review · Diagnostics
Depression represents a major global public health concern linked to rising suicide rates worldwide. Analysing electroencephalogram signals through artificial intelligence, machine learning, and deep learning techniques offers an objective means to detect and diagnose major depressive disorder by identifying relevant neural biomarkers. A systematic examination of current research outlines the five essential steps in computational depression detection, covering signal preprocessing, feature extraction, and model development. Current methodologies increasingly focus on automating workflows, improving diagnostic accuracy, and addressing the constraints found in existing benchmark datasets. Future developments point towards optimising electrode channel selection, applying data augmentation, and adopting advanced architectures such as encoder-decoder networks and transfer learning. Furthermore, combining electroencephalogram systems with Internet of Things technologies shows promise for continuous mental health tracking and distinguishing between varied forms of depression.
Depression disrupts cognitive and emotional functions, making prompt and accurate identification vital for clinical intervention. Conventional assessments can be subjective and resource-intensive. Utilising electroencephalogram analysis alongside artificial intelligence introduces objective physiological measurements to the diagnostic process. This approach can assist clinicians with automated screening, improve diagnostic precision, and support timely treatment to mitigate severe outcomes such as suicide.
The surveyed approaches could support automated diagnostic software and continuous monitoring applications for psychiatric clinics and digital health providers. Potential implementations include integrating electroencephalogram systems with Internet of Things hardware. However, this technology appears to be at an early research stage, with substantial barriers remaining around dataset limitations, channel selection optimisation, and model reliability before clinical or market deployment is viable.
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<b>Background:</b> Mental disorders are disturbances of brain functions that cause cognitive, affective, volitional, and behavioral functions to be disrupted to varying degrees. One of these disorders is depression, a significant factor contributing to the increase in suicide cases worldwide. Consequently, depression has become a significant public health issue globally. Electroencephalogram (EEG) data can be utilized to diagnose mild depression disorder (MDD), offering valuable insights into the pathophysiological mechanisms underlying mental disorders and enhancing the understanding of MDD. <b>Methods:</b> This survey emphasizes the critical role of EEG in advancing artificial intelligence (AI)-driven approaches for depression diagnosis. By focusing on studies that integrate EEG with machine learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. The survey highlights advancements in EEG preprocessing, feature extraction, and model development, showcasing how these approaches enhance the diagnostic precision, scalability, and automation of depression detection. <b>Results:</b> This survey is distinguished from prior reviews by addressing their limitations and providing researchers with valuable insights for future studies. It offers a comprehensive comparison of ML and DL approaches utilizing EEG and an overview of the five key steps in depression detection. The survey also presents existing datasets for depression diagnosis and critically analyzes their limitations. Furthermore, it explores future directions and challenges, such as enhancing diagnostic robustness with data augmentation techniques and optimizing EEG channel selection for improved accuracy. The potential of transfer learning and encoder-decoder architectures to leverage pre-trained models and enhance diagnostic performance is also discussed. Advancements in feature extraction methods for automated depression diagnosis are highlighted as avenues for improving ML and DL model performance. Additionally, integrating Internet of Things (IoT) devices with EEG for continuous mental health monitoring and distinguishing between different types of depression are identified as critical research areas. Finally, the review emphasizes improving the reliability and predictability of computational intelligence-based models to advance depression diagnosis. <b>Conclusions:</b> This study will serve as a well-organized and helpful reference for researchers working on detecting depression using EEG signals and provide insights into the future directions outlined above, guiding further advancements in the field.
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DOI: 10.3390/diagnostics15020210
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