Analysis of intracranial pressure time series using wavelets (Haar basis functions)

Area of Science:

• Biomedical Engineering
• Signal Processing
• Neuroscience

Background:

• Intracranial pressure (ICP) analysis historically used frequency domain transformations.
• Classical spectral analysis struggles with the non-stationary nature of ICP signals.
• Understanding cerebrospinal fluid dynamics relies on accurate ICP signal interpretation.

Purpose of the Study:

• To introduce a novel wavelet-based approach for analyzing intracranial pressure (ICP) signals.
• To overcome the limitations of classical spectral analysis in handling non-stationary ICP data.
• To map cerebrospinal fluid dynamics using ICP spectral composition.

Main Methods:

• Acquired epidural ICP data from 118 patients with suspected cerebrospinal fluid disorders during volume pressure testing.
• Applied discrete Haar wavelet transform for multiresolution analysis of ICP fluctuations.
• Utilized scalograms to visualize time-frequency representations of ICP data.

Main Results:

• Wavelet decomposition revealed distinct patterns in ICP fluctuations via scalograms.
• Pathological wave activity and artificial ICP changes were topographically identifiable.
• The method demonstrated topographical detection in the time-frequency plane.

Conclusions:

• Wavelet analysis offers a powerful and simple method for assessing static and dynamic ICP properties.
• This approach surpasses classical spectral transforms for real-world biomedical data.
• Haar wavelets provide a fast and robust solution for ICP signal processing in clinical settings.