Multi-center validation study of automated classification of pathological slowing in adult scalp electroencephalograms via frequency features

• Main Authors: Peh, Wei Yan, Thomas, John, Bagheri, Elham, Chaudhari, Rima, Karia, Sagar, Rathakrishnan, Rahul, Saini, Vinay, Shah, Nilesh, Srivastava, Rohit, Tan, Yee-Leng, Dauwels, Justin
• 其他作者: School of Electrical and Electronic Engineering
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Engineering::Electrical and electronic engineering; Electroencephalogram; EEG Slowing
• 在線閱讀: https://hdl.handle.net/10356/159814

Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose th...