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

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: (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'.

Effects of altitude changes on mild‑to‑moderate closed‑head injury in rats following acute high‑altitude exposure

“…In the control group, no such observations were made; indicating that CC swelling and acute ventricular dilatation of mmCHI may be closely associated with AHH. PTH may cause increased intracranial pressure, which has been recognized as an indicator of poorer outcomes following TBI (48). The dynamic brain MRI manifestations were more gradually improved by hypobaric-hypoxia conditions (slowly descending altitude) compared with rapid reoxygenation (rapidly descending altitude) and continuous hypoxia (maintained at extreme high-altitude) in mmCHI following AHH at 12 and 24 h.…”

Automatic identification of intracranial pressure waveform during external ventricular drainage clamping: segmentation via wavelet analysis

“…We filtered out any ICP values greater than 80 mmHg, furthermore all the recordings were processed using a finite impulse response (FIR) bandpass filter with cut-off frequencies 0.1 Hz and 40 Hz. This frequency range preserved the relevant spectral content of the signals and minimized the presence of the DC component (García et al 2018).…”

“…It is a Gaussian-windowed complex sinusoidal waveform with parameters f c for the center frequency and f b for the bandwidth. The complex Morlet wavelet is chosen for its ability to analyze non-stationary signals including ICP waveform(García et al 2018). As in(García et al 2018), the parameters f c and f b were set to 1, to achieve a balanced trade-off between time resolution (Δt) and frequency resolution (Δf ) at low frequencies.…”

Introduction to redundancy rules: the continuous wavelet transform comes of age

“…Three papers concerning medical applications of the CWT are then presented. These are breath sound analysis and pain characterization using higher order spectral techniques, including the instantaneous wavelet bispectrum, biamplitude, biphase and bicoherence by Hadjileontiadis [6]; the analysis of ECG and EMG biosignals using wavelet ridge analysis and wavelet-based dispersion measures by Wachowiak et al [7]; and a study of intracranial pressure during lumbar infusion tests by Garcia et al [8] using wavelet entropy, Jensen divergence and spectral flux. CWT applications are not restricted to time series: this is wonderfully illustrated in the analysis of the intricate spatial properties of the radial ring structure of Saturn in this issue by Tiscareno & Hedman [9].…”