Related Papers
ArXiv
Neural Networks based approaches for Major Depressive Disorder and Bipolar Disorder Diagnosis using EEG signals: A review
2020 •
mokhtar mohammadi
Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There's a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this work, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable re...
Computer Methods and Programs in Biomedicine
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks:A review
2021 •
Sinem Aslan
Brain and Behavior
Sex differences in rTMS treatment response: A deep learning-based EEG investigation
2022 •
Caglar Uyulan
Introduction: The present study aimed to investigate sex differences in response to repetitive transcranial magnetic stimulation (rTMS) in Major Depressive Disorder (MDD) patients. Identifying the factors that mediate treatment response to rTMS in MDD patients can guide clinicians to administer more appropriate, reliable, and personalized interventions. Methods: In this paper, we developed a novel pipeline based on convolutional LSTM based deep learning (DL) to classify 25 female and 25 male patients based on their rTMS treatment response. Results: Five different classification models were generated, namely pre-/post-rTMS female (model 1), pre-/post-rTMS male (model 2), pre-rTMS female responder versus pre-rTMS female nonresponders (model 3), pre-rTMS male responder vs. pre-rTMS male nonresponder (model 4), and pre-rTMS responder versus nonresponder of both sexes (model 5), achieving 93.3%, 98%, 95.2%, 99.2%, and 96.6% overall test accuracy, respectively. Conclusion: These results indicate the potential of our approach to be used as a response predictor especially regarding sex-specific antidepressant effects of rTMS in MDD patients.
Diagnostics
Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques
Oumaima Saidani
The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals’ complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given the association between EEG signals and particular demographics, such as gender and age, and the influences of these demographic characteristics on the incidence of depression, it would be preferable to include demographic factors during EEG modeling and depression detection. The main objective of this work is to develop an algorithm that can recognize depression patterns by studying EEG data. Following a multiband analysis of such signals, machine learning and deep learning techniques were used to detect depression patients automatically. EEG signal data are collected from the multi-modal open dataset MODMA and employed in studying mental diseases. The EEG...
IEEE Access
Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review
Olimpia Matarazzo
Biomedical Signal Processing and Control
A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders
2021 •
Abdelkader Nasreddine Belkacem
Journal of Computer Science
Autism Detection from 2D Transformed EEG Signal using Convolutional Neural Network
JANNAT PEYA
Electronics
Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions
2022 •
Noor Ul Huda
Over the years, stress, anxiety, and modern-day fast-paced lifestyles have had immense psychological effects on people’s minds worldwide. The global technological development in healthcare digitizes the scopious data, enabling the map of the various forms of human biology more accurately than traditional measuring techniques. Machine learning (ML) has been accredited as an efficient approach for analyzing the massive amount of data in the healthcare domain. ML methodologies are being utilized in mental health to predict the probabilities of mental disorders and, therefore, execute potential treatment outcomes. This review paper enlists different machine learning algorithms used to detect and diagnose depression. The ML-based depression detection algorithms are categorized into three classes, classification, deep learning, and ensemble. A general model for depression diagnosis involving data extraction, pre-processing, training ML classifier, detection classification, and performance...
Indonesian Journal of Electrical Engineering and Computer Science
Major depressive disorder diagnosis based on PSD imaging of electroencephalogram EEG and AI
aseel ahmed
One of the most common causes of functional frailty is major depressive disorder (MDD). MDD is a chronic condition that requires long-term therapy and professional assistance. Additionally, MDD effective treatment requires early detection. Unfortunately, it has intricated clinical characteristics that make early diagnosis and treatment difficult for clinicians. Furthermore, there are currently no clinically effective diagnostic biomarkers that can confirm an MDD diagnosis. However, electroencephalogram (EEG) data from the brain have recently been used to make a quantitative diagnosis of MDD. In addition, As being among the most cutting-edge artificial intelligence (AI) technologies, deep learning (DL) has exhibited superior performance in a wide range of real-world applications, from computer vision to healthcare. However, an additional challenge could be the extraction of information from the ECG raw data. This paper presents a method for converting EEG data to power spectral densi...
Resting-state EEG Classification of Children and Adolescents Diagnosed with Major Depression Disorder Using Convolutional Neural Network
Amir Jahanian Najafabadi
Previous studies have applied Deep learning models on Electroencephalography data for classification and prediction tasks. In this work, we aim to test a model using convolutional neural network (CNN) in order to identify biomarkers in childhood and Adolescents with major depression disorder from age matched healthy young individuals. CNN used to transfer learning from the VGG16 network. The data were tested using several filtered frequencies combined with a preprocessing pipeline. Overall, results achieved an accuracy of 0.875 and F1-Score of 0.638 and revealed that while lower Delta (0.856 accuracy and 0.539 F1-Score), and higher Theta activity (0.895 accuracy and 0.717 F1-Score), and higher Alpha (0.804 accuracy and 0.606 F1-Score) were classified in MDD compared with healthy. We did not find whether Beta and Gamma are biomarkers for MMD with higher accuracy. We further showed that while in MDD group, delta frequency bands were featured in left temporal, occipital, bilateral frontal ...
