Children suffering from Autism Spectrum Disorder (ASD) have impaired social communication, interaction and restricted and repetitive behaviors. ASD is caused by abnormal brain developments which give rise to the behavioral characteristics associated with ASD. The clinical diagnosis of ASD is performed on the basis of behavioral assessment and it causes a time delay in early intervention, as there is a time gap between abnormal brain developments and associated behavioral characteristics. Electroencephalography (EEG) is a technique which measures the electrical activity produced by the brain and it has been used to detect several neurological disorders. Studies have shown that there is a variation in the EEG signals of a normal subject and EEG signals of ASD subjects. In this study, we obtained scalograms of EEG signals by using Continuous Wavelet Transform (CWT). Pre-trained deep Convolutional Neural Networks (CNNs) such as GoogLeNet, AlexNet, MobileNet and SqueezeNet were used for extracting the features from scalograms and classification of obtained scalograms from EEG signals of normal and ASD subjects. We also used Support Vector Machine (SVM) algorithm and Relevance Vector Machine (RVM) for classification of the features extracted by the deep CNNs. The GoogLeNet, AlexNet, MobileNet and SqueezeNet deep CNNs achieved a validation accuracy of 75%, 75.84%, 79.45% and 82.98% in classifying the scalograms generated from EEG signals. The SVM achieved an accuracy of 71.6%, 74.76%, 70.70% and 81.47% using GoogleNet, Mobilenet, AlexNet and SqueezeNet for scalogram feature extraction. The RVM achieved an accuracy of 65.5%, 69.9%, 65.3% and 72.59% when used for classification using the features generated from GoogLeNet, AlexNet, MobileNet and SqueezeNet.The SqueezeNet deep CNN performed better than GoogLeNet, AlexNet and MobileNet for classification of the EEG scalograms. The feature extraction using SqueezeNet also resulted in better classification accuracy obtained by SVM and RVM. The results indicate that pre-trained models can be used for classifying the ASD using scalograms of the EEG signals.
Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, impacts the subject’s social communication and interaction and the subjects exhibit restricted and repetitive behaviors. Subjects with ASD may need assistance throughout their life, depending on the severity. Early diagnosis of ASD is therefore critical for early intervention. ASD is diagnosed clinically based on behavioral assessments of the subjects, which results in delayed diagnosis, since the typical ASD traits due to aberrant brain development take time to develop. Neurological disorders associated with aberrant brain electrical activity have been detected by analyzing Electroencephalogram (EEG) signal patterns. In this study, we used features extracted from EEG brain waves to categorize ASD and normal subjects using Machine Learning (ML) classifiers. Autoregressive (AR) coefficients, Shannon entropy, Multifractal wavelet leader estimates, Multiscale wavelet variance and Discrete Fourier Transform (DFT) coefficients were extracted from EEG brain waves of ASD and normal subjects. Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k-Nearest Neighbor (k-NN) and Feed-forward Neural Network (FNN) were utilized as classification algorithms to categorize the ASD subjects and the control subjects. An accuracy of 90% was achieved by k-NN algorithm using AR features, Shannon entropy, Multifractal wavelet leader estimates and Multiscale wavelet variance estimates in ASD categorization. An accuracy of 93% was achieved by k-NN using the DFT features. The findings of this study indicate that features extracted from EEG are sufficient enough for categorization of ASD subjects and the control subjects.
Autism Spectrum Disorder (ASD), a neurological abnormality that influences how an individual perceives and interacts with others, which leads to issues with social interaction and communication. In accordance with the Centers for Disease Control and Prevention, 1 in every 44 children in USA is affected by ASD. The identification of ASD is based on behavioural characteristics and it generally takes a long time from the initial observation of behavioural signs to the final diagnosis, due to the complexity and diversity of ASD symptoms. The application of Electroencephalography (EEG) signals, recorded from 14 ASD affected children and 14 healthy controls, as a potential biomarker for ASD categorisation, was analysed in this study. After pre-processing, second-order Wavelet Scattering Transform (WST) coefficients were extracted from the EEG signals and Deep Learning (DL) based ASD detection networks (WST-ASDNets) were used for categorisation of ASD and control subjects. Long Short Term Memory Network (LSTM) based WST-ASDNet and Convolution Neural Network (CNN) based WST-ASDNet achieved accuracy of 94% and 92% respectively, in ASD subject identification. The results demonstrate that the proposed WST-ASDNets can efficiently classify ASD and the usage of WST coefficients extracted from EEG signals can be used as potential biomarker for ASD categorisation.
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