Journal
IEEE ACCESS
Volume 9, Issue -, Pages 78060-78074Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3083519
Keywords
Electroencephalography; Dementia; Databases; Electrodes; Reliability; Feature extraction; Licenses; EEG systems; detection reliability; neurodegenerative diseases; automatic; semi-automatic detection; biomedical applications
Categories
Funding
- Universidad Autonoma de Queretaro (UAQ)
- Consejo Nacional de Ciencia y Tecnologia (CONACYT)
- Programa para el Desarrollo Profesional Docente (PRODEP)
- Universidad Autonoma de Baja California (UABC)
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The study thoroughly reviewed the results of using EEG systems for the detection of dementia diseases, finding common combinations with reliability levels exceeding 90% and detailing considerations at each stage, as well as describing the most commonly used classification tools and processing techniques.
Dementia diseases are increasing rapidly, according to the World Health Organization (WHO), becoming an alarming problem for the health sector. The electroencephalogram (EEG) is a non-invasive test that records brain electrical activity and has a wide field of applications in the medical area, one of which is the detection of neurodegenerative diseases. The aim of this work is to present the results of a thorough review of the use of EEG systems for the detection of dementia diseases. Around 82 papers published between 2009 and 2020 were reviewed and compared obtaining data such as sampling time, number of electrodes, the most popular processing, classification, and validation techniques, as well as an analysis of the reported results. The relationship of the selected parameters with the efficiency obtained is shown. Some more common combinations in the reviewed papers that demonstrated to have reliability levels greater than 90%, and details to be considered at each stage of the process. An overview of the most commonly used classification tools and processing techniques is also described.
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