Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning
Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) has gained attention as a potential objective tool...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/409 |
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| author | Damir Mulc Jaksa Vukojevic Eda Kalafatic Mario Cifrek Domagoj Vidovic Alan Jovic |
| author_facet | Damir Mulc Jaksa Vukojevic Eda Kalafatic Mario Cifrek Domagoj Vidovic Alan Jovic |
| author_sort | Damir Mulc |
| collection | DOAJ |
| description | Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) has gained attention as a potential objective tool for diagnosing depression. This study aimed to evaluate the effectiveness of EEG in identifying MDD by analyzing 140 EEG recordings from patients diagnosed with depression and healthy volunteers. Using various machine learning (ML) classification models, we achieved up to 80% accuracy in distinguishing individuals with MDD from healthy controls. Despite its promise, this approach has limitations. The variability in the clinical and biological presentations of depression, as well as patient-specific confounding factors, must be carefully considered when integrating ML technologies into clinical practice. Nevertheless, our findings suggest that an EEG-based ML model holds potential as a diagnostic aid for MDD, paving the way for further refinement and clinical application. |
| format | Article |
| id | doaj-art-af24c396bd58458e88e396732ed7a2f9 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-af24c396bd58458e88e396732ed7a2f92025-01-24T13:48:49ZengMDPI AGSensors1424-82202025-01-0125240910.3390/s25020409Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine LearningDamir Mulc0Jaksa Vukojevic1Eda Kalafatic2Mario Cifrek3Domagoj Vidovic4Alan Jovic5University Psychiatric Hospital Vrapče, Bolnička Cesta 32, 10000 Zagreb, CroatiaUniversity Psychiatric Hospital Vrapče, Bolnička Cesta 32, 10000 Zagreb, CroatiaUniversity of Zagreb Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, CroatiaUniversity of Zagreb Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, CroatiaUniversity Psychiatric Hospital Vrapče, Bolnička Cesta 32, 10000 Zagreb, CroatiaUniversity of Zagreb Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, CroatiaMajor depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) has gained attention as a potential objective tool for diagnosing depression. This study aimed to evaluate the effectiveness of EEG in identifying MDD by analyzing 140 EEG recordings from patients diagnosed with depression and healthy volunteers. Using various machine learning (ML) classification models, we achieved up to 80% accuracy in distinguishing individuals with MDD from healthy controls. Despite its promise, this approach has limitations. The variability in the clinical and biological presentations of depression, as well as patient-specific confounding factors, must be carefully considered when integrating ML technologies into clinical practice. Nevertheless, our findings suggest that an EEG-based ML model holds potential as a diagnostic aid for MDD, paving the way for further refinement and clinical application.https://www.mdpi.com/1424-8220/25/2/409depression detectionmajor depressive disorderelectroencephalographymachine learning |
| spellingShingle | Damir Mulc Jaksa Vukojevic Eda Kalafatic Mario Cifrek Domagoj Vidovic Alan Jovic Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning Sensors depression detection major depressive disorder electroencephalography machine learning |
| title | Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning |
| title_full | Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning |
| title_fullStr | Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning |
| title_full_unstemmed | Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning |
| title_short | Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning |
| title_sort | opportunities and challenges for clinical practice in detecting depression using eeg and machine learning |
| topic | depression detection major depressive disorder electroencephalography machine learning |
| url | https://www.mdpi.com/1424-8220/25/2/409 |
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