Depression Detection from Electroencephalogram Signals Induced by Affective Auditory Stimuli

Shen, Jian; Zhang, Xiaowei; Li, Junlei; Li, Yuanxi; Feng, Lei; Hu, Changqing; Ding, Zhijie; Wang, Gang; Hu, Bin

doi:10.1109/acii.2019.8925528

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…Atualmente, há diversas aplicações que utilizam, direta ou indiretamente, sinais biológicos, como identificação de condições patológicas [1,7,8,2] e compressão de sinais [9,10,11]. O sinal biológico de EEG tem sido bastante explorado pela comunidade científica [1,9,6,12,13,14,3]. Isso é devido ao amplo número de aplicações médicas que o sinal possui.…”

Section: Introductionunclassified

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Classificação de Sinais de EEG para Diagnóstico do Transtorno do Espectro Autista utilizando CNN e RNN

Silva,

Sousa Neto,

Furtado

et al. 2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Pesquisas recentes indicam um crescente número de crianças diagnosticadas com TEA, o que impulsiona o estudo e desenvolvimento de novas ferramentas para diagnóstico precoce desta doença, para tratamento nos primeiros anos. O presente trabalho explora uma abordagem com CNN e RNN combinadas com técnicas de pré-processamento e extração de características, a partir da classificação de sinais de eletroencefalograma, para o diagnóstico do TEA. Utilizando-se a base de dados de Milne et al., mostra-se que o melhor modelo proposto alcançou uma acurácia de classificação de 99, 51%, o que corrobora a capacidade do sistema proposto em distinguir indivíduos com TEA dos tipicamente desenvolvidos.

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Section: Introductionunclassified

“…Como os sinais de EEG são registros da atividade elétrica do cérebro [2], pode-se empregá-los no diagnóstico e tratamento de doenças neurológicas, tais como epilepsia, Parkinson e Alzheimer [12], entre outras. Outra aplicação clínica do sinal é o auxílio na avaliação de transtornos mentais, e.g., depressão e autismo [13,14].…”

Section: Introductionunclassified

Classificação de Sinais de EEG para Diagnóstico do Transtorno do Espectro Autista utilizando CNN e RNN

Silva,

Sousa Neto,

Furtado

et al. 2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…EEG data, which are recordings of the brain's electrical activity, are frequently utilized in clinical and academic contexts to identify and investigate neurological problems. Traditional approaches to analyzing EEG signals involve handcrafted feature extraction and signal processing techniques, which can be timeconsuming and require domain-specific knowledge [2] Machine learning's area of deep learning has shown rapid growth and advancement in recent years. Deep learning has proven to perform better in various applications, including computer versions, speech recognition, and natural language processing, among others.…”

Section: Introductionmentioning

confidence: 99%

EEG signals of depression diagnosis in recognition using deep learning methods

Chen

2023

ACE

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Depression is a mental disease which symptom is people feel negative about life during long period. With the fast-paced social lifestyle, increasingly stress make people tired toward their life and work. The prevalence rate of depression become higher worldwide. However, the frequent way to detect the depression is depended on the Self-Rating Depression Scale (SDS) and the diagnosis given by the Professional psychologist. Those methods performance is always unstable and inefficiency. Electroencephalogram (EEG) as a functional neuron signal have been widely used in neurology diagnosis. It has been testified as a great tool to diagnose the depression. Deep learning (DL) can extract some latent features from complex data which tradition way cant analyze efficiently. For this reason DL intensely utilized in Medical field.This paper aims to analyze the general and feasible solution of applying deep learning to diagnose depression through EEG signals. It reviews the relevant literature in this field in recent years, summarizes the corresponding methods and breakthroughs used in this paper, and systematically constructs this solution. Finally, based on the shortcomings and deficiencies found in these papers, the main problems that need to be addressed in the future are proposed, and the future potential of this field is discussed.

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“…Due to this complexity, available abnormality would be difficult to detect with the naked eyes. These properties have made physiological signals are deemed to be valuable tools for depression detection [8][9].…”

Section: Introductionmentioning

confidence: 99%

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

Safayari¹,

Bolhasani²

2021

Preprint

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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.

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Depression Detection from Electroencephalogram Signals Induced by Affective Auditory Stimuli

Cited by 6 publications

References 32 publications

Classificação de Sinais de EEG para Diagnóstico do Transtorno do Espectro Autista utilizando CNN e RNN

Classificação de Sinais de EEG para Diagnóstico do Transtorno do Espectro Autista utilizando CNN e RNN

EEG signals of depression diagnosis in recognition using deep learning methods

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

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