Identifying Patients With PTSD Utilizing Resting-State fMRI Data and Neural Network Approach

Shahzad, Mirza Naveed; Ali, Haider; Saba, Tanzila; Kolivand, Hoshang; Bahaj, Saeed Ali

doi:10.1109/access.2021.3098453

Cited by 10 publications

(8 citation statements)

References 53 publications

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“…It may also severely impact people causing self-harm and sometimes suicide. Mood disorder and mental illness in adult life are also associated with depressive disorder [ 5 , 6 ]. From depression, people may also experience a bad mood, low self-esteem, loss of interest, low energy, and body pain without a clear cause [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning

Saba

Khan

Abunadi

et al. 2022

Computational Intelligence and Neuroscience

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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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Section: Introductionmentioning

confidence: 99%

Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning

Saba

Khan

Abunadi

et al. 2022

Computational Intelligence and Neuroscience

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“…Similarly, the FPR, FNR, and loss values are less for the KNN and SVM‐RBF three models. The previous studies (De Bont et al, 2015; Nicholson et al, 2018; Shahzad et al, 2021 and Tahmasian et al, 2017) had not achieved a better accuracy rate to identify PTSD patients than our proposed approaches. Our selected approaches found the performance level, functional connectivity, and classification of PTSD and control subjects.…”

Section: Analysis and Discussionmentioning

confidence: 58%

“…Post-traumatic stress disorder (PTSD) is an anxiety disorder that has affected tens of thousands of people globally, as it is a severe and significant psychiatric disorder (Shahzad et al, 2021). PTSD is the most common psychopathological consequence of traumatic events (Stein et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…ML approaches are progressively utilized to classify, select, predict, and characterize individuals suffering from a range of psychiatric disorders (Rehman et al, 2021; Schultebraucks et al, 2019). Recently, ML approaches have been used for the classification, emotion regulation, treatment selection, prediction, or characterization the PTSD individuals by Zandvakili et al (2020), Nixon et al (2021), Siegel et al (2021), Christ et al (2021), Shahzad et al (2021), and Sheynin et al (2021). The most frequently used ML approaches are logistic regression (LR), K‐nearest neighbor (KNN), and support vector machine linear (SVM) (Jabeen et al, 2018; Saba et al, 2018; Yousaf et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning for post‐traumatic stress disorder identification utilizing resting‐state functional magnetic resonance imaging

Saba

Rehman

Shahzad

et al. 2022

Microscopy Res & Technique

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Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains.The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms. HighlightsMachine learning techniques are applied to identify cancerous patients utilizing resting-state fMRI data. Among several MLAs, the SVM-RBF performed a better prediction rate at higher reliability than other competitive models.

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“…A more extensive, more diverse data set would be necessary to generalise the approach. Finally, to achieve optimum models, the impacts of ambient noise on unfiltered signals and hence the derived predictors must be investigated further (Shahzad et al 2021 ; Rehman et al 2021b ). Because there aren't enough medical images with plenty of labels in real life, and labelling such data can only be done by physicians, studying the localization of lesions under poor supervision is useful.…”

Section: Limitations Pitfalls and Future Challengesmentioning

confidence: 99%

Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions

Alyami

2024

EJNMMI Rep.

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Radiological image analysis using machine learning has been extensively applied to enhance biopsy diagnosis accuracy and assist radiologists with precise cures. With improvements in the medical industry and its technology, computer-aided diagnosis (CAD) systems have been essential in detecting early cancer signs in patients that could not be observed physically, exclusive of introducing errors. CAD is a detection system that combines artificially intelligent techniques with image processing applications thru computer vision. Several manual procedures are reported in state of the art for cancer diagnosis. Still, they are costly, time-consuming and diagnose cancer in late stages such as CT scans, radiography, and MRI scan. In this research, numerous state-of-the-art approaches on multi-organs detection using clinical practices are evaluated, such as cancer, neurological, psychiatric, cardiovascular and abdominal imaging. Additionally, numerous sound approaches are clustered together and their results are assessed and compared on benchmark datasets. Standard metrics such as accuracy, sensitivity, specificity and false-positive rate are employed to check the validity of the current models reported in the literature. Finally, existing issues are highlighted and possible directions for future work are also suggested.

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Identifying Patients With PTSD Utilizing Resting-State fMRI Data and Neural Network Approach

Cited by 10 publications

References 53 publications

Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning

Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning

Machine learning for post‐traumatic stress disorder identification utilizing resting‐state functional magnetic resonance imaging

Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions

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