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, cilt.25, sa.8, ss.840-851, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 8
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1080/10255842.2021.1983803
  • Dergi Adı: COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Biotechnology Research Abstracts, Compendex, EMBASE, INSPEC, MEDLINE
  • Sayfa Sayıları: ss.840-851
  • Anahtar Kelimeler: Amyotrophic lateral sclerosis, event-related potentials, variational mode decomposition, brain-computer interface, classification, ATTENTION, ALS
  • Erciyes Üniversitesi Adresli: Evet

Özet

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.