Phase Lag Index Histogram Features for Identifying Epileptic Seizure EEG Signals

[Abstract]

Recent medical analytical studies have focused on the phase lag index (PLI) to analyze the functional connectivity of the brain. This paper assumes that the PLI contains informative characteristics for automatically identifying electroencephalogram (EEG) signals of epilepsy patients. We propose a method for distinguishing between epileptic seizure and non-seizure EEG signals using PLI histograms acquired from the signals of a short period, randomly sampled from longer recordings, in different brain regions. We demonstrate the ability of the method to identify epileptic seizures by experiments on the publicly available CHB-MIT Scalp EEG database.

[Publications]

• Masayoshi Oguri, Tetsuya Okazaki, Tohru Okanishi, Masashi Nishiyama, Sotaro Kanai, Hiroyuki Yamada, Kaoru Ogo, Takashi Himoto, Yoshihiro Maegaki, Ayataka Fujimoto,
  Phase Lag Analysis Scalp Electroencephalography May Predict Seizure Frequencies in Patients with Childhood Epilepsy with Centrotemporal Spikes,
  Yonago Acta Medica, Vol. 66, No. 1, pp. 48 - 55, February 2023.
• Masashi Nishiyama, Yoshio Iwai, Masayoshi Oguri, and Yoshihiro Maegaki
  Phase Lag Index Histogram Features for Identifying Epileptic Seizure EEG Signals,
  Proceedings of IEEE 10th Global Conference on Consumer Electronics (GCCE), OS-HPL, pp. 167 - 170, October 2021.