Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network
Autor: | D. N. Jamal, G. Anuradha |
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Rok vydání: | 2021 |
Předmět: |
medicine.medical_specialty
neural network dominant frequency Information technology 02 engineering and technology Audiology Electroencephalography 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering T1-995 Medicine Dementia EEG Technology (General) Lewy body medicine.diagnostic_test Artificial neural network business.industry Dementia with Lewy bodies 020208 electrical & electronic engineering Dominant frequency Engineering (General). Civil engineering (General) T58.5-58.64 medicine.disease Feed forward artificial neural network Lewy body dementia TA1-2040 Abnormality business 030217 neurology & neurosurgery dementia |
Zdroj: | Engineering, Technology & Applied Science Research, Vol 11, Iss 3 (2021) |
ISSN: | 1792-8036 2241-4487 |
Popis: | Dementia has become a global public health issue. The current study is focused on diagnosing dementia with Electro Encephalography (EEG). The detection of the advancement of the disease is carried out by detecting the abnormal behavior in EEG measurements. Assessment and evaluation of EEG abnormalities is conducted for all the subjects in order to detect dementia. EEG feature analysis, namely dominant frequency, dominant frequency variability, and frequency prevalence, is done for abnormal and normal subjects and the results are compared. For dementia with Lewy bodies, in 85% of the epochs, the dominant frequency is present in the delta range whereas for normal subjects it lies in the alpha range. The dominant frequency variability in 75% of the epochs is above 4Hz for dementia with Lewy bodies, and in normal subjects at 72% of the epochs, the dominant frequency variability is less than 2Hz. It is observed that these features are sufficient to diagnose dementia with Lewy bodies. The classification of Lewy body dementia is done by using a feed-forward artificial neural network wich proved to have a 94.4% classification accuracy. The classification with the proposed feed-forward neural network has better accuracy, sensitivity, and specificity than the already known methods. |
Databáze: | OpenAIRE |
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