An 8 Channel Patient Specific Neuromorphic Processor for the Early Screening of Autistic Children through Emotion Detection

Aslam, Abdul Rehman; Altaf, Muhammad Awais Bin

doi:10.1109/iscas.2019.8702738

Cited by 22 publications

(7 citation statements)

References 20 publications

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“…This is the first study (to the best of our knowledge) that provides a framework and guideline for both emotions (EMT) and ASD classification. The significance of EEG-based emotions and ASD classification systems is highly dependent on the channel count, especially for wearable systems (Aslam and Altaf, 2019;Fang et al, 2019). The identification of a minimum (4) number of channels was a primary focus of this study.…”

Section: Discussionmentioning

confidence: 99%

Channels and Features Identification: A Review and a Machine-Learning Based Model With Large Scale Feature Extraction for Emotions and ASD Classification

et al. 2022

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Autism Spectrum Disorder (ASD) is characterized by impairments in social and cognitive skills, emotional disorders, anxiety, and depression. The prolonged conventional ASD diagnosis raises the sheer need for early meaningful intervention. Recently different works have proposed potential for ASD diagnosis and intervention through emotions prediction using deep neural networks (DNN) and machine learning algorithms. However, these systems lack an extensive large-scale feature extraction (LSFE) analysis through multiple benchmark data sets. LSFE analysis is required to identify and utilize the most relevant features and channels for emotion recognition and ASD prediction. Considering these challenges, for the first time, we have analyzed and evaluated an extensive feature set to select the optimal features using LSFE and feature selection algorithms (FSA). A set of up to eight most suitable channels was identified using different best-case FSA. The subject-wise importance of channels and features is also identified. The proposed method provides the best-case accuracies, precision, and recall of 95, 92, and 90%, respectively, for emotions prediction using a linear support vector machine (LSVM) classifier. It also provides the best-case accuracy, precision, and recall of 100% for ASD classification. This work utilized the largest number of benchmark data sets (5) and subjects (99) for validation reported till now in the literature. The LSVM classification algorithm proposed and utilized in this work has significantly lower complexity than the DNN, convolutional neural network (CNN), Naïve Bayes, and dynamic graph CNN used in recent ASD and emotion prediction systems.

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Section: Discussionmentioning

confidence: 99%

Channels and Features Identification: A Review and a Machine-Learning Based Model With Large Scale Feature Extraction for Emotions and ASD Classification

et al. 2022

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“…Promising medical therapies, including behavioral interventions and antipsychotic medications, have been implicated in the treatment of ASD over the past decades. However, drug resistance against ASD may occur frequently if traditional classifications based on gender and comorbidities are used to guide anti-ASD treatment ( Anderson et al, 2007 ; Ospina et al, 2008 ; Woolfenden et al, 2012 ; Aslam and Altaf, 2019 ; 2021 ). Therefore, accurate differentiation of ASD clusters at the molecular level will improve the understanding of the heterogeneity in ASD and is vital to guide the individualized treatment of ASD.…”

Section: Discussionmentioning

confidence: 99%

Identification of Ferroptosis-Related Molecular Clusters and Immune Characterization in Autism Spectrum Disorder

et al. 2022

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Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with clinical presentation and prognostic heterogeneity. Ferroptosis is a regulated non-apoptotic cell death program implicated in the occurrence and progression of various diseases. Therefore, we aimed to explore ferroptosis-related molecular subtypes in ASD and further illustrate the potential mechanism.Methods: A total of 201 normal samples and 293 ASD samples were obtained from the Gene Expression Omnibus (GEO) database. We used the unsupervised clustering analysis to identify the molecular subtypes based on ferroptosis-related genes (FRGs) and evaluate the immune characteristics between ferroptosis subtypes. Ferroptosis signatures were identified using the least absolute shrinkage and selection operator regression (LASSO) and recursive feature elimination for support vector machines (SVM-RFE) machine learning algorithms. The ferroptosis scores based on seven selected genes were constructed to evaluate the ferroptosis characteristics of ASD.Results: We identified 16 differentially expressed FRGs in ASD children compared with controls. Two distinct molecular clusters associated with ferroptosis were identified in ASD. Analysis of immune infiltration revealed immune heterogeneity between the two clusters. Cluster2, characterized by a higher immune score and a larger number of infiltrated immune cells, exhibited a stronger immune response and was markedly enriched in immune response-related signaling pathways. Additionally, the ferroptosis scores model was capable of predicting ASD subtypes and immunity. Higher levels of ferroptosis scores were associated with immune activation, as seen in Cluster2. Lower ferroptosis scores were accompanied by relative immune downregulation, as seen in Cluster1.Conclusion: Our study systematically elucidated the intricate correlation between ferroptosis and ASD and provided a promising ferroptosis score model to predict the molecular clusters and immune infiltration cell profiles of children with ASD.

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“…Comparison with the state-of the-arts The comparison of the work with previous ASD classification processors [6], [9] is shown in Table 1. Since no other hardware-based ASD classification processor exists, the results are also compared with similar systems for other biomedical applications [8], [12], [13]. The classification performance of the work is quite good (85.5%) being the 1 st hardware implementation and using the lowest number (4) of electrodes.…”

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confidence: 99%

“…These evaluations take ample time and may be avoided by many parents due to the Electroencephalogram (EEG) signals record the electrical activity inside the human brain using a certain number of electrodes. Despite the various challenges related to EEG signal acquisition including noise and artifacts, there is significant research to show the effectivity of scalp EEG for ASD diagnosis [6].There are some solutions to assist ASD children using their emotions [7]- [8]. But no hardware-based ASD prediction processor is available.…”

Section: Introductionmentioning

confidence: 99%

An 8.62 μ W Processor for Autism Spectrum Disorder Classification using Shallow Neural Network

Aslam

Hafeez

Heidari

et al. 2021

2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)

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Autism Spectrum Disorder (ASD) is the most prevalent child neurological and developmental disorder causing cognitive and behavioral impairments. The early diagnosis is an urgent need for the treatment and rehabilitation of ASD patients. This work presents electroencephalogram (EEG) based ASD classification processor implementation that targets a patch-form factor sensor for long time monitoring in a wearable device. A patient is classified as ASD or typically developing using scalp EEG. The selection of frontal and parietal lobe electrodes causes minimum uneasiness to the children. The proposed and implemented algorithm utilizes only four EEG electrodes. The processor is implemented and validated on Artix-7 FPGA, requiring only 26229 lookup tables and 15180 flip flops. The hardware efficient implementation of the complex kurtosis value and Katz fractal dimension features using kurtosis value indicator and Katz fractal dimension indicator with 54% and 38% efficient implementations, respectively, is provided. A hardware feasible shallow neural network architecture is used for the ASD classification. The implemented system classifies the ASD with a high classification accuracy of 85.5% using the power and latency of 8.62 µW and 2.25 milliseconds, respectively.feeling of disgrace and repeated visits to neurologists. Fig. 1 shows the difference between the conventional ADOS-2 diagnosis and the proposed solution to solve the pain problem. The proposed solution (longer-term) would be able to diagnose a child as ASD or TD earlier in the form of a wearable head-band system on chip (SoC) processor. The processor would pre-process the brain waves (EEG), extract suitable features, and classify the child as ASD or TD using suitable machine learning (ML) or deep neural network (DNN) classification method with limited electrodes. It would not only avoid the stigma associated with the prolonged diagnosis but also reduce the rehabilitation costs due to early intervention [4].

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An 8 Channel Patient Specific Neuromorphic Processor for the Early Screening of Autistic Children through Emotion Detection

Cited by 22 publications

References 20 publications

Channels and Features Identification: A Review and a Machine-Learning Based Model With Large Scale Feature Extraction for Emotions and ASD Classification

Channels and Features Identification: A Review and a Machine-Learning Based Model With Large Scale Feature Extraction for Emotions and ASD Classification

Identification of Ferroptosis-Related Molecular Clusters and Immune Characterization in Autism Spectrum Disorder

An 8.62 μ W Processor for Autism Spectrum Disorder Classification using Shallow Neural Network

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