Detection of amyotrophic lateral sclerosis disease from event-related potentials using variational mode decomposition method


Orhanbulucu F., LATİFOĞLU F.

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING, vol.25, no.8, pp.840-851, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 25 Issue: 8
  • Publication Date: 2022
  • Doi Number: 10.1080/10255842.2021.1983803
  • Journal Name: COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Biotechnology Research Abstracts, Compendex, EMBASE, INSPEC, MEDLINE
  • Page Numbers: pp.840-851
  • Keywords: Amyotrophic lateral sclerosis, event-related potentials, variational mode decomposition, brain-computer interface, classification, ATTENTION, ALS
  • Erciyes University Affiliated: Yes

Abstract

This study, it was aimed to contribute to the literature on Amyotrophic lateral sclerosis (ALS) diagnosis and Brain-Computer Interface (BCI) technologies by analyzing the electroencephalography (EEG) signals obtained as a result of visual stimuli and attention from ALS patients and healthy controls. It was observed that the success rate significantly increased both in the occipital and central regions in all classifiers, especially in the entropy features. The most successful classification was obtained with the Naive Bayes (NB) classifier using the Morphological Features (MF) + Variational Mode Decomposition (VMD) -Entropy features at 88.89% in the occipital region and 94.44% in the central region.