Seizure detection using the phase-slope index and multichannel ECoG

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Epileptic seizure detection and analysis are crucial for clinical management and research.
• Existing methods may lack accuracy or real-time applicability.

Purpose of the Study:

• To develop and validate a novel seizure detection and analysis scheme using the phase-slope index (PSI).
• To assess the efficacy of PSI in distinguishing seizure from interictal activity in electrocorticogram (ECoG) data.
• To enable computationally efficient, real-time seizure detection.

Main Methods:

• Application of the phase-slope index (PSI) to multichannel electrocorticogram (ECoG) data.
• Development of a global interaction metric based on PSI to detect seizures.
• Utilizing a moving average for adaptive thresholding to account for inter-patient and intra-patient variability.
• Evaluation on data from five epilepsy patients, encompassing 47 seizures over 258 hours.

Main Results:

• The PSI metric effectively identified increased spatio-temporal interactions during seizures, distinguishing them from interictal activity.
• The proposed method detected all seizures in 4 out of 5 patients with a false detection rate below 2 per hour.
• A variation of the global metric successfully identified channels driving seizure activity.

Conclusions:

• The PSI-based approach offers a robust and computationally efficient method for epileptic seizure detection and analysis.
• This technique shows promise for real-time seizure monitoring applications.
• The ability to identify driving channels provides further insight into seizure generation mechanisms.