Evaluating EIT, hdEEG, and iPhone Electrode Localization for Stroke Applications.

Area of Science:

• Biomedical Engineering
• Neuroscience
• Medical Imaging

Background:

• Stroke diagnosis and monitoring are critical for patient outcomes.
• Current neuroimaging techniques have limitations in continuous monitoring.
• Combining EIT and hdEEG offers a potential novel approach.

Purpose of the Study:

• To investigate the feasibility of integrating EIT and hdEEG for stroke assessment.
• To develop and validate a computational framework for subject-specific modeling.
• To assess the utility of this combined approach in acute stroke patients.

Main Methods:

• Development of a computational framework for high-fidelity, subject-specific meshes.
• Accurate registration of a 256-electrode hdEEG cap to the computational models.
• Data acquisition from seven acute stroke patients shortly after stabilization.
• Initial EIT image reconstructions and error analysis.

Main Results:

• Successful development of a computational framework for EIT and hdEEG integration.
• Demonstration of electrode-to-mesh and mesh-to-mesh registration accuracy.
• Initial EIT image reconstructions were achieved in acute stroke patients.
• Workflow complexity identified as a current limitation.

Conclusions:

• The combination of EIT and hdEEG is a promising area for stroke research.
• The developed computational framework is a key step towards clinical application.
• Long-term stroke monitoring is identified as the most suitable application for this technology given current limitations.