Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 62, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2020.102128
Keywords
Heart rate variability; Arterial blood pressure; Autonomic nervous system; Symbolic dynamics; Complexity analysis; Parkinson's disease; Head-up tilt
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Funding
- Ricerca Corrente from the Italian Ministry of Health to IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
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Symbolic analysis was frequently utilized in cardiovascular control assessment from spontaneous variability of heart period (HP) and systolic arterial pressure (SAP). However, due to different symbolization approaches comparison among studies present in literature is difficult especially on pathological groups with signs of autonomic dysfunction. We performed HP and SAP symbolic analyses via different symbolization methods over a group of Parkinson's disease (PD) patients with no sign of orthostatic hypotension and over age- and gender-matched healthy (H) controls at rest in supine position (REST) and during head-up tilt (HUT). The most frequently exploited symbolization methods applied directly to the original values, referred to as amplitude-based (AB) techniques, and to their first variations, labeled as variation-based (VB) methods, were compared. The rates of pattern families featuring different amount of variability among symbols were computed. In agreement with the inclusion criteria PD patients could maintain steady SAP during HUT via a tachycardic response. In spite of similar trends symbolic markers exhibited subtle differences. The level of consistency among different symbolic approaches is more dependent on the type of series and pattern family than on symbolization strategy (i.e. AB or VB). Consistency was higher over SAP than HP symbolic indexes and over highly variable patterns than more stable ones. All the symbolic methods detected an increased complexity of cardiac and vascular controls in PD patients compared to H subjects more evident during HUT than at REST. Symbolic markers of highly variable patterns provide interpretation independent of the symbolization technique exploited for their computation.
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