Loading...

Improving ALS Diagnosis Based on Human EEG Signal Analysis with Machine Learning

Abedi, A ; Sharif University of Technology | 2024

57 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/ICBME64381.2024.10895722
  3. Publisher: 2024
  4. Abstract:
  5. Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease of the motor neurons in the central nervous system, which causes considerable morbidity and mortality. The basis for effective management and intervention rests on early and accurate diagnosis. This paper seeks to propose a method to improve diagnostic precision for ALS using an approach with machine learning techniques coupled with Electroencephalography (EEG) signals. EEG data from 10 ALS patients and 7 healthy controls are broadly classified based on state-of-the-art EEG-based brain signal processing and advanced algorithms of classification. Using the K-Nearest Neighbors and Support Vector Machine classifiers results in an accuracy of 9 4. 1 1% and 100%, respectively. We further shared common EEG features across ALS, Parkinson, and Multiple Sclerosis, which we termed as common mode features (CMF), suggesting the possibility of a shared biomarker for these neurodegenerative conditions. Our results emphasize the capability of EEG-based Machine Learning models in distinguishing ALS from healthy conditions, hence opening an avenue for its early diagnosis and distinction from other neurological disorders. This underlines the great potential that machine learning, when combined with EEG analysis, may have for transformational ALS diagnostics. © 2024 IEEE
  6. Keywords:
  7. Amyotrophic Lateral Sclerosis (ALS) ; Common Mode Features (CMF) ; Electroencephalography (EEG) ; K-Nearest Neighbor (KNN) ; Support Vector Machine (SVM) ; Adversarial machine learning ; Brain mapping ; Image segmentation ; Nearest neighbor search ; Neurons ; Commonmode ; K-near neighbor ; Mode features ; Nearest-neighbour ; Neurodegenerative diseases
  8. Source: 2024 31st National and 9th International Iranian Conference on Biomedical Engineering, ICBME 2024 ; 2024 , Pages 63-68 ; 979-833152971-0 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/10895722