Shaun A. Hussain

The paper predicts treatment response in infantile epileptic spasms syndrome using EEG phase-amplitude coupling, finding that lower pre-treatment modulation index values correlate with better treatment outcomes.

The paper uses EEG functional connectivity to track progression from infantile epileptic spasms syndrome to Lennox-Gastaut Syndrome and identifies patterns associated with treatment response and disease markers.

PyHFO is a lightweight deep learning platform for efficient HFO detection in EEG data, achieving 50x speed improvements over standard methods.

This paper develops a deep learning algorithm to detect and classify epileptogenic high-frequency oscillations (eHFOs) from intracranial EEG data with high accuracy.

The paper identifies computational EEG features that distinguish epileptic spasms patients from controls with high accuracy.

This study shows that scalp EEG high frequency oscillations (HFOs) and their coupling with slow-wave activity correlate with active infantile spasms and can predict them better than hypsarrhythmia.