Volume 10, Issue 2 (9-2023)
jhbmi 2023, 10(2): 175-184 |
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Askari E, Motamed S, Ashori Ghale Koli S. Recognition of Alzheimer’s Patients in Emotional States Based on the Optimal Convolutional Neural Network and Electroencephalography. jhbmi 2023; 10 (2) :175-184
URL: http://jhbmi.ir/article-1-779-en.html
URL: http://jhbmi.ir/article-1-779-en.html
Ph.D. in Computer Engineering, Assistant Professor, Department of Computer Engineering, Fouman and Shaft Branch, Islamic Azad University, Fouman, Iran
Abstract: (679 Views)
Introduction: Accurate diagnosis of Alzheimer’s disease in the early stages plays an important role in patient care, and preventive measures should be taken before irreversible brain damage occurs. With increasing age, there are changes in memory, which is normal, but the symptoms of Alzheimer’s disease are more than temporary forgetfulness. Early and intelligent diagnosis of Alzheimer’s disease in different situations can greatly help patients and physicians.
Method: In the proposed method, a convolutional neural network will be used to improve the recognition of people with Alzheimer’s disease from healthy people in emotional states. First, the required pre-processing is done on the electroencephalography signal, and then, it will be applied as an input to the network. Next, the genetic algorithm is used to optimize the weights of the convolutional neural network.
Results: The research shows that the frontal lobe of the brain is related to emotions and the use of F3 and F4 channels reflects more information compared to other channels, so with this information, the process of recognizing Alzheimer’s patients in emotional states is better.
Conclusion: The proposed method was evaluated with other categories in valence and arousal states. It was observed that this method has a better efficiency compared to other methods with an accuracy of 92.3% in valence and 94.3% in arousal in recognizing people with Alzheimer’s disease.
Method: In the proposed method, a convolutional neural network will be used to improve the recognition of people with Alzheimer’s disease from healthy people in emotional states. First, the required pre-processing is done on the electroencephalography signal, and then, it will be applied as an input to the network. Next, the genetic algorithm is used to optimize the weights of the convolutional neural network.
Results: The research shows that the frontal lobe of the brain is related to emotions and the use of F3 and F4 channels reflects more information compared to other channels, so with this information, the process of recognizing Alzheimer’s patients in emotional states is better.
Conclusion: The proposed method was evaluated with other categories in valence and arousal states. It was observed that this method has a better efficiency compared to other methods with an accuracy of 92.3% in valence and 94.3% in arousal in recognizing people with Alzheimer’s disease.
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