Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach-Reference-Cited by-同舟云学术

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Published:2020-07-26 Issue:2 Volume:15 Page:239-252
ISSN:1871-4080
Container-title:Cognitive Neurodynamics
language:en
Short-container-title:Cogn Neurodyn

Author:

Saeedi Abdolkarim,Saeedi Maryam,Maghsoudi Arash,Shalbaf Ahmad^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience

Link

https://link.springer.com/content/pdf/10.1007/s11571-020-09619-0.pdf

Reference46 articles.

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2. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H (2018a) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278

3. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP (2018b) Automated EEG-based screening of depression using deep convolutional neural network. Comput Methods Programs Biomed 161:103–113

4. Afshani F, Shalbaf A, Shalbaf R, Sleigh J (2019) Frontal–temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia. Cogn Neurodyn 13(6):531–540

5. Ahmadlou M, Adeli H, Adeli A (2012) Fractality analysis of the frontal brain in major depressive disorder. Int J Psychophysiol 85:206–211

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