EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

Djemal, Ridha; AlSharabi, Khalil; Ibrahim, Sutrisno; Alsuwailem, Abdullah

doi:10.1155/2017/9816591

Cited by 91 publications

(63 citation statements)

References 20 publications

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“…Next, the robustness of our DANN was further tested using varying k-fold cross validation. A classification model that is not robust may appear to perform very differently with different k. Figure 4 shows plots of the accuracy, sensitivity, precision, F-score, and specificity of the proposed DANN over k-fold cross validation strategies (k � [6,7,8,9,10]). Using one-way ANOVA, the proposed DANN exhibited no significantly different performance across varying k-fold experiments (p � 0.082), indicating the robustness of the proposed multichannel DANN model.…”

Section: Robustness Of Multichannel Dann On Varying Data Splitmentioning

confidence: 99%

“…Patients with ASD exhibit different levels of impairments, ranging from above average to intellectual disability. In neuroscience, ASD remains a formidable challenge, due to their high prevalence, complexity, and substantial heterogeneity, which require multidisciplinary efforts [6][7][8]. Although clinical therapies have been developed to treat the symptoms, the diagnosis of ASD remains to be a challenging task.…”

Section: Introductionmentioning

confidence: 99%

“…Comparison of random forest (RF), support vector machine (SVM), multichannel deep neural network (DNN), and multichannel deep attention neural network (DANN) classifiers trained using 10-fold cross validation on the entire dataset. Performance of multichannel DANN over varying data split schemes with k-fold cross validation strategies (k �[6,7,8,9,10]). Mean and standard deviation are displayed.…”

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confidence: 99%

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Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

et al. 2020

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Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.

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Section: Robustness Of Multichannel Dann On Varying Data Splitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

et al. 2020

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“…Several studies report second-order blind identification (SOBI), an ICA algorithm, as a successful technique to remove all types of artefacts from the EEG signal (Urigüen and Garcia-Zapirain, 2015). ICA has been used as a pre-processing technique for ASD classification in (Djemal et al, 2017). It has also been used in (Abdulhay et al, 2017) as a pre-processing step to detect abnormal EEG activities and neural connectivity in autistic individuals.…”

Section: Independent Component Analysismentioning

confidence: 99%

“…Signal decomposition using DWT is shown in Fig. 2 (Bosl et al, 2018) O X ASD classification using EEG and eye movement (Thapaliya et al, 2018) X X Classifying ASD using MS-ROM/I-FAST algorithm (Grossi et al, 2017) X ASD diagnosis using DWT, Shannon entropy and ANN (Djemal et al, 2017) O X X X Wavelet-based ASD classification (Cheong et al, 2015) O X X ASD diagnosis utilizing brain connectivity (Jamal et al, 2014) X X O Fuzzy synchronization likelihood methodology for ASD diagnosis (Ahmadlou et al, 2012a) O ASD diagnosis based on improved visibility graph fractality (Ahmadlou et al, 2012b) O X EEG as a biomarker for distinguishing ASD children (Bosl et al, 2011) X Classification of ASD using fractal dimensions (Ahmadlou et al, 2010) O X Frequency 3D mapping and interchannel stability of EEG as indicators towards ASD diagnosis (Abdulhay et al, 2017) X X O O Diagnosing ASD utilizing EEG spectral coherence (Duffy and Als, 2012) X X X X ASDGenus: channel optimised classification using EEG (Haputhanthri et al, 2019) X O X…”

Section: Wavelet-based Analysismentioning

confidence: 99%

EEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review

Brihadiswaran¹,

Haputhanthri²,

Gunathilaka³

et al. 2019

Journal of Computer Science

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Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical tests to diagnose ASD. Over the years, several objective measures utilizing abnormalities found in EEG signals and statistical analysis have been proposed. Machine learning based approaches provide more flexibility and have produced better results in ASD classification. This paper presents a survey of major EEG-based ASD classification approaches from 2010 to 2018, which adopt machine learning. The methodology is divided into four phases: EEG data collection, pre-processing, feature extraction and classification. This study explores different techniques and tools used for pre-processing, feature extraction and feature selection techniques, classification models and measures for evaluating the model. We analyze the strengths and weaknesses of the techniques and tools. Further, this study summarizes the ASD classification approaches and discusses the existing challenges, limitations and future directions.

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Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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ImportanceNeuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.ObjectiveTo systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.Evidence ReviewPubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.FindingsA total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).Conclusions and RelevanceThis systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

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EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

Cited by 91 publications

References 20 publications

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

EEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

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