⚡ Quick Summary
This study presents a novel cloud-based computer-aided depression diagnostic (CCADD) system that utilizes EEG signals and deep learning techniques to detect depression. The system achieved impressive accuracy rates of up to 98% in identifying major depressive disorder, highlighting its potential for early diagnosis and intervention.
🔍 Key Details
- 📊 Datasets Used: Two publicly available databases, including 31 individuals with major depressive disorder and 90 participants in total.
- ⚙️ Technology: Pre-trained convolutional neural network, ResNet18, fine-tuned on synchrosqueezed wavelet transform (SSWT) images.
- 🏆 Performance Metrics: Highest average accuracies of 98%, 97%, 91%, and 88% for different lobes across datasets.
🔑 Key Takeaways
- 🧠 EEG analysis is being leveraged for automated depression detection.
- 💻 Cloud-based systems can facilitate remote diagnosis and monitoring.
- 🔍 Transfer learning enhances model performance by utilizing pre-trained networks.
- 📈 High accuracy rates (up to 98%) demonstrate the effectiveness of the CCADD system.
- 🧪 Data augmentation techniques were crucial in optimizing model performance.
- 🌐 Potential for widespread application in mental health diagnostics and management.
- 📅 Study conducted post-COVID-19, addressing rising depression rates.
- 🔬 Validation method: Leave-subjects-out cross-validation with 20 subjects.
📚 Background
The rise in depression rates following the COVID-19 pandemic has underscored the urgent need for effective diagnostic tools. Traditional methods of diagnosing depression can be subjective and often rely on self-reported symptoms. The integration of electroencephalogram (EEG) technology with advanced machine learning techniques offers a promising alternative for early and accurate diagnosis.
🗒️ Study
This study aimed to develop a cloud-based system for detecting depression using EEG signals. Researchers utilized data from two publicly available databases, focusing on optimizing the model through various experiments. The ResNet18 architecture was fine-tuned on images generated from EEG signals using synchrosqueezed wavelet transform (SSWT), showcasing the innovative approach to mental health diagnostics.
📈 Results
The CCADD system demonstrated remarkable performance, achieving average accuracies of 98% and 97% for the parietal and central lobes in Database I, respectively. The corresponding F-scores were also impressive, reaching 96.27% and 94.87%. The study’s findings indicate a strong potential for this technology in clinical settings, particularly for early detection of major depressive disorder.
🌍 Impact and Implications
The introduction of a cloud-based model for depression detection could significantly transform mental health diagnostics. By utilizing EEG signals and advanced machine learning, healthcare providers can offer more accurate and timely interventions. This technology not only enhances diagnostic capabilities but also paves the way for improved management of depression, potentially reducing the burden on healthcare systems.
🔮 Conclusion
This study highlights the transformative potential of integrating cloud-based EEG analysis with deep learning for depression detection. The high accuracy rates achieved by the CCADD system suggest a promising future for automated mental health diagnostics. Continued research and development in this area could lead to significant advancements in how we understand and treat depression.
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Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform.
Abstract
Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tackle this, we developed a cloud-based computer-aided depression diagnostic (CCADD) system that utilizes EEG signals from local databases. This system was optimized through a series of experiments to identify the most accurate model. The experiments employed a pre-trained convolutional neural network, ResNet18, fine-tuned on time-frequency synchrosqueezed wavelet transform (SSWT) images derived from EEG signals. Various data augmentation methods, including image processing techniques and noises, were applied to identify the best model for CCADD. To offer this device with minimal electrodes, we aimed to balance high accuracy with fewer electrodes. Two publicly databases were evaluated using this approach. Dataset I included 31 individuals detected with major depressive disorder and a control class of 27 age-matched healthy subjects. Dataset II comprised 90 participants, with 45 diagnosed with depression and 45 healthy controls. The leave-subjects-out cross-validation method with 20 subjects was used to validate the proposed method. The highest average accuracies for the selected model are 98%, 97%, 91%, and 88% for the parietal and central lobes in Databases I and II, respectively. The corresponding highest f-scores are 96.27%, 94.87%, 90.56%, and 89.65%. The highest intra-database accuracy and F1-score are 75.10% and 73.56% when training with SSWT images from Database II and testing with parietal images from Database I. This study introduces a novel cloud-based model for depression detection, paving the way for effective diagnostic tools and potentially revolutionizing depression management.
Author: [‘Bagherzadeh S’, ‘Norouzi MR’, ‘Ghasri A’, ‘Tolou Kouroshi P’, ‘Bahri Hampa S’, ‘Farokhshad F’, ‘Shalbaf A’]
Journal: Sci Rep
Citation: Bagherzadeh S, et al. Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform. Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform. 2025; 15:18008. doi: 10.1038/s41598-025-02452-7