An Active Dry-Contact Continuous EEG Monitoring System for Seizure Detection Applications in Clinical Neurophysiology

Area of Science:

• Biomedical Engineering
• Neuroscience
• Artificial Intelligence

Background:

• Neonatal seizures pose significant risks for long-term neurological damage.
• Current continuous electroencephalography (cEEG) monitoring is expensive and requires specialized personnel.
• There is a need for accessible and accurate seizure detection methods for vulnerable infants.

Purpose of the Study:

• To develop a low-cost, adjustable electroencephalography (EEG) headset using active dry-contact electrodes.
• To integrate an explainable deep learning model for real-time seizure detection from reduced-montage EEG.
• To implement a multimodal artifact removal algorithm for enhanced signal quality.

Main Methods:

• Acquisition of EEG signals using active electrodes and a custom analog front end.
• Fabrication of a 3D-printed, adjustable headset for diverse head sizes.
• Training a deep learning model for neonatal seizure detection and applying artifact removal algorithms.
• Clinical evaluation against a commercial wet-electrode cEEG system.

Main Results:

• The proposed system achieved a correlation coefficient > 0.8 with commercial devices.
• Noise mitigation performance was comparable to commercial systems.
• The deep learning model showed significant accuracy and recall improvements over existing methods.
• Artifact removal effectively preserved seizure-relevant EEG features.

Conclusions:

• The developed EEG system offers a promising low-cost solution for early seizure detection in infants.
• The combination of dry electrodes, deep learning, and artifact removal enhances diagnostic capabilities.
• This technology has the potential to improve neurological outcomes for high-risk newborns.