The primary aim of the study is to identify the existence of the post-traumatic stress disorder (PTSD) in an individual and to detect the dominance level of each affected brain region in PTSD using rs-fMRI data. This will assist the psychiatrists and neurologists to distinguish impartially between PTSD individuals and healthy controls for the brain-based treatment of PTSD. Methods: Twenty-eight individuals (14 with PTSD, 14 healthy controls) were assessed to obtain rs-fMRI data of their six brain regions-of-interest. The rs-fMRI data analyzed by the Artificial Neural Network (ANN), adopting the training-validation-testing approach to classify PTSD and to identify the most affected brain region due to PTSD. The classification accuracy is justified by a variety of different methods and metrics. Results: Three ANN models were established to attain the study's purpose using the susceptible regions in the right, left, and both hemispheres, and the classification accuracy of ANN models achieved 79%, 93.5%, and 94.5%, respectively. The prediction accuracy even increased in the independent holdout sample using trained models. The developed models are reliable, intellectually attractive and generalize. Additionally, the most dominant region in the PTSD individuals was the left hippocampus and the least was the right hippocampus.
Conclusion:The present investigation achieved high classification accuracy and identified the brain regions those contributed most to differentiating PTSD individuals from healthy controls. The results indicated that the left hippocampus is the most affected brain region in PTSD individuals. Therefore, our findings are helpful for practitioners for diagnostic, medication, and therapy of the affected brain regions by knowing the strength of infected regions.
Depression is a global prevalent ailment for possible mental illness or mental disorder globally. Recognizing depressed early signs is critical for evaluating and preventing mental illness. With the progress of machine learning, it is possible to make intelligent systems capable of detecting depressive symptoms using speech analysis. This study presents a hybrid model to identify and predict mental illness from Arabic speech analysis due to depression. The proposed hybrid model comprises convolutional neural network (CNN) and a support vector machine (SVM) to identify and predict mental disorders. Experiments are performed on the Arabic speech benchmark data set of 200 speeches. A total of 70% of data were reserved for training, while 30% of data were to test the proposed model. The hybrid model (CNN + SVM) attained a 90.0% and 91.60% accuracy rate to predict the depression from Arabic speech analysis for training and testing stages. To authenticate the results of a proposed hybrid model, recurrent neural network (RNN) and CNN are also applied to the same data set individually, and the results are compared with each other. The RNN achieved an 80.70% and 81.60% accuracy rate to predict depression while speaking in the training and testing stages. The CNN predicted the depression in the training and testing stages with 88.50% and 86.60% accuracy rates. Based on the analysis, the proposed hybrid model secured better prediction results than individual RNN and CNN models on the same data set. Furthermore, the suggested model had a lower FPR, FNR, and higher accuracy, AUC, sensitivity, and specificity rate than individual RNN, CNN model performance in predicting depression. Finally, the achieved findings will be helpful to classify depression while speaking Arabic/speech and will be beneficial for physicians, psychiatrists, and psychologists in the detection of depression.
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