ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2018
Continuous wavelet transform in the study of the time-scale properties of intracranial pressure in hydrocephalus.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Correlation between night sweats and season fluctuation in China.Frontiers in public health · 2024Article
- Automatic identification of intracranial pressure waveform during external ventricular drainage clamping: segmentation via wavelet analysis.Physiological measurement · 2023Article
- Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach.Physiological measurement · 2020Article
- An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms.Physiological measurement · 2019Article
- Effects of altitude changes on mild-to-moderate closed-head injury in rats following acute high-altitude exposure.Experimental and therapeutic medicine · 2019Article
- Introduction to redundancy rules: the continuous wavelet transform comes of age.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2018Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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:
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