**6. Conclusion**

Investigation and analysis of nonlinear dynamics of brain signal show nonlinear and dynamic behavior in different stages of Alzheimer's disease. Nonlinear dynamics analysis of this signal shows a decrease in the complexity of the brain signal pattern and a decrease in connections due to a decrease in the nonlinear cell dynamics between cortical regions. The next two features are correlation and Lyapanov's appearance, which indicates the feature space, and the convergence or divergence of this space is slightly reduced in this disease. The courses studied are closed-eyed, open-eyed, reminder, and stimulation, and among these four periods, the stimulation period was the best period for recording brain signals, because to diagnose Alzheimer's disease, it is more effective to evaluate the speed of stimulus-response. The mean rate of asymmetry and the mean rate of temporal lobe atrophy increase with the progression of Alzheimer's disease because the amount of damage to the temporal lobe in MRI images of Alzheimer's disease has increased. The accuracy of the results in Elman neural network with the combination of brain signal features and medical images is 94.4% and in the case without combining the signal and image features, the accuracy of the results is 92.2%. The use of nonlinear classifiers is more appropriate than other classification methods due to the nonlinear dynamics of the brain signal. The accuracy of the results in the support vector machine with RBF core with the combination of brain signal features and medical images is 75.5% and in the case without combining the signal and image features, the accuracy of the results is 76.8%. Due to its nonlinear and normal distribution nature, this nucleus has been able to produce better results. Among the processing methods proposed to classify the three classes of healthy, mild, and severely ill, the method of combining brain signal characteristics and medical images has increased the accuracy of Elman classifier results and decreased the accuracy of SVM results. Because spatial features do not have the same nature as temporal features, and if the classifier divides the groups based on linear and non-return methods by extracting inappropriate features, the correct results will not be created. The main innovation in this research is the extraction of the most appropriate features and the appropriate combination of spatial features of medical images and temporal features of brain signals to diagnose Alzheimer's disease.

*Vision Sensors - Recent Advances*
