The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals
Abstract
Depression is a mental disorder characterized by persistent occurrences of lower mood states in the affected person. The electroencephalogram (EEG) signals are highly complex, nonlinear, and nonstationary in nature. The characteristics of the signal vary with the age and mental state of the subject. The signs of abnormality may be invisible to the naked eyes. Even when they are visible, deciphering the minute changes indicating abnormality is tedious and time consuming for the clinicians. This paper presents a novel method for automated EEG-based diagnosis of depression using nonlinear methods: fractal dimension, largest Lyapunov exponent, sample entropy, detrended fluctuation analysis, Hurst's exponent, higher order spectra, and recurrence quantification analysis. A novel Depression Diagnosis Index (DDI) is presented through judicious combination of the nonlinear features. The DDI calculated automatically based on the EEG recordings can be used to diagnose depression objectively using just one numeric value. Also, these features extracted from nonlinear methods are ranked using the t value and fed to the support vector machine (SVM) classifier. The SVM classifier yielded the highest classification performance with an average accuracy of about 98%, sensitivity of about 97%, and specificity of about 98.5%.
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Cites
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Data Mining Concepts and Techniques Third Edition
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Self-citationHosseinifard, B., Moradi, M. H., & Rostami, R. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2012.10.008 · Cited by 558
Fractality analysis of frontal brain in major depressive disorder
Ahmadlou, M., Adeli, H., & Adeli, A. (2012) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2012.05.001 · Cited by 286
Self-citationGrin-Yatsenko, V. A., Baas, I., Ponomarev, V. A., & Kropotov, J. D. (2010) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.11.015 · Cited by 211
Computer-Aided Diagnosis of Depression Using EEG Signals
Acharya, U. R., Sudarshan, V. K., Adeli, H., Santhosh, J., Koh, J. E. W., & Adeli, A. (2015) · European Neurology · doi:10.1159/000381950 · Cited by 197
Self-citationFrontal EEG predictors of treatment outcome in major depressive disorder
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Improved visibility graph fractality with application for the diagnosis of Autism Spectrum Disorder
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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-citationRafiei, M. H., & Adeli, H. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2017.10.070 · Cited by 401
Self-citationAcharya, U. R., Fernandes, S. L., WeiKoh, J. E., Ciaccio, E. J., Fabell, M. K. M., Tanik, U. J., Rajinikanth, V., & Yeong, C. H. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1428-9 · Cited by 325
Self-citationDeep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals
Oh, S. L., Vicnesh, J., Ciaccio, E. J., Yuvaraj, R., & Acharya, U. R. (2019) · Applied Sciences · doi:10.3390/app9142870 · Cited by 304
Self-citationČukić, M., Stokić, M., Simić, S., & Pokrajac, D. (2020) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09581-x · Cited by 130
Metadata
- Authors
- Acharya, U. R., Sudarshan, V. K., Adeli, H., Santhosh, J., Koh, J. E. W., Puthankatti, S. D., & Adeli, A.
- Venue
- European Neurology
- Publisher
- S. Karger AG
- Published
- 2015
- Volume
- 74
- Issue
- 1-2
- Pages
- 79-83
- ISSN
- 0014-3022 (ISSN-L)
- 1421-9913
- OpenAlex
- W1879995557
- Cited by
- 270
- References
- 22
- FWCI
- 9.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- Acharya, U. R., Sudarshan, V. K., Adeli, H., Santhosh, J., Koh, J. E. W., Puthankatti, S. D., & Adeli, A. (2015). A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals. European Neurology. https://doi.org/10.1159/000438457
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