OBJECTIVE: A model-based conditional transfer entropy approach was exploited to quantify the information transfer in cerebrovascular (CBV) and cardiovascular (CV) systems in subjects prone to develop postural syncope. APPROACH: Spontaneous beat-to-beat variations of mean cerebral blood flow velocity (MCBFV) derived from a transcranial Doppler device, heart period (HP) derived from surface electrocardiogram, mean arterial pressure (MAP) and systolic arterial pressure (SAP) derived from finger plethysmographic arterial pressure device were monitored at rest in supine position (REST) and during 60° head-up tilt (TILT) in 13 individuals (age mean ± standard deviation: 28 ± 9 years, min-max range: 18-44 years, 5 males) with a history of recurrent episodes of syncope (SYNC) and in 13 age- and gender-matched controls (NonSYNC). Respiration (R) obtained from a thoracic belt was acquired as well and considered as a conditioning signal in transfer entropy assessment. Synchronous sequences of 250 consecutive MCBFV, HP, MAP, SAP and R values were utilized to estimate the information genuinely transferred from MAP to MCBFV (i.e. disambiguated from R influences) and vice versa. Analogous indexes were computed from SAP to HP and vice versa. Traditional time and frequency domain analyses were carried out as well. MAIN RESULTS: SYNC subjects showed an increased genuine information transfer from MAP to MCBFV during TILT, while they did not exhibit the expected rise of the genuine information transfer from SAP to HP. SIGNIFICANCE: We conclude that SYNC individuals featured an impaired cerebral autoregulation visible during TILT and were unable to activate cardiac baroreflex to cope with the postural challenge. Traditional frequency domain markers based on transfer function modulus, phase and coherence functions were less powerful or less specific in typifying the CBV and CV controls of SYNC individuals. Conditional transfer entropy approach can identify the impairment of CBV and CV controls and provide specific clues to identify subjects prone to develop postural syncope.
OBJECTIVE: A model-based conditional transfer entropy approach was exploited to quantify the information transfer in cerebrovascular (CBV) and cardiovascular (CV) systems in subjects prone to develop postural syncope. APPROACH: Spontaneous beat-to-beat variations of mean cerebral blood flow velocity (MCBFV) derived from a transcranial Doppler device, heart period (HP) derived from surface electrocardiogram, mean arterial pressure (MAP) and systolic arterial pressure (SAP) derived from finger plethysmographic arterial pressure device were monitored at rest in supine position (REST) and during 60° head-up tilt (TILT) in 13 individuals (age mean ± standard deviation: 28 ± 9 years, min-max range: 18-44 years, 5 males) with a history of recurrent episodes of syncope (SYNC) and in 13 age- and gender-matched controls (NonSYNC). Respiration (R) obtained from a thoracic belt was acquired as well and considered as a conditioning signal in transfer entropy assessment. Synchronous sequences of 250 consecutive MCBFV, HP, MAP, SAP and R values were utilized to estimate the information genuinely transferred from MAP to MCBFV (i.e. disambiguated from R influences) and vice versa. Analogous indexes were computed from SAP to HP and vice versa. Traditional time and frequency domain analyses were carried out as well. MAIN RESULTS: SYNC subjects showed an increased genuine information transfer from MAP to MCBFV during TILT, while they did not exhibit the expected rise of the genuine information transfer from SAP to HP. SIGNIFICANCE: We conclude that SYNC individuals featured an impaired cerebral autoregulation visible during TILT and were unable to activate cardiac baroreflex to cope with the postural challenge. Traditional frequency domain markers based on transfer function modulus, phase and coherence functions were less powerful or less specific in typifying the CBV and CV controls of SYNC individuals. Conditional transfer entropy approach can identify the impairment of CBV and CV controls and provide specific clues to identify subjects prone to develop postural syncope.
Authors: Katarzyna Buszko; Agnieszka Piątkowska; Edward Koźluk; Tomasz Fabiszak; Grzegorz Opolski Journal: Entropy (Basel) Date: 2018-12-16 Impact factor: 2.524
Authors: Alberto Porta; Angela Fantinato; Vlasta Bari; Francesca Gelpi; Beatrice Cairo; Beatrice De Maria; Enrico Giuseppe Bertoldo; Valentina Fiolo; Edward Callus; Carlo De Vincentiis; Marianna Volpe; Raffaella Molfetta; Marco Ranucci Journal: PLoS One Date: 2020-12-10 Impact factor: 3.240
Authors: Trevor Witter; Yu-Chieh Tzeng; Terry O'Donnell; Jessica Kusel; Bridget Walker; Mary Berry; Chloe E Taylor Journal: Front Neurosci Date: 2017-08-15 Impact factor: 4.677
Authors: Eric A Schmidt; Fabien Despas; Anne Pavy-Le Traon; Zofia Czosnyka; John D Pickard; Kamal Rahmouni; Atul Pathak; Jean M Senard Journal: Front Physiol Date: 2018-02-08 Impact factor: 4.566