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Early Detection of Mild Cognitive Impairment Using Quantitative Analysis of EEG Signals
Hadiyoso S.a, Cynthia L.F.A.R.a, Mengko T.L.E.R.a, Zakaria H.a
a Bandung Institute of Technology, School of Electrical and Information Engineering, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Detection of Alzheimer’s Disease (AD) at initial onset is urgent because it greatly influences the success of therapy. Mild cognitive impairment (MCI) is a condition of transition to (AD) although not all subjects with MCI will develop AD. However, the detection of MCI is still important to avoid the risk of getting worse. There are several biomarkers that can be used, one of which is an electroencephalogram (EEG), which is of much concern because it is non-invasive, inexpensive and safe to use continuously. In this study, quantitative EEG (QEEG) analysis was carried out for MCI detection compared to the normal group. Linear QEEG based on power spectral features was applied. EEG signals in the temporal, parietal and occipital electrode areas of 27 subjects (16 normal and 11 MCI) were observed. The simulation results using K-NN, shows an accuracy of 81.5%. Thus EEG can be used as a potential reference as a tool for diagnosing MCI in large populations by considering costs, convenience, and non-invasive.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Alzheimer’s disease,EEG signals,Electro-encephalogram (EEG),Electrode areas,Large population,Mild cognitive impairments,Mild cognitive impairments (MCI),Power spectral[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Alzheimer,EEG,MCI,QEEG,spectral features[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/BioMIC48413.2019.9034892[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]