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

Fırat Orhanbulucu; Fatma Latifoğlu

doi:10.1080/10255842.2021.1983803

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

Comput Methods Biomech Biomed Engin. 2022 Jun;25(8):840-851. doi: 10.1080/10255842.2021.1983803. Epub 2021 Oct 4.

Authors

Fırat Orhanbulucu^{1

2}, Fatma Latifoğlu²

Affiliations

¹ Department of Biomedical Engineering, Inonu University, Malatya, Turkey.
² Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey.

PMID: 34602001
DOI: 10.1080/10255842.2021.1983803

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 Naïve 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.

Keywords: Amyotrophic lateral sclerosis; brain-computer interface; classification; event-related potentials; variational mode decomposition.

MeSH terms

Amyotrophic Lateral Sclerosis* / diagnosis
Bayes Theorem
Brain-Computer Interfaces*
Electroencephalography / methods
Evoked Potentials
Humans