DeprNet: A Deep Convolution Neural Network Framework for Detecting Depression Using EEG

Seal, Ayan; Bajpai, Rishabh; Agnihotri, Jagriti; Yazidi, Anis; Liu, Xinyue; Krejcar, Ondřej

doi:10.1109/tim.2021.3053999

“…A new, 18-layer, CNN-based framework named DeprNet has been proposed by [33] in which both spatial and temporal dimensions of the EEG data were utilized during training. Produced artifacts by eye blink eliminated from EEG signals with the help of independent component analysis (ICA) Method.…”

Section: Deep Learning Methods For Depression Detection Using Eeg Signalsmentioning

confidence: 99%

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Safayari¹,

Bolhasani²

2021

Preprint

View full text Add to dashboard Cite

Depression is considered by WHO as the main contributor to global disability and it poses dangerous threats to approximately all aspects of human life, in particular public and private health. This mental disorder is usually characterized by considerable changes in feelings, routines, or thoughts. With respect to the fact that early diagnosis of this illness would be of critical importance ineffective treatment, some development has occurred in the purpose of depression detection. EEG signals reflect the working status of the human brain by which are considered the most proper tools for a depression diagnosis. Deep learning algorithms have the capacity of pattern discovery and extracting features from the raw data which is fed into them. Owing to this significant characteristic of deep learning, recently, these methods have intensely utilized in the diverse field of researches, specifically medicine and healthcare. Thereby, in this article, we aimed to review all papers concentrated on using deep learning to detect or predict depressive subjects with the help of EEG signals as input data. Regarding the adopted search method, we finally evaluated 22 articles between 2016 and 2021. This article which is organized according to the systematic literature review (SLR) method, provides complete summaries of all exploited studies and compares the noticeable aspects of them. Moreover, some statistical analysis performs to gain a depth perception of the general ideas of the latest researches in this area. A pattern of a five-step procedure was also established by which almost all reviewed articles fulfilled the goal of depression detection. Finally, open issues and challenges in this way of depression diagnosis or prediction and suggested works as the future directions discussed.

show abstract

“…A new, 18-layer, CNN-based framework named DeprNet has been proposed by [34] in which both spatial and temporal dimensions of the EEG data have been utilized during training. Produced artifacts by eye blink have been removed from EEG signals with the help of independent component analysis (ICA) Method.…”

Section: Deep Learning Methods For Depression Detection Using Eeg Signalsmentioning

confidence: 99%

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Safayari¹,

Bolhasani²

2021

Preprint

View full text Add to dashboard Cite

Depression is considered by WHO as the main contributor to global disability and it poses dangerous threats to approximately all aspects of human life, in particular public and private health. This mental disorder is usually characterized by considerable changes in feelings, routines, or thoughts. With respect to the fact that early diagnosis of this illness would be of critical importance ineffective treatment, some development has occurred in the purpose of depression detection. EEG signals reflect the working status of the human brain by which are considered the most proper tools for a depression diagnosis. Deep learning algorithms have the capacity of pattern discovery and extracting features from the raw data which is fed into them. Owing to this significant characteristic of deep learning, recently, these methods have intensely utilized in the diverse field of researches, specifically medicine and healthcare. Thereby, in this article, we aimed to review all papers concentrated on using deep learning to detect or predict depressive subjects with the help of EEG signals as input data. Regarding the adopted search method, we finally evaluated 22 articles between 2016 and 2021. This article which is organized according to the systematic literature review (SLR) method, provides complete summaries of all exploited studies and compares the noticeable aspects of them. Moreover, some statistical analysis performs to gain a depth perception of the general ideas of the latest researches in this area. A pattern of a five-step procedure was also established by which almost all reviewed articles fulfilled the goal of depression detection. Finally, open issues and challenges in this way of depression diagnosis or prediction and suggested works as the future directions discussed.

show abstract

“…Machine learning algorithms applied to EEG signals have also shined in other fields. Seal et al (2021) In recent years, more and more new methods have begun to be applied to the automatic detection of epilepsy. The development of faster and more accurate epilepsy detection models will contribute to epilepsy detection techniques in clinical diagnosis and the development of portable and integrated epilepsy detection equipment.…”

Section: Other Applications Of Machine Learningmentioning

confidence: 99%

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

Liu

¹

,

Xiao

²

,

Xu

³

et al. 2021

Front. Syst. Neurosci.

View full text Add to dashboard Cite

Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.

show abstract

DeprNet: A Deep Convolution Neural Network Framework for Detecting Depression Using EEG

Cited by 144 publications

References 40 publications

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

Contact Info

Product

Resources

About