A novel automated autism spectrum disorder detection system

Abstract

Autism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review

“…Deep learning is a subfield of machine learning in which large data is used to train these models, which can also predict outcomes with high accuracies. Both models are commonly used in the diagnosis of some neurological disorders, such as autism [41,42], ADHD [43,44] and depression [45][46][47], with high accuracies. The models are either fed with images obtained from computerised tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans or electroencephalogram (EEG) signals for the diagnosis of neurological disorders.…”

Feedback Artificial Tree‐Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder

“…It failed to report the unusual neural structures in the cerebral regions of ASD. Oh et al (2021) devised the support vector machine (SVM) for performing the ASD classification. In this method, three effective and dynamic features included to design the index of autism, when the ordered set of feature was considered as an input for further classification.…”

“…ASD is identified based on the shortfall of social behaviour and nonverbal communications, like evading eye interaction or facing problems with handling emotions (Anitha & Abinaya, 2019; Darekar & Dhande, 2019) and recognizing the emotions of others in the premature stage of childhood. Nonspecific indications, like irregular sensory perception abilities as well as experiences, in expert motorized skills with insomnia are common problem in certain children with ASD (Jagadeesan et al, 2021; Ke et al, 2020; Oh et al, 2021).…”

Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features

“…These healthcare solutions may include elderly care [9], remote healthcare [10], fitness programs [11], detection and prognosis of neurological and mental disorders like Alzheimer, epilepsy, autism spectrum disorder and schizophrenia, etc. [12][13][14][15][16][17]. Deep learning [18] is another paradigm in this regard, which is capable of handling the large volume of signal data generated by wearable IoT sensing devices like EEG headsets for epilepsy [19].…”