Early-Stage Non-Severe Depression Detection Using a Novel Convolutional Neural Network Approach Based on Resting-State EEG Data
Over 300 million people worldwide are affected by depression, with symptoms that have a major impact on patients and, in the worst cases, can lead to suicide. As the severity of the disease increases over time, early detection can save a patient’s life. The disease is diagnosed by profess...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10757418/ |
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| author | Pascal Penava Ricardo Buettner |
| author_facet | Pascal Penava Ricardo Buettner |
| author_sort | Pascal Penava |
| collection | DOAJ |
| description | Over 300 million people worldwide are affected by depression, with symptoms that have a major impact on patients and, in the worst cases, can lead to suicide. As the severity of the disease increases over time, early detection can save a patient’s life. The disease is diagnosed by professionals using questionnaires that might be influenced by biases, and of which the accuracy and reliability are not guaranteed. For this reason, an increasing number of studies are looking at physiological ways of detecting the disease, with electroencephalogram-based machine learning prediction models having been successful in recent years. However, the focus is not on the early detection of mild depression, which can be the entry point to major depression. In this work, we developed a deep learning based model using a 1D convolutional neural network to detect mild depression in resting-state electroencephalogram data. We evaluated the model using a realistic world-like dataset and were able to achieve a balanced accuracy of 69.21%. With this result, we are setting a new benchmark for resting-state-based early detection. Due to the low level of preprocessing and the associated fast computing time and low computational intensity, our innovative approach can serve as a basis for applications in the real world. This enables patients with suitable hardware to recognize the disease themselves at an early stage and thus receive timely treatment to prevent further development. |
| format | Article |
| id | doaj-art-de9bce6708b84f64b4cfead567ff6937 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-de9bce6708b84f64b4cfead567ff69372024-11-26T00:01:31ZengIEEEIEEE Access2169-35362024-01-011217338017338910.1109/ACCESS.2024.350254010757418Early-Stage Non-Severe Depression Detection Using a Novel Convolutional Neural Network Approach Based on Resting-State EEG DataPascal Penava0https://orcid.org/0009-0004-9870-8193Ricardo Buettner1https://orcid.org/0000-0003-2263-6408Chair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armes Forces Hamburg, Hamburg, GermanyChair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armes Forces Hamburg, Hamburg, GermanyOver 300 million people worldwide are affected by depression, with symptoms that have a major impact on patients and, in the worst cases, can lead to suicide. As the severity of the disease increases over time, early detection can save a patient’s life. The disease is diagnosed by professionals using questionnaires that might be influenced by biases, and of which the accuracy and reliability are not guaranteed. For this reason, an increasing number of studies are looking at physiological ways of detecting the disease, with electroencephalogram-based machine learning prediction models having been successful in recent years. However, the focus is not on the early detection of mild depression, which can be the entry point to major depression. In this work, we developed a deep learning based model using a 1D convolutional neural network to detect mild depression in resting-state electroencephalogram data. We evaluated the model using a realistic world-like dataset and were able to achieve a balanced accuracy of 69.21%. With this result, we are setting a new benchmark for resting-state-based early detection. Due to the low level of preprocessing and the associated fast computing time and low computational intensity, our innovative approach can serve as a basis for applications in the real world. This enables patients with suitable hardware to recognize the disease themselves at an early stage and thus receive timely treatment to prevent further development.https://ieeexplore.ieee.org/document/10757418/Depression detectionearly-stageEEGresting-stateCNN |
| spellingShingle | Pascal Penava Ricardo Buettner Early-Stage Non-Severe Depression Detection Using a Novel Convolutional Neural Network Approach Based on Resting-State EEG Data IEEE Access Depression detection early-stage EEG resting-state CNN |
| title | Early-Stage Non-Severe Depression Detection Using a Novel Convolutional Neural Network Approach Based on Resting-State EEG Data |
| title_full | Early-Stage Non-Severe Depression Detection Using a Novel Convolutional Neural Network Approach Based on Resting-State EEG Data |
| title_fullStr | Early-Stage Non-Severe Depression Detection Using a Novel Convolutional Neural Network Approach Based on Resting-State EEG Data |
| title_full_unstemmed | Early-Stage Non-Severe Depression Detection Using a Novel Convolutional Neural Network Approach Based on Resting-State EEG Data |
| title_short | Early-Stage Non-Severe Depression Detection Using a Novel Convolutional Neural Network Approach Based on Resting-State EEG Data |
| title_sort | early stage non severe depression detection using a novel convolutional neural network approach based on resting state eeg data |
| topic | Depression detection early-stage EEG resting-state CNN |
| url | https://ieeexplore.ieee.org/document/10757418/ |
| work_keys_str_mv | AT pascalpenava earlystagenonseveredepressiondetectionusinganovelconvolutionalneuralnetworkapproachbasedonrestingstateeegdata AT ricardobuettner earlystagenonseveredepressiondetectionusinganovelconvolutionalneuralnetworkapproachbasedonrestingstateeegdata |