LSTM-based Electroencephalogram Classification on Autism Spectrum Disorder

2021 ◽  
Vol 13 (6) ◽  
Author(s):  
N. A. Ali ◽  
◽  
A. R Syafeeza ◽  
A. S. Jaafar ◽  
S. Shamsuddin ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Technology System ◽  
Output State ◽  
Layered Architecture ◽  
Deep Learning Algorithm ◽  
Automated Technology

Autism Spectrum Disorder (ASD) is categorized as a neurodevelopmental disability. Having an automated technology system to classify the ASD trait would have a huge influence on paediatricians, which can aid them in diagnosing ASD in children using a quantifiable method. A novel autism diagnosis method based on a bidirectional long-short-term-memory (LSTM) network's deep learning algorithm is proposed. This multi-layered architecture merges two LSTM blocks with the other direction of propagation to classify the output state on the brain signal data from an electroencephalogram (EEG) on individuals; normal and autism obtained from the Simon Foundation Autism Research Initiative (SFARI) database. The accuracy of 99.6% obtained for 90:10 train:test data distribution, while the accuracy of 97.3% was achieved for 70:30 distribution. The result shows that the proposed approach had better autism classification with upgraded efficiency compared to single LSTM network method and potentially giving a significant contribution in neuroscience research.

scholarly journals Autism spectrum disorder classification on electroencephalogram signal using deep learning algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 91
Author(s):  
Nur Alisa Ali
Keyword(s):  
Autism Spectrum Disorder ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Research Work ◽  
Autism Spectrum ◽  
Performance Measure ◽  
Spectrum Disorder ◽  
Font Size ◽  
Deep Learning Algorithm

<span style="color: black; font-family: 'Times New Roman',serif; font-size: 9pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Autism Spectrum Disorder (ASD) is a neurodevelopmental that impact the social interaction and communication skills. Diagnosis of ASD is one of the difficult problems facing researchers. This research work aimed to reveal the different pattern between autistic and normal children via electroencephalogram (EEG) by using the deep learning algorithm. The brain signal database used pattern recognition where the extracted features will undergo the multilayer perceptron network for the classification process. The promising method to perform the classification is through a deep learning algorithm, which is currently a well-known and superior method in the pattern recognition field. The performance measure for the classification would be the accuracy. The higher percentage means the more effectiveness for the ASD diagnosis. </span><span style="color: black; font-family: 'Times New Roman',serif; font-size: 9pt; mso-fareast-font-family: 'Times New Roman+FPEF'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">This can be seen as the ground work for applying a new algorithm for further development diagnosis of autism to see how the treatment is working as well in future.</span>

scholarly journals Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 411
Author(s):  
Yunkai Zhang ◽  
Yinghong Tian ◽  
Pingyi Wu ◽  
Dongfan Chen
Keyword(s):  
Autism Spectrum Disorder ◽  
Action Recognition ◽  
Short Term Memory ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Short Term ◽  
Term Memory ◽  
Clinical Observations ◽  
The Neural Network ◽  
Long Short Term Memory

The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children’s actions. The performed experiments show that our proposed method is effective for ASD children’s action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.

scholarly journals Consecrate Recurrent Neural Network Classifier for Autism Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 2033-2041
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Sequence Data ◽  
Large Data ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Neural Network Classifier ◽  
Gene Sequences

Most recent discoveries in Autism Spectrum Disorder (ASD) detection and classification studies reveal that there is a substantial relationship between Autism disorders and gene sequences. This work is indented to classify the autism spectrum disorder groups and sub-groups based on the gene sequences. The gene sequences are large data and perplexed for handling with conventional data mining or classification procedures. The Consecrate Recurrent Neural Network Classifier for Autism Classification (CRNNC-AC) work is introduced in this work to classify autism disorders using gene sequence data. A dedicated Elman [1] type Recurrent Neural Network (RNN) is introduced along with a legacy Long Short-Term Memory (LSTM) [2] in this classifier. The LSTM model is contrived to achieve memory optimization to eliminate memory overflows without affecting the classification accuracy. The classification quality metrics [3] such as Accuracy, Sensitivity, Specificity and F1-Score are concerned for optimization. The processing time of the proposed method is also measured to evaluate the pertinency.

scholarly journals A Temporal Activity of CA1 Neurons Underlying Short-Term Memory for Social Recognition Altered in PTEN Mouse Models of Autism Spectrum Disorder

2021 ◽  
Vol 15 ◽  
Author(s):  
An-Ping Chai ◽  
Xue-Feng Chen ◽  
Xiao-Shan Xu ◽  
Na Zhang ◽  
Meng Li ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Short Term Memory ◽  
Autism Spectrum ◽  
Social Recognition ◽  
Neuronal Firing ◽  
Spectrum Disorder ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Ca1 Neurons

Memory-guided social recognition identifies someone from previous encounters or experiences, but the mechanisms of social memory remain unclear. Here, we find that a short-term memory from experiencing a stranger mouse lasting under 30 min interval is essential for subsequent social recognition in mice, but that interval prolonged to hours by replacing the stranger mouse with a familiar littermate. Optogenetic silencing of dorsal CA1 neuronal activity during trials or inter-trial intervals disrupted short-term memory-guided social recognition, without affecting the ability of being sociable or long-term memory-guided social recognition. Postnatal knockdown or knockout of autism spectrum disorder (ASD)-associated phosphatase and tensin homolog (PTEN) gene in dorsal hippocampal CA1 similarly impaired neuronal firing rate in vitro and altered firing pattern during social recognition. These PTEN mice showed deficits in social recognition with stranger mouse rather than littermate and exhibited impairment in T-maze spontaneous alternation task for testing short-term spatial memory. Thus, we suggest that a temporal activity of dorsal CA1 neurons may underlie formation of short-term memory to be critical for organizing subsequent social recognition but that is possibly disrupted in ASD.

scholarly journals Inner speech is used to mediate short-term memory, but not planning, among intellectually high-functioning adults with autism spectrum disorder

2012 ◽  
Vol 24 (1) ◽  
pp. 225-239 ◽  
Author(s):  
David M. Williams ◽  
Dermot M. Bowler ◽  
Christopher Jarrold
Keyword(s):  
Autism Spectrum Disorder ◽  
Articulatory Suppression ◽  
Short Term Memory ◽  
Recall Performance ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Inner Speech ◽  
Short Term ◽  
Order Problem ◽  
Term Memory

AbstractEvidence regarding the use of inner speech by individuals with autism spectrum disorder (ASD) is equivocal. To clarify this issue, the current study employed multiple techniques and tasks used across several previous studies. In Experiment 1, participants with and without ASD showed highly similar patterns and levels of serial recall for visually presented stimuli. Both groups were significantly affected by the phonological similarity of items to be recalled, indicating that visual material was spontaneously recoded into a verbal form. Confirming that short-term memory is typically verbally mediated among the majority of people with ASD, recall performance among both groups declined substantially when inner speech use was prevented by the imposition of articulatory suppression during the presentation of stimuli. In Experiment 2, planning performance on a tower of London task was substantially detrimentally affected by articulatory suppression among comparison participants, but not among participants with ASD. This suggests that planning is not verbally mediated in ASD. It is important that the extent to which articulatory suppression affected planning among participants with ASD was uniquely associated with the degree of their observed and self-reported communication impairments. This confirms a link between interpersonal communication with others and intrapersonal communication with self as a means of higher order problem solving.

scholarly journals Short-term memory in autism spectrum disorder.

2011 ◽  
Vol 120 (1) ◽  
pp. 247-252 ◽  
Author(s):  
Marie Poirier ◽  
Jonathan S. Martin ◽  
Sebastian B. Gaigg ◽  
Dermot M. Bowler
Keyword(s):  
Autism Spectrum Disorder ◽  
Short Term Memory ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Short Term ◽  
Term Memory

scholarly journals Narrative skill in boys with fragile X syndrome with and without autism spectrum disorder

2011 ◽  
Vol 32 (2) ◽  
pp. 359-388 ◽  
Author(s):  
BRUNO ESTIGARRIBIA ◽  
GARY E. MARTIN ◽  
JOANNE E. ROBERTS ◽  
AMY SPENCER ◽  
AGNIESZKA GUCWA ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Intellectual Disability ◽  
Fragile X Syndrome ◽  
Short Term Memory ◽  
Fragile X ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Story Grammar ◽  
Caregiver Education ◽  
Almost All

ABSTRACTWe examined recalled narratives of boys with fragile X syndrome with autism spectrum disorder (FXS-ASD; N = 28) and without ASD (FXS-O; N = 29), and compared them to those of boys with Down syndrome (N = 33) and typically developing (TD) boys (N = 39). Narratives were scored for mentions of macrostructural story grammar elements (introduction, relationship, initiating events, internal response, attempts/actions, and ending). We found that narrative recall is predicted by short-term memory and nonverbal mental age levels in almost all groups (except TD), but not by expressive syntax or caregiver education. After adjusting for these covariates, there were no differences between the three groups with intellectual disability. The FXS-ASD group, however, had significantly poorer performance than the TD group on the overall story grammar score, and both the FXS-O and FXS-ASD groups had lower attempts/actions scores than the TD group. We conclude that some form of narrative impairment may be associated with FXS, that this impairment may be shared by other forms of intellectual disability, and that the presence of ASD has a significantly detrimental effect on narrative recall.

scholarly journals AppPECS: Mobile Application for Children with Autism Spectrum Disorder

2019 ◽  
pp. 27-38
Author(s):  
Carlos Enríquez-Ramírez ◽  
Juan Carlos Cruz-Reséndiz ◽  
Miriam Olvera-Cueyar ◽  
Roberto Arturo Sánchez-Herrera
Keyword(s):  
Autism Spectrum Disorder ◽  
Mobile Applications ◽  
Mobile Application ◽  
Short Term Memory ◽  
Educational Games ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Short Term ◽  
Learning Development

The study of treatments for children with autism and interventions through educational games is growing because researchers have seen an acceptance by users with autism spectrum disorder in this type of applications. Allowing this type of users to acquire and develop new skills such as digital, the development of writing through the use of the keyboard, as a means of communication and a mechanism of reinforcement in sociable aspects. Taking into account the benefits of using games through mobile applications in the treatment of targeted therapies in children with autism spectrum disorder, a mobile application has been developed to obtain an experience that interactively stimulates children for the purpose of Reinforce areas of learning development, such as repetition of activities (socialization), concentration, reinforcement of short-term memory, order and development of kinesthetic skills through the use of digitization. This project was applied in the Unidad de Servicios de Apoyo a la Escuela Regular No. 21 (USAER) instance of Special Education, dependent on the Secretaría de Educación Pública de Hidalgo.

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