Objective. Closed loop cardiovascular (CV) and cerebrovascular (CBV) variability interactions are assessed via transfer entropy (TE) from systolic arterial pressure (SAP) to heart period (HP) and vice versa and from mean arterial pressure (MAP) to mean cerebral blood velocity (MCBv) and vice versa. This analysis is exploited to assess the efficiency of baroreflex and cerebral autoregulation. This study aims at characterizing CV and CBV controls in postural orthostatic tachycardiac syndrome (POTS) subjects experiencing exaggerated sympathetic response during orthostatic challenge via unconditional TE and TE conditioned on respiratory activity (R). Approach. In 18 healthy controls (age: 28 ± 13 yrs; 5 males, 13 females) and 15 POTS individuals (age: 29 ± 11 yrs; 3 males, 12 females) we acquired beat-to-beat variability of HP, SAP, MAP and MCBv and two R signals, namely respiratory chest movement (RCM) and capnogram (CAP). Recordings were made at sitting rest and during active standing (STAND). TE was computed via vector autoregressive approach. Main results. We found that: (i) when assessing CV interactions, the increase of the TE from SAP to HP during STAND, indicating baroreflex activation, is detected solely when conditioning on RCM; (ii) when assessing CBV interactions, the impact of R on the TE computation is negligible; (iii) POTS shows baroreflex impairment during STAND; (iv) POTS exhibits a normal CBV response to STAND. Significance. TE is useful for detecting the impairment of specific regulatory mechanisms in POTS. Moreover, using different R signals highlights the sensitivity of CV and CBV controls to specific R aspects.

Evaluation of cardiovascular and cerebrovascular control mechanisms in postural orthostatic tachycardia syndrome via conditional transfer entropy: the impact of the respiratory signal type / F. Gelpi, V. Bari, B. Cairo, B. De Maria, R. Wells, M. Baumert, A. Porta. - In: PHYSIOLOGICAL MEASUREMENT. - ISSN 0967-3334. - 44:(2023), pp. 064001.1-064001.17. [10.1088/1361-6579/acdb47]

Evaluation of cardiovascular and cerebrovascular control mechanisms in postural orthostatic tachycardia syndrome via conditional transfer entropy: the impact of the respiratory signal type

F. Gelpi
Primo
;
V. Bari
Secondo
;
B. Cairo;A. Porta
Ultimo
2023

Abstract

Objective. Closed loop cardiovascular (CV) and cerebrovascular (CBV) variability interactions are assessed via transfer entropy (TE) from systolic arterial pressure (SAP) to heart period (HP) and vice versa and from mean arterial pressure (MAP) to mean cerebral blood velocity (MCBv) and vice versa. This analysis is exploited to assess the efficiency of baroreflex and cerebral autoregulation. This study aims at characterizing CV and CBV controls in postural orthostatic tachycardiac syndrome (POTS) subjects experiencing exaggerated sympathetic response during orthostatic challenge via unconditional TE and TE conditioned on respiratory activity (R). Approach. In 18 healthy controls (age: 28 ± 13 yrs; 5 males, 13 females) and 15 POTS individuals (age: 29 ± 11 yrs; 3 males, 12 females) we acquired beat-to-beat variability of HP, SAP, MAP and MCBv and two R signals, namely respiratory chest movement (RCM) and capnogram (CAP). Recordings were made at sitting rest and during active standing (STAND). TE was computed via vector autoregressive approach. Main results. We found that: (i) when assessing CV interactions, the increase of the TE from SAP to HP during STAND, indicating baroreflex activation, is detected solely when conditioning on RCM; (ii) when assessing CBV interactions, the impact of R on the TE computation is negligible; (iii) POTS shows baroreflex impairment during STAND; (iv) POTS exhibits a normal CBV response to STAND. Significance. TE is useful for detecting the impairment of specific regulatory mechanisms in POTS. Moreover, using different R signals highlights the sensitivity of CV and CBV controls to specific R aspects.
active standing; autonomic nervous system; baroreflex; cerebral autoregulation; cerebral blood flow; heart rate variability; vector autoregresssive model
Settore ING-INF/06 - Bioingegneria Elettronica e Informatica
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1022865
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