التفاصيل البيبلوغرافية
| العنوان: |
Deep Neural Network Model for Automated Detection of Alzheimer’s Disease using EEG Signals. |
| المؤلفون: |
Deshmukh, Atharva, V. Karki, Maya, S. R., Bhuvan, S., Gaurav, J. P., Hitesh |
| المصدر: |
International Journal of Online & Biomedical Engineering; 2022, Vol. 18 Issue 8, p115-126, 12p |
| مصطلحات موضوعية: |
ARTIFICIAL neural networks, ALZHEIMER'S disease, DATA augmentation, ELECTROENCEPHALOGRAPHY, SIGNAL processing, MOTOR neuron diseases, ENTORHINAL cortex |
| مستخلص: |
Our brain is our body’s control centre and is essential for proper functioning of the body. Alzheimer’s disease is a chronic neurodegenerative disease that affects the cerebral cortex of the brain and causes memory loss and loss of cognitive thinking. EEG (Electroencephalography) is a method of recording neurological electrical activity with electrodes. It was chosen as it is a simple, painless procedure. This paper suggests an automated and accurate algorithm for the detection of Alzheimer’s Disease using EEG signals with a combination of Signal processing and Deep Learning Methods. Concepts like Butterworth filters, DWT, statistical parameters, Data Augmentation and CNN were used in order to achieve a classification algorithm with high accuracy. A total highest system accuracy of 97.61% was achieved. [ABSTRACT FROM AUTHOR] |
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