Textural feature based intelligent approach for neurological abnormality detection from brain signal data

• Published in: PloS one Vol. 17; no. 11; p. e0277555
• Main Authors: Tawhid, Md. Nurul Ahad, Siuly, Siuly, Wang, Kate, Wang, Hua
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.11.2022; Public Library of Science (PLoS)
• Subjects: Abnormalities; Algorithms; Alzheimer's disease; Artificial intelligence; Autism; Biology and Life Sciences; Biomarkers; Brain; Brain research; Classification; Classifiers; Computational neuroscience; Computer and Information Sciences; Data mining; Diagnosis; Discriminant analysis; EEG; Electric properties; Electroencephalography; Electroencephalography - methods; Engineering and Technology; Feature extraction; Fourier transforms; Health aspects; Histograms; Humans; Image classification; Machine Learning; Medical imaging; Medicine and Health Sciences; Nervous system diseases; Neurological diseases; Neurological disorders; Neurophysiology; Parkinson's disease; Principal Component Analysis; Principal components analysis; Research and Analysis Methods; Schizophrenia; Segments; Signal Processing, Computer-Assisted; Support Vector Machine; Support vector machines; System effectiveness; Australia

The diagnosis of neurological diseases is one of the biggest challenges in modern medicine, which is a major issue at the moment. Electroencephalography (EEG) recordings is usually used to identify various neurological diseases. EEG produces a large volume of multi-channel time-series data that neur...