The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Computer-Aided Diagnosis of Depression Using EEG Signals
Open accessAbstract
The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression.
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Cites
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Davidson, R. J. (1998) · Psychophysiology · doi:10.1017/s0048577298000134 · Cited by 646
The stability of resting frontal electroencephalographic asymmetry in depression
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Cited by
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Zhong, P., Wang, D., & Miao, C. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2994159 · Cited by 910
Automated EEG-based screening of depression using deep convolutional neural network
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H., & Subha, D. P. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.012 · Cited by 650
Self-citationAl-Nafjan, A., Hosny, M., Al-Ohali, Y., & Al-Wabil, A. (2017) · Applied Sciences · doi:10.3390/app7121239 · Cited by 300
EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network
Cui, H., Liu, A., Zhang, X., Chen, X., Wang, K., & Chen, X. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106243 · Cited by 284
A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals
Acharya, U. R., Sudarshan, V. K., Adeli, H., Santhosh, J., Koh, J. E. W., Puthankatti, S. D., & Adeli, A. (2015) · European Neurology · doi:10.1159/000438457 · Cited by 270
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Metadata
- Authors
- Acharya, U. R., Sudarshan, V. K., Adeli, H., Santhosh, J., Koh, J. E. W., & Adeli, A.
- Venue
- European Neurology
- Publisher
- S. Karger AG
- Published
- 2015
- Volume
- 73
- Issue
- 5-6
- Pages
- 329-336
- ISSN
- 0014-3022 (ISSN-L)
- 1421-9913
- OpenAlex
- W1488104929
- Cited by
- 197
- References
- 3
- FWCI
- 5.72
- Type
- journal article
- Language
- en
- Open access
- bronze
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