A Novel Approach for Analysis and Detection of Depression Using Electroencephalogram (EEG) Signals
Depression is a widespread mental health disorder that affects millions of individuals globally. Early and accurate detection of depression is essential for timely intervention and effective treatment. The abstract outlines the key steps involved in developing a depression detection system using EEG...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01101.pdf |
| _version_ | 1797248884246839296 |
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| author | K Chidananda Reddy G. Vijendar Gari Anil Kumar Reddy Madireddy Rohith Raj Kodithyala Harsha Sree Kiran Ravi Singla Tara |
| author_facet | K Chidananda Reddy G. Vijendar Gari Anil Kumar Reddy Madireddy Rohith Raj Kodithyala Harsha Sree Kiran Ravi Singla Tara |
| author_sort | K Chidananda |
| collection | DOAJ |
| description | Depression is a widespread mental health disorder that affects millions of individuals globally. Early and accurate detection of depression is essential for timely intervention and effective treatment. The abstract outlines the key steps involved in developing a depression detection system using EEG, starting with data collection from individuals with and without depression. Preprocessing techniques are applied to clean and normalize the EEG signals, ensuring the removal of artifacts and noise. Feature extraction is a critical phase where relevant information is derived from EEG signals to characterize brain activity patterns associated with depression. After that, the extracted features are used to train machine learning models for the categorization of depression, such as support vector machines (SVMs), random forests, or deep learning architectures (CNN). This highlights the importance of addressing challenges like small and imbalanced datasets, inter-subject variability, and generalizability across diverse populations. Additionally, the model emphasizes the importance of interpretability in machine learning models for depression detection, as it aids in understanding the underlying neural correlates of depression. The abstract gives underscoring the promising prospects of EEG-based depression detection in early diagnosis, personalized treatment, and improved management of depression, ultimately contributing to enhanced mental health care and patient well-being. |
| first_indexed | 2024-04-24T20:21:41Z |
| format | Article |
| id | doaj.art-6163607d62ee455ca851db9490a88b86 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-04-24T20:21:41Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-6163607d62ee455ca851db9490a88b862024-03-22T08:05:26ZengEDP SciencesMATEC Web of Conferences2261-236X2024-01-013920110110.1051/matecconf/202439201101matecconf_icmed2024_01101A Novel Approach for Analysis and Detection of Depression Using Electroencephalogram (EEG) SignalsK Chidananda0Reddy G. Vijendar1Gari Anil Kumar Reddy Madireddy2Rohith Raj Kodithyala3Harsha Sree4Kiran Ravi5Singla Tara6Computer Science and Engineering Department, KG Reddy College of Engineering & TechnologyInformation Technology Department, GRIET, BachupallyInformation Technology Department, GRIET, BachupallyInformation Technology Department, GRIET, BachupallyInformation Technology Department, GRIET, BachupallyDepartment of IT, GRIETLovely Professional UniversityDepression is a widespread mental health disorder that affects millions of individuals globally. Early and accurate detection of depression is essential for timely intervention and effective treatment. The abstract outlines the key steps involved in developing a depression detection system using EEG, starting with data collection from individuals with and without depression. Preprocessing techniques are applied to clean and normalize the EEG signals, ensuring the removal of artifacts and noise. Feature extraction is a critical phase where relevant information is derived from EEG signals to characterize brain activity patterns associated with depression. After that, the extracted features are used to train machine learning models for the categorization of depression, such as support vector machines (SVMs), random forests, or deep learning architectures (CNN). This highlights the importance of addressing challenges like small and imbalanced datasets, inter-subject variability, and generalizability across diverse populations. Additionally, the model emphasizes the importance of interpretability in machine learning models for depression detection, as it aids in understanding the underlying neural correlates of depression. The abstract gives underscoring the promising prospects of EEG-based depression detection in early diagnosis, personalized treatment, and improved management of depression, ultimately contributing to enhanced mental health care and patient well-being.https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01101.pdf |
| spellingShingle | K Chidananda Reddy G. Vijendar Gari Anil Kumar Reddy Madireddy Rohith Raj Kodithyala Harsha Sree Kiran Ravi Singla Tara A Novel Approach for Analysis and Detection of Depression Using Electroencephalogram (EEG) Signals MATEC Web of Conferences |
| title | A Novel Approach for Analysis and Detection of Depression Using Electroencephalogram (EEG) Signals |
| title_full | A Novel Approach for Analysis and Detection of Depression Using Electroencephalogram (EEG) Signals |
| title_fullStr | A Novel Approach for Analysis and Detection of Depression Using Electroencephalogram (EEG) Signals |
| title_full_unstemmed | A Novel Approach for Analysis and Detection of Depression Using Electroencephalogram (EEG) Signals |
| title_short | A Novel Approach for Analysis and Detection of Depression Using Electroencephalogram (EEG) Signals |
| title_sort | novel approach for analysis and detection of depression using electroencephalogram eeg signals |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01101.pdf |
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