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García M, Poza J, Santamarta D, Romero-Oraá R, Hornero R. Continuous wavelet transform in the study of the time-scale properties of intracranial pressure in hydrocephalus. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2018; 376:rsta.2017.0251. [PMID: 29986920 PMCID: PMC6048580 DOI: 10.1098/rsta.2017.0251] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/03/2018] [Indexed: 06/01/2023]
Abstract
Normal pressure hydrocephalus (NPH) encompasses a heterogeneous group of disorders generally characterized by clinical symptoms, ventriculomegaly and anomalous cerebrospinal fluid (CSF) dynamics. Lumbar infusion tests (ITs) are frequently performed in the preoperatory evaluation of patients who show NPH features. The analysis of intracranial pressure (ICP) signals recorded during ITs could be useful to better understand the pathophysiology underlying NPH and to assist treatment decisions. In this study, 131 ICP signals recorded during ITs were analysed using two continuous wavelet transform (CWT)-derived parameters: Jensen divergence (JD) and spectral flux (SF). These parameters were studied in two frequency bands, associated with different components of the signal: B1(0.15-0.3 Hz), related to respiratory blood pressure oscillations; and B2 (0.67-2.5 Hz), related to ICP pulse waves. Statistically significant differences (p < 1.70 × 10-3, Bonferroni-corrected Wilcoxon signed-rank tests) in pairwise comparisons between phases of ITs were found using the mean and standard deviation of JD and SF. These differences were mainly found in B2, where a lower irregularity and variability, together with less prominent time-frequency fluctuations, were found in the hypertension phase of ITs. Our results suggest that wavelet analysis could be useful for understanding CSF dynamics in NPH.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.
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Affiliation(s)
- María García
- Biomedical Engineering Group (GIB), Department T.S.C.I.T., E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group (GIB), Department T.S.C.I.T., E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
- IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Valladolid, Spain
- INCYL, Instituto de Neurociencias de Castilla y León, University of Salamanca, Salamanca, Spain
| | - David Santamarta
- Servicio de Neurocirugía, Complejo Asistencial Universitario de León, León, Spain
| | - Roberto Romero-Oraá
- Biomedical Engineering Group (GIB), Department T.S.C.I.T., E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group (GIB), Department T.S.C.I.T., E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
- IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Valladolid, Spain
- INCYL, Instituto de Neurociencias de Castilla y León, University of Salamanca, Salamanca, Spain
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