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Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis

Khosla Ashima, Khandnor Padmavati, Chand Trilok

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 1

108--142

Depression is one of the significant contributors to the global burden disease, affecting nearly 264 million people worldwide along with the increasing rate of suicidal deaths. Electroencephalogram (EEG), a non-invasive functional neuroimaging tool has been widely used to study the significant biomarkers for the diagnosis of the disorder. Computational Psychiatry is a novel avenue of research that has shown a tremendous success in the automated diagnosis of depression. The present comprehensive review concentrate on two approaches widely adopted for an EEG based automated diagnosis of depression: Deep Learning (DL) approach and the traditional approach based upon Machine Learning (ML). In this review, we focus on performing the comparative analysis of a variety of signal processing and classification methods adopted in the existing literature for these approaches. We have discussed a variety of EEG based objective biomarkers and the data acquisition systems adopted for the diagnosis of depression. Few EEG studies focusing on multimodal fusion of data have also been explained. Additionally, the research based upon the analysis and prediction of treatment outcome response for depression using EEG signals and machine learning techniques has been briefly discussed to aware the researchers about this emerging field. Finally, the future opportunities and a valuable discussion on major issues related to this field have been summarized that will help the researchers in developing more reliable and computationally intelligent systems in the field of psychiatry.

A new approach to multiple fault diagnosis: A combination of diagnostic matrices, graphs, algebraic and rule-based models. The case of two-layer models

Ligęza A., Kościelny J. M.

International Journal of Applied Mathematics and Computer Science

2008

Vol. 18, no 4

465-476

The diagnosis of multiple faults is significantly more difficult than singular fault diagnosis. However, in realistic industrial systems the possibility of simultaneous occurrence of multiple faults must be taken into account. This paper investigates some of the limitations of the diagnostic model based on the simple binary diagnostic matrix in the case of multiple faults. Several possible interpretations of the diagnostic matrix with rule-based systems are provided and analyzed. A proposal of an extension of the basic, single-level model based on diagnostic matrices to a two-level one, founded on causal analysis and incorporating an OR and an AND matrix is put forward. An approach to the diagnosis of multiple faults based on inconsistency analysis is outlined, and a refinement procedure using a qualitative model of dependencies among system variables is sketched out.