Information theoretic approaches have been recently proposed to assess information transfer in multivariate systems Assigned the universe of knowledge Ω as composed by one target (y) and two exogenous signals (x 1 and x 2 ), different quantities can be derived Joint transfer entropy (JTE), represents the information that can be derived from the two sources above and beyond the self entropy Conditioning out one of the two exogenous sources it is possible to derive the so-called conditional joint transfer entropy (CJTE) Adapted from Porta et al, PLoS One, vol. 10, e , 2015

3 Information transfer in multivariate systems CJTE was found useful to study cardiovascular control, and it can also be applied to study mechanisms mutually influencing heart and brain As an example, CJTE from systolic arterial pressure (SAP) to heart period (HP) conditioned on respiration (R): Increased as a function of the tilt angles Porta et al, PLoS One, vol. 10, e , 2015 Was reduced during anesthesia in patients undergoing coronary artery bypass graft Porta et al, proceedings of the 37 th EMBC, 2015

4 Cardiovascular regulation Cardiac baroreflex is a short term mechanism adjusting HP variations in response to SAP ones. (Neural Feedback Pathway) Diastolic runoff and Starling law: variations in SAP in response to HP (Mechanical Feedforward Pathway) HP-SAP is a closed loop dynamical interaction Baroreflex SAP (i) SAP HP HP (i) Mechanical Pathway

5 Cerebrovascular regulation Cerebral autoregulation (CA) is a homeostatic mechanism responsible for maintaining mean cerebral blood flow (mcbf) relatively constant, despite the changes in mean AP (map) Transcranial Doppler (TCD) provides a noninvasive estimation of blood velocity in middle cerebral arteries cerebral blood flow velocity (CBFV) estimation Decrease of CBFV anticipated decrease of AP, for example in presyncope Cerebral autoregulation map mcbfv map (i) mcbfv (i)

6 Cardio- and cerebrovascular interactions The study of cardiac-brain interactions is a key point to understand vital control mechanisms regulation The autonomic nervous system has a clue role in the close and bidirectional relationship existing between cardiovascular (CV) and cerebrovascular (CBV) systems CV and CBV regulations are impaired before postural syncope development

7 Respiratory Influence Both CV and CBV regulations are disturbed and influenced by respiration (R) Control breathing experiments proved the important role of respiration in modulating HP-SAP and MCBFV-MAP interactions Eames PJ Clin. Sci. 2004, 106, Respiratory instability influences CBV and CV dynamics during presyncope Porta C Heart 2008, 94, RESPIRATION RESPIRATION SAP HP map mcbfv SAP (i) RESPIRATION HP (i) map (i) RESPIRATION mcbfv (i)

8 AIM To quantify cardiovascular regulation between HP and SAP and cerebrovascular regulation between mcbfv and map along predefined causal directions and conditioned on R as derived from CJTE analysis in subjects with recurrent episodes of postural syncope contrasted to healthy subjects during head-up tilt test

9 Experimental protocol 13 SYNC subjects (age: 28 9 years, 5 males) previous history of syncope events (>3 events in the foregoing year) 13 nonsync healthy subjects (age: 27 8 years, 5 males) 10 minutes recording at REST 10 minutes recording during 60 head up TILT All SYNC subjects and none of nonsync developed postural syncope at the prolonging of TILT up to 40 minutes Signals: ECG lead II Photopletismographic arterial pressure (AP, Finapres, NL) CBFV through a TCD device (Multi-Dop T2, Dwl, USA) Respiration through a thoracic impedance belt Sampling frequency: 1000 Hz

10 R [mv] CBFV [cm/s] AP [mmhg] ECG [mv] Signals [s]

11 Time series extraction HP [ms] -> Time interval between two consecutive R peaks SAP [mmhg] -> Maximum of AP inside HP(i) HP (i-1) HP (i) HP (i+1) map [mmhg] -> Integral of AP between two diastolic values divided by the time distance t AP (i) SAP (i-1) SAP (i) SAP (i+1) map (i) mcbfv [cm/s] -> Integral of CBFV between two diastolic values divided by the time distance t CBFV (i) R [n.u.] -> Sampling of respiration in correspondence of R peaks t AP (i-1) t AP (i) t AP (i+1) mcbfv (i) 250 beats length series During REST, TILT In SYNC and nonsync subjects t CBFV (i-1) t CBFV (i) t CBFV (i+1) R(i-1) R(i) R(i+1) Time domain indices evaluation: Mean (µ) and Variance (σ 2 ) of each series

12 Series modelization In the full Ω Given the universe of knowledge Ω={y,x 1,x 2 } y = output variable x 1, x 2 = exogenous sources The output y can be described as an ARX 1 X 2 process Given the one step ahead prediction model Coefficients estimated via least square approach and Cholesky decomposition Model order optimized via Akaike information criterion σ 2 ARX X = variance of the prediction error of the ARX 1 X 2 process, 1 2 given by the difference between y(n) and y(n n-1) measure of goodness of fit of the model in Ω

13 Series modelization in the restricted Ωs y = output variable x 1, x 2 = exogenous sources Given the restricted universes of knowledge Ω\x 1 ={y,x 2 } The output y can be described as an ARX 2 process Same model order optimized in the full Ω Coefficients re-estimated via least square approach and Cholesky decomposition σ 2 ARX = variance of the error term of the ARX 2 process 2 measure of goodness of fit of the model in Ω\x 1

14 Conditional Joint Transfer Entropy CJTE measures the information transferred to y that can be solely attributable to x 1 computed by comparing the variances of the error term of y in Ω\x 1 and Ω Cardiovascular variabilities Cerebrovascular variabilities 1. CJTE SAP,R HP R y= HP, x 1 = SAP, x 2 = R 3. CJTE map,r mcbfv R y= mcbfv, x 1 = map, x 2 = R 2. CJTE HP,R SAP R y= SAP, x 1 = HP, x 2 = R 4. CJTE mcbfv,r map R y= map, x 1 = mcbfv, x 2 = R * Zero lag effects were accounted for from SAP to HP and from R to any other variables

15 Results time domain Parameter nonsync SYNC REST TILT REST TILT µ HP [ms] 848 ± ± 1075* 910 ± ± 112* σ 2 HP [ms 2 ] 2492 ± ± ± ± 1897* µ SAP [mmhg] 135 ± ± ± ± 23 σ 2 SAP [mmhg 2 ] 35 ± ± 27* 24 ± ± 17* µ map [mmhg] 16 ± ± 7 17 ± 4 16 ± 4 σ 2 map [mmhg 2 ] 5 ± 8 3 ± 3 3 ± 7 3 ± 4 µ mcbfv [cm/s] 42 ± ± 29* 59 ± ± 49* σ 2 mcbfv [cm 2 /s 2 ] 30 ± ± ± ± 77 * means p<0.05 vs REST TILT provoked the expected changes of time domain parameters. HP decreased and SAP variance increased because of the sympathetic stimulus mcbfv was reduced as a consequence of the cerebral vasoconstriction

16 Results CJTE CJTE SAP,R HP R increased during TILT in NonSYNC subjects, but not in SYNC ones CJTE map,r mcbfv R was significantly reduced in SYNC subjects after TILT

17 Discussion Information transfer from SAP and R to HP given R was increased during TILT only in NonSYNC subjects lack of maintenance of a physiological baroreflex control mechanism in SYNC Information transfer from map and R to mcbfv given R was reduced in SYNC during TILT changes in cerebral autoregulation and CBFV dynamics are likely before postural syncope

18 Conclusions The study of CJTE applied over CV and CBV variabilities allowed to determine differences in SYNC and NonSYNC subjects during a head up tilt test Importance of the assessing the information transferred on both arms of HP-SAP and mcbfv-map closed loops together The joint study of CV and CBV mechanisms related to R influence is suggested in order to better understand the overall contributes of the regulatory systems to postural syncope development Future developments: Optimizing the delay of the interactions among variables Comparison of results with model free nonlinear transfer entropy approach

19 Thank you for your attention!