Effect of infusion tests on the dynamical properties of intracranial pressure in hydrocephalus

García, María M.; Poza, Jesús; Bachiller, Alejandro; Santamarta, David Cortés; Hornero, Roberto

doi:10.1016/j.cmpb.2016.06.007

Cited by 2 publications

(9 citation statements)

References 47 publications

(108 reference statements)

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“…All the participants presented clinical symptoms of NPH: gait and balance disturbances, cognitive deterioration and urinary incontinence [10,27]. Lumbar ITs were performed as a supplementary hydrodynamic study to help in the decision on the surgical management of patients [9,24]. Table 1 summarizes the data of the population under study.…”

Section: Materials and Methods (A) Subjectsmentioning

confidence: 99%

“…Pressure was measured using the second needle (rostral needle), which was connected to a pressure microtransducer (Codman ® MicroSensor™ ICP transducer, Codman & Shurtleff). The analogue output of the microtransducer was connected to an amplifier (ML110 Bridge amplifier), an analogue to digital converter (PowerLab 2/25 Data recording system ML825, ADI Instruments) and a computer, where signals could be visualized and recorded [9,24]. Each ICP recording contained four different phases.…”

Section: (B) Data Acquisition Protocolmentioning

confidence: 99%

“…The continuous wavelet transform (CWT) is a mathematical tool that can be used to analyse time series with a variable resolution in the time-frequency plane [29]. It has been used in the context of ICP signal analysis due to the non-stationary and multiscale features of cerebral haemodynamics [20,24]. In the CWT, the signal to be analysed, x(t), is decomposed using translated and dilated versions of a function called 'mother wavelet', ψ(t) [29].…”

Section: (C) Continuous Wavelet Transformmentioning

confidence: 99%

“…It has the form of a Gaussian-windowed complex sinusoidal waveform with several cycles [33]. The complex Morlet wavelet has been previously used in the analysis of several types of biological signals that show a non-stationary behaviour [20,[34][35][36], including ICP signal analysis [20,24]. It can be defined as [20]…”

Section: (C) Continuous Wavelet Transformmentioning

confidence: 99%

“…Parameters derived from the wavelet transform of ICP signals, like wavelet entropy (WE) and wavelet turbulence (WT), have been also used to study signal irregularity and variability in ICP recordings obtained during ITs [24]. Alternative time-frequency representations, such as Zhao-Atlas-Marks distribution, have been used to analyse cerebral autoregulation in healthy subjects during hypercapnia [25].…”

Section: Introductionmentioning

confidence: 99%

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Continuous wavelet transform in the study of the time-scale properties of intracranial pressure in hydrocephalus

García

Poza

Santamarta

et al. 2018

Phil. Trans. R. Soc. A.

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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: (0.15-0.3 Hz), related to respiratory blood pressure oscillations; and (0.67-2.5 Hz), related to ICP pulse waves. Statistically significant differences ( < 1.70 × 10, 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 , 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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