A signal analysis algorithm for determining brain compliance non-invasively.

Area of Science:

• Biomedical Engineering
• Neuroscience
• Medical Devices

Background:

• Increased intracranial pressure (ICP) due to hydrocephalus or brain injury leads to poor brain compliance or stiffness.
• Ventriculoperitoneal (VP) shunts are common treatments but frequently fail, necessitating invasive replacement procedures.
• Current assessment of failed shunts requires invasive ICP sensor implantation.

Purpose of the Study:

• To develop a non-invasive method for assessing brain compliance.
• To provide an alternative to invasive ICP monitoring for evaluating brain stiffness.

Main Methods:

• Comparing intracranial pressure (ICP) waveforms from an ear-canal piezo sensor with digital artery waveforms from a pulse oximeter.
• Utilizing digital signal processing to analyze the time-lag or phase relationship between the two waveforms.
• Developing an algorithm to compute brain compliance based on the waveform analysis.

Main Results:

• A widening time-lag between ICP and digital artery waveforms correlates with increased brain stiffness or decreased compliance.
• The presented algorithm effectively computes brain compliance using non-invasively acquired waveform data.
• An instrument for real-time brain compliance calculation has been designed.

Conclusions:

• Non-invasive assessment of brain compliance is feasible using synchronized ICP and digital artery waveforms.
• This technique offers a potential non-invasive alternative for monitoring patients with conditions affecting brain compliance.
• The developed algorithm and instrument may aid healthcare professionals in managing ICP-related conditions.