Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals

• Published in: Applied sciences Vol. 11; no. 4; p. 1761
• Main Authors: Choi, Yoon-A, Park, Sejin, Jun, Jong-Arm, Ho, Chee Meng Benjamin, Pyo, Cheol-Sig, Lee, Hansung, Yu, Jaehak
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2021
• Subjects: Aging; Blood pressure; Brain diseases; Brain research; Cardiovascular diseases; Coronary artery disease; Disability; Disease detection; EEG; Electroencephalography; Epilepsy; Experiments; Fast Fourier transformations; Fourier transforms; Geriatrics; Health care; Heart diseases; Hemorrhage; Imaging techniques; Learning algorithms; machine learning; Magnetic resonance imaging; Medical imaging; Monitoring systems; Older people; Patients; Precursors; Predictions; Real time; real-time health monitoring; Risk factors; Signs and symptoms; Stroke; stroke disease analysis; stroke prediction; Telemedicine; Testing time

Stroke is the third highest cause of death worldwide after cancer and heart disease, and the number of stroke diseases due to aging is set to at least triple by 2030. As the top three causes of death worldwide are all related to chronic disease, the importance of healthcare is increasing even more....