scholarly journals Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder

2021 ◽  
Vol 11 (4) ◽  
pp. 409
Author(s):  
Lina Abou-Abbas ◽  
Stefon van Noordt ◽  
James A. Desjardins ◽  
Mike Cichonski ◽  
Mayada Elsabbagh
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
High Risk ◽  
Empirical Mode Decomposition ◽  
Familial Risk ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Mode Decomposition

Event-related potentials (ERPs) activated by faces and gaze processing are found in individuals with autism spectrum disorder (ASD) in the early stages of their development and may serve as a putative biomarker to supplement behavioral diagnosis. We present a novel approach to the classification of visual ERPs collected from 6-month-old infants using intrinsic mode functions (IMFs) derived from empirical mode decomposition (EMD). Selected features were used as inputs to two machine learning methods (support vector machines and k-nearest neighbors (k-NN)) using nested cross validation. Different runs were executed for the modelling and classification of the participants in the control and high-risk (HR) groups and the classification of diagnosis outcome within the high-risk group: HR-ASD and HR-noASD. The highest accuracy in the classification of familial risk was 88.44%, achieved using a support vector machine (SVM). A maximum accuracy of 74.00% for classifying infants at risk who go on to develop ASD vs. those who do not was achieved through k-NN. IMF-based extracted features were highly effective in classifying infants by risk status, but less effective by diagnostic outcome. Advanced signal analysis of ERPs integrated with machine learning may be considered a first step toward the development of an early biomarker for ASD.

scholarly journals Development of attention from birth to 5 months in infants at risk for autism spectrum disorder

2019 ◽  
Vol 32 (2) ◽  
pp. 491-501 ◽  
Author(s):  
Jessica Bradshaw ◽  
Ami Klin ◽  
Lindsey Evans ◽  
Cheryl Klaiman ◽  
Celine Saulnier ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
At Risk ◽  
High Risk ◽  
Social Interactions ◽  
Social Communication ◽  
Cognitive Skills ◽  
Familial Risk ◽  
Autism Spectrum ◽  
Low Risk ◽  
Spectrum Disorder

AbstractSocial-communication skills emerge within the context of rich social interactions, facilitated by an infant's capacity to attend to people and objects in the environment. Disruption in this early neurobehavioral process may decrease the frequency and quality of social interactions and learning opportunities, potentially leading to downstream deleterious effects on social development. This study examined early attention in infant siblings of children with autism spectrum disorder (ASD) who are at risk for social and communication delays. Visual and auditory attention was mapped from age 1 week to 5 months in infants at familial risk for ASD (high risk; N = 41) and low-risk typically developing infants (low risk; N = 39). At 12 months, a subset of participants (N = 40) was administered assessments of social communication and nonverbal cognitive skills. Results revealed that high-risk infants performed lower on attention tasks at 2 and 3 months of age compared to low-risk infants. A significant association between overall attention at 3 months and developmental outcome at 12 months was observed for both groups. These results provide evidence for early vulnerabilities in visual attention for infants at risk for ASD during a period of important neurodevelopmental transition (between 2 and 3 months) when attention has significant implications for social communication and cognitive development.

scholarly journals Newly Proposed Technique for Autism Spectrum Disorder based Machine Learning

2021 ◽  
Vol 13 (2) ◽  
pp. 1-15
Author(s):  
Sherif Kamel ◽  
Rehab Al-harbi
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Screening Methods ◽  
Disease Prediction ◽  
Healthcare Organizations ◽  
Machine Learning Applications ◽  
Research Stage

The rapid growth in the number of autism disorder among toddlers needs for the development of easily implemented and effective screening methods. In this current era, the causes of Autism Spectrum Disorder (ASD) do not know yet, however, the diagnosis and detection of ASD is based on behaviours and symptoms. This paper aims to improve ASD disease prediction accuracy among toddlers by using the Logistic Regression model of Machine Learning, through the collected health care dataset and by using an algorithm for rapid classification of the behaviours to check whether the children are having autism diseases or not according to information in the dataset. Therefore, Machine Learning decreasing the time needed to detect the disorder, then providing the necessary health services early for infected toddlers to enhance their lifestyle. In healthcare, most machine learning applications are in the research stage, and to take the advantage of emerging software tools that incorporate artificial intelligence, healthcare organizations first need to overcome a variety of challenges.

scholarly journals A Machine Learning Way to Classify Autism Spectrum Disorder

2021 ◽  
Vol 16 (06) ◽  
pp. 182
Author(s):  
Sujatha R ◽  
Aarthy SL ◽  
Jyotir Moy Chatterjee ◽  
A. Alaboudi ◽  
NZ Jhanjhi
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Stochastic Gradient Descent ◽  
Screening Tests ◽  
Support Vector ◽  
Attention Switching ◽  
Adaptive Boosting ◽  
Over The Top

In recent times Autism Spectrum Disorder (ASD) is picking up its force quicker than at any other time. Distinguishing autism characteristics through screening tests is over the top expensive and tedious. Screening of the same is a challenging task, and classification must be conducted with great care. Machine Learning (ML) can perform great in the classification of this problem. Most researchers have utilized the ML strategy to characterize patients and typical controls, among which support vector machines (SVM) are broadly utilized. Even though several studies have been done utilizing various methods, these investigations didn't give any complete decision about anticipating autism qualities regarding distinctive age groups. Accordingly, this paper plans to locate the best technique for ASD classi-fication out of SVM, K-nearest neighbor (KNN), Random Forest (RF), Naïve Bayes (NB), Stochastic gradient descent (SGD), Adaptive boosting (AdaBoost), and CN2 Rule Induction using 4 ASD datasets taken from UCI ML repository. The classification accuracy (CA) we acquired after experimentation is as follows: in the case of the adult dataset SGD gives 99.7%, in the adolescent dataset RF gives 97.2%, in the child dataset SGD gives 99.6%, in the toddler dataset Ada-Boost gives 99.8%. Autism spectrum quotients (AQs) varied among several sce-narios for toddlers, adults, adolescents, and children that include positive predic-tive value for the scaling purpose. AQ questions referred to topics about attention to detail, attention switching, communication, imagination, and social skills.

scholarly journals Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

2016 ◽  
Author(s):  
Elaheh Moradi ◽  
Budhachandra Khundrakpam ◽  
John D. Lewis ◽  
Alan C. Evans ◽  
Jussi Tohka
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Cortical Thickness ◽  
Symptom Severity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Imaging Data ◽  
Learning Stage ◽  
Severity Prediction

AbstractMachine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multi-protocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a whole-brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.

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