Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network
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 abnormalit...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
D. G. Pylarinos
2021-06-01
|
| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://www.etasr.com/index.php/ETASR/article/view/4112 |
| _version_ | 1811191216223551488 |
|---|---|
| author | G. Anuradha D. N. Jamal |
| author_facet | G. Anuradha D. N. Jamal |
| author_sort | G. Anuradha |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T15:02:40Z |
| format | Article |
| id | doaj.art-61e239fe4b3b4c57a7311b24b4a0e153 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-04-11T15:02:40Z |
| publishDate | 2021-06-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-61e239fe4b3b4c57a7311b24b4a0e1532022-12-22T04:16:54ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362021-06-0111310.48084/etasr.4112Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural NetworkG. Anuradha0D. N. Jamal1Department of ECE, BSA Crescent Institute of Science and Technology, IndiaDepartment of Electronics and Communication Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, IndiaDementia 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.https://www.etasr.com/index.php/ETASR/article/view/4112Lewy body dementiaEEGdementianeural networkdominant frequency |
| spellingShingle | G. Anuradha D. N. Jamal Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network Engineering, Technology & Applied Science Research Lewy body dementia EEG dementia neural network dominant frequency |
| title | Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network |
| title_full | Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network |
| title_fullStr | Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network |
| title_full_unstemmed | Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network |
| title_short | Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network |
| title_sort | classification of dementia in eeg with a two layered feed forward artificial neural network |
| topic | Lewy body dementia EEG dementia neural network dominant frequency |
| url | https://www.etasr.com/index.php/ETASR/article/view/4112 |
| work_keys_str_mv | AT ganuradha classificationofdementiaineegwithatwolayeredfeedforwardartificialneuralnetwork AT dnjamal classificationofdementiaineegwithatwolayeredfeedforwardartificialneuralnetwork |