Deep LSTM Recurrent Neural Network for Anxiety Classification from EEG in Adolescents with Autism

Penchina, Brian; Sundaresan, Alamelu; Cheong, Sean; Martel, Adrien

doi:10.1007/978-3-030-59277-6_21

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…Electroencephalograms (EEG) has been used extensively to study the internal brain states by recording the electrical activity of brain waves for predicting diseases such as Parkinson's [16]. Even though using EEG to study Autism could have contradictions based on the experimental conditions during EEG registration between subjects, age differences, and diversity of subjects, the abnormal EEG laterization in subjects with ASD can be leveraged to build AI models to predict traits of autism [17]. [20].…”

Section: B Artificially Intelligent Methods In Behavioral Healthmentioning

confidence: 99%

AI-Augmented Behavior Analysis for Children With Developmental Disabilities: Building Toward Precision Treatment

Ghafghazi

Carnett

Neely

et al. 2021

IEEE Syst. Man Cybern. Mag.

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Autism spectrum disorder is a developmental disorder characterized by significant social, communication, and behavioral challenges. Individuals diagnosed with autism, intellectual, and developmental disabilities (AUIDD) typically require long-term care and targeted treatment and teaching. Effective treatment of AUIDD relies on efficient and careful behavioral observations done by trained applied behavioral analysts (ABAs). However, this process overburdens ABAs by requiring the clinicians to collect and analyze data, identify the problem behaviors, conduct pattern analysis to categorize and predict categorical outcomes, hypothesize responsiveness to treatments, and detect the effects of treatment plans. Successful integration of digital technologies into clinical decision-making pipelines and the advancements in automated decision-making using Artificial Intelligence (AI) algorithms highlights the importance of augmenting teaching and treatments using novel algorithms and high-fidelity sensors. In this article, we present an AI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to provide personalized treatment and learning plans to AUIDD individuals. By defining systematic experiments along with automated data collection and analysis, AI-ABA can promote self-regulative behavior using reinforcement-based augmented or virtual reality and other mobile platforms. Thus, AI-ABA could assist clinicians to focus on making precise data-driven decisions and increase the quality of individualized interventions for individuals with AUIDD.

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Section: B Artificially Intelligent Methods In Behavioral Healthmentioning

confidence: 99%

AI-Augmented Behavior Analysis for Children With Developmental Disabilities: Building Toward Precision Treatment

Ghafghazi

Carnett

Neely

et al. 2021

IEEE Syst. Man Cybern. Mag.

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“…In addition, this allows us to extract data from each interval, and enables us to place more importance on small differences in the data. Further, other studies in the past have shown that RNNs have promise in the task of mental disorder prediction using EEG data 42,43 . In addition, attention mechanisms are often used in conjunction with RNNs 44 .…”

Section: Gated Recurrent Unit (Gru)mentioning

confidence: 99%

Multi-Modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for Mental Disorder Prediction

Greiner,

Zhang

2024

Preprint

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Early detection and accurate diagnosis of mental disorders is difficult due to the complexity of the diagnostic process, resulting inconditions being left undiagnosed or misdiagnosed. Previous studies have demonstrated that features of Electroencephalogram (EEG) data, such as Power Spectral Density (PSD), are useful biomarkers for indicating the onset of various mental disorders. Existing models using EEG data are typically trained to distinguish between healthy and afflicted individuals, and they are unable to distinguish between individuals with different disorders. We propose MENTAL (Multi-modal EEG NEO-FFI with Trained Attention Layer) to predict an individual’s mental state using both EEG and Neo-Five Factor Inventory (NEO-FFI) personality data. We include an attention layer that captures the interactions between personality traits and PSD features, and emphasizes the important PSD features. MENTAL features a Recurrent Neural Network (RNN) to model the temporal nature of EEG data. We train our model with the Two Decades Brainclinics Research Archive for Insights in Neuroscience (TDBRAIN) dataset, which consists of 1274 healthy and psychiatric individuals including over 30 different diagnoses. MENTAL is able to achieve 93.3% accuracy when trained to classify between healthy individuals and those with ADHD. When trained to identify individuals with ADHD from among 33 disorder classes, MENTAL improves accuracy from 70.5% to 81.7%. MENTAL also demonstrates over 20% improvement in predictive accuracy when trained for MDD prediction. For the multi-class classification task of three classes, MENTAL improves accuracy by nearly 10%, and for five classes, by nearly 8%. MENTAL is the first multi-modal model that utilizes both EEG and NEO-FFI data for the task of mental disorder prediction. We are one of the first groups to utilize TDBRAIN for automated disorder classification. MENTAL is accessible and cost-effective, as EEG is more affordable than other neuroimaging methods such as MRI, and the NEO-FFI is a self-reported survey. Our model can lead to acceptance and support for individuals living with mental health challenges and improve quality of life in our society.

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“…Pancerz, Paja, and Gomula (2016) Usar dados apropriados para construir classificadores mais simples e precisos, usando IA, na detecção do TEA. Penchina, Sundaresan, Cheong, and Martel (2020) Propor um classificador de aprendizagem profunda no tratamento de ansiedade em pessoas com TEA. Rabbi, Hasan, Champa, and Zaman (2021) Criar um modelo de rede neural convolucional para detecção em estágio inicial do TEA Saranya and Anandan (2021) Propor uma técnica de aprendizagem de máquina no prognóstico do TEA.…”

Section: Critérios De Seleção De Estudounclassified

“…Em Shahamiri and Thabtah (2020), foi proposto um novo sistema de triagem de autismo que substitui as funções convencionais de pontuação em métodos de triagem clássicos usando Redes Neurais Convolucionais (CNN). Em Penchina et al (2020), foi proposto classificador de aprendizagem profunda usando Rede Neural Recorrente de Curto Prazo (LSTM RNN), capaz de identificar estados de ansiedade em pacientes adolescentes com autismo. Em Bowrin and Iqbal (2020), avaliou-se a eficácia de uma intervenção que incorpora tecnologias de agentes conversacionais de Inteligência Artificial e técnicas de terapia de ativação comportamental voltadas para pessoas com autismo.…”

Section: Técnicas De Detecção E Intervençãounclassified

Sistemas Inteligentes para Desenvolvimento de Competências e Diagnóstico de pessoas com o Transtorno do Espectro do Autismo: Uma Revisão Sistemática da Literatura

Valentim

Asnis

Elias

et al. 2022

RBIE

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O Transtorno do Espectro do Autismo (TEA) é um tema que pode ser discutido em diversas áreas do conhecimento. Periódicos e conferências científicas importantes incluem temas relacionados ao TEA no seu âmbito de interesse. Neste contexto, a área de Inteligência Artificial (IA), com o seu crescimento rápido e a grande variedade de locais de disseminação, também tem abordado o TEA e o desenvolvimento de tecnologias, o que representa atualmente um desafio aos pesquisadores em manter o acompanhamento das novas descobertas e tendências nesta área. Nesta perspectiva, este trabalho coletou e analisou artigos de pesquisa que abordaram o TEA considerando aplicações de IA para o desenvolvimento de tecnologias no âmbito do treinamento educacional e comportamental, com o objetivo de detectar tendências, padrões, lacunas e oportunidades de pesquisa, proporcionando uma visão detalhada deste campo de pesquisa em um período de 6 anos. Adotou-se a abordagem de Revisão Sistemática da Literatura (RSL) como metodologia para responder importantes questões de pesquisa na área.

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Deep LSTM Recurrent Neural Network for Anxiety Classification from EEG in Adolescents with Autism

Cited by 14 publications

References 41 publications

AI-Augmented Behavior Analysis for Children With Developmental Disabilities: Building Toward Precision Treatment

AI-Augmented Behavior Analysis for Children With Developmental Disabilities: Building Toward Precision Treatment

Multi-Modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for Mental Disorder Prediction

Sistemas Inteligentes para Desenvolvimento de Competências e Diagnóstico de pessoas com o Transtorno do Espectro do Autismo: Uma Revisão Sistemática da Literatura

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