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
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering; Electroencephalogram; EEG Slowing
• Online Access: https://hdl.handle.net/10356/159814
• Institution: Nanyang Technological University

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...