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Citation |
Bashir N, Narejo S, Naz B, Ismail F, Anjum MR, Butt A, Anwar S, Prasad R. Wirel. Personal Commun. 2023; ePub(ePub): ePub. |
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Copyright |
(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group) |
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DOI |
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PMID |
unavailable |
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Abstract |
According to World Health Organization (WHO) report, every 40 seconds a person attempts suicide globally. Depression, one of the world's most prevailing diseases has become a reason behind these suicides. It is believed that early diagnosis of major depressive disorder (MDD) can reduce the adversity of this heinous deformity. For few years various machine learning and advanced neurocomputing techniques are being utilized in Electroencephalogram (EEG) based detection of multiple neurological diseases. In the proposed study, an EEG based screening of MDD is presented while using various Machine Learning and one Deep Learning approach. The majority of previous EEG based MDD decoding research has concentrated on a limited features. It was necessary to conduct in-depth comparisons of different approaches, besides more detailed feature-based EEG analysis. This research starts with the creation of a complete feature-based framework, which is then further compared against the state of the art end to end techniques. The K-nearest neighbors (KNN) model outperformed the other models and gained an accuracy of 87.5%. While long short term memory (LSTM) model acquired an accuracy of 83.3%. This study can further support in clinical diagnosis of multiple stages of MDD and can attempt to provide an early intervention. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. Language: en |
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Keywords |
World Health Organization; Major depressive disorder; Electroencephalography; Learning systems; Early diagnosis; Machine-learning; Long short-term memory; Electroencephalogram signals; Nearest neighbor search; Feature based framework; Feature-based; Learning frameworks; Machine learning and deep learning; Machine learning and Deep learning; Neurocomputing |