Optimal EEG Data Segmentation in LSTM Networks for Learning Neural Dynamics of ADHD

I. Wen Huang, Yang Chang, Cory Stevenson, I. Chun Chen, Dar Shong Lin, Li Wei Ko

研究成果: Conference contribution同行評審

摘要

Attention deficit/hyperactivity disorder (ADHD) is a condition that generally affects the neurodevelopment of children. Both electroencephalography (EEG) and continuous performance tests (CPT) can be used not only to provide objective identification ADHD, but also to directly observe and quantify the performance of subject task performance. In this study, we propose an optimized segmentation for learning EEG time-series information with long short-term memory (LSTM) networks used to differentiate ADHD and neurotypical (NT) children. A total of 30 NT children and 30 children diagnosed with ADHD participated in CPT while simultaneously monitored with EEG. The experimental results show that, whether it is a single feature of beta power at a the O2 electrode location or all the features, the optimal data segment is the same, an EEG segment containing the data from 30 seconds of eyes-open resting and 30 seconds of CPT task. This produced improved performance for discriminating the differences in EEG between the two groups, thus assisting in the diagnosis of ADHD.

原文English
主出版物標題ICSSE 2022 - 2022 International Conference on System Science and Engineering
發行者Institute of Electrical and Electronics Engineers Inc.
頁面33-38
頁數6
ISBN(電子)9781665488525
DOIs
出版狀態Published - 2022
事件2022 International Conference on System Science and Engineering, ICSSE 2022 - Virtual, Online, Taiwan
持續時間: 26 5月 202229 5月 2022

出版系列

名字ICSSE 2022 - 2022 International Conference on System Science and Engineering

Conference

Conference2022 International Conference on System Science and Engineering, ICSSE 2022
國家/地區Taiwan
城市Virtual, Online
期間26/05/2229/05/22

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