Classification of stroke using machine learning techniques : review study
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Abstract—Presently, stroke is the leading cause of adult injury worldwide. The World Health Organization estimates that each year 15 million people around the world suffer a stroke. Five million of them die, and another five million are disabled for life. There is a chance to dramatically enhance the classification of strokes in the early stages. In this article, we reviewed all portable devices that produced electroencephalogram(EEG) data and all machine learning (ML) methods and deep-learning methods used to identify stroke using EEG data, and we noted that the amount of work on ML and deep learning in analyzing EEG data have increased rapidly in recent years. Such analysis has achieved greater precision compared to that conventional methods. We also discussed in this study the opportunities and key challenges for improving the accuracy of future work.
Açıklama
Anahtar Kelimeler
Deep learning , Imaging , Organizations , Stroke (medical condition) , Brain modeling , Electroencephalography , Real-time systems, Deep Learning, Imaging, Organizations, Stroke (Medical Condition), Brain Modeling, Electroencephalography, Real-Time Systems
Kaynak
IEEE
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
33
Sayı
1
Künye
Sawan, A., Awad, M., & Qasrawi, R. (2023, May). Classification of Stroke Using Machine Learning Techniques: Review Study. In 2023 International Conference on Control, Automation and Diagnosis (ICCAD) (pp. 1-8). IEEE.
