JSAI2022

Presentation information

Organized Session

Organized Session » OS-5

[3I4-OS-5b] 生体信号を活用した医療・ヘルスケアAI(2/2)

Thu. Jun 16, 2022 3:30 PM - 5:10 PM Room I (Room I)

オーガナイザ:藤原 幸一(名古屋大学)[現地]、久保 孝富(奈良先端科学技術大学院大学)

3:30 PM - 3:50 PM

[3I4-OS-5b-01] Long-term prognostic classification of West syndrome based on scalp EEG using phase-amplitude coupling

〇Tatsuki Saito1, Koichi Fujiwara1, Jun Natsume2, Ryosuke Suzui2 (1. Nagoya University Graduate School of Engineering, 2. Nagoya University Graduate School of Medicine)

Keywords:west syndrome, long-term prognosis, high-frequency oscillations, phase-amplitude coupling, time series classification

West syndrome (WS), an infantile epileptic encephalopathy defined on the basis of epileptic spasms and hypsarrhythmia on the Electroencephalogram (EEG), is recognised to have a very poor long-term prognosis in terms of spasm control, freedom from other seizure types and developmental arrest.WS is an important clinical problem for patients and patients' families because of its poor developmental prognosis; however, the pathophysiological of WS have not been fully understood in spite of extensive work by many investigators. Accurate biomarkers of WS for the evaluating the effect and prognosis of treatment is needed.To predict the long-term prognosis of WS after the treatment, we used two deep learning models with the EEG in which High-frequency Oscillations(HFO) were appearing as input.The highest Micro-average accuracy rate was found to be 78\% , and Macro-average accuracy of 64\% was obtained from each subject.

Authentication for paper PDF access

A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.

Password