Intracranial pressure wave morphological classification: automated analysis and clinical validation

Area of Science:

• Neuroscience
• Biomedical Engineering

Background:

• Existing software for intracranial pressure (ICP) analysis is often complex and lacks clinical relevance.
• Novel methods are needed for accurate and clinically applicable ICP parameter analysis.

Purpose of the Study:

• To evaluate a new morphological classification of cerebrospinal fluid pulse pressure waveforms (CSFPPW).
• To compare this classification with elastance index (EI) and CSF-outflow resistance (Rout).
• To test the efficacy of an automated ICP analysis using an artificial neural network (ANN).

Main Methods:

• An ANN was trained to classify 60 CSFPPWs into four morphological classes.
• The ANN's classification accuracy was compared to that of an expert examiner.
• CSFPPW morphology was correlated with EI and Rout from infusion tests in 60 patients.

Main Results:

• The ANN achieved 88.3% concordance with expert CSFPPW classification.
• Elevated EI correlated with progression in morphological classes.
• All patients with pathological baseline CSFPPW (class IV) showed altered CSF hydrodynamics.

Conclusions:

• The proposed morphological CSFPPW classification accurately reflects and predicts altered CSF hydrodynamics.
• ANNs can be trained to recognize CSF wave morphologies effectively.
• This classification offers a helpful and accurate diagnostic tool for ICP assessment.