EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> This paper presents a statistical method for removing ocular artifacts in the electroencephalogram (EEG) records. Artifacts in EEG signals are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). The removal of ocular artifact from scalp EEGs is of considerable importance for both the automated and visual analysis of underlying brainwave activity. These noise sources increase the difficulty in analyzing the EEG and obtaining clinical information. For this reason, it is necessary to design a procedure to decrease such artifacts in EEG records. In this paper, a statistical method for removing ocular artifacts from EEG recordings through wavelet transform without using an EOG reference channel is proposed. <s> BIB001 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> Summary:A method is presented for investigation of EEG of children with autistic spectrum disorder using complexity and chaos theory with the goal of discovering a nonlinear feature space. Fractal Dimension is proposed for investigation of complexity and dynamical changes in autistic spectrum disord <s> BIB002 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> BackgroundComplex neurodevelopmental disorders may be characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. Such endophenotypes may be measurable biomarkers for later cognitive impairments. The nonlinear complexity of electroencephalography (EEG) signals is believed to contain information about the architecture of the neural networks in the brain on many scales. Early detection of abnormalities in EEG signals may be an early biomarker for developmental cognitive disorders. The goal of this paper is to demonstrate that the modified multiscale entropy (mMSE) computed on the basis of resting state EEG data can be used as a biomarker of normal brain development and distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD), defined on the basis of an older sibling with ASD.MethodsUsing mMSE as a feature vector, a multiclass support vector machine algorithm was used to classify typically developing and high-risk groups. Classification was computed separately within each age group from 6 to 24 months.ResultsMultiscale entropy appears to go through a different developmental trajectory in infants at high risk for autism (HRA) than it does in typically developing controls. Differences appear to be greatest at ages 9 to 12 months. Using several machine learning algorithms with mMSE as a feature vector, infants were classified with over 80% accuracy into control and HRA groups at age 9 months. Classification accuracy for boys was close to 100% at age 9 months and remains high (70% to 90%) at ages 12 and 18 months. For girls, classification accuracy was highest at age 6 months, but declines thereafter.ConclusionsThis proof-of-principle study suggests that mMSE computed from resting state EEG signals may be a useful biomarker for early detection of risk for ASD and abnormalities in cognitive development in infants. To our knowledge, this is the first demonstration of an information theoretic analysis of EEG data for biomarkers in infants at risk for a complex neurodevelopmental disorder. <s> BIB003 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> Abstract This paper presents a methodology for investigation of functional connectivity in patients with autism spectrum disorder (ASD) using Fuzzy Synchronization Likelihood (Fuzzy SL). Fuzzy SLs between and within brain regions are calculated in all EEG sub-bands produced by the wavelet decomposition as well as in the full-band EEG. Then, discriminative Fuzzy SLs between and within different regions and different EEG sub-bands or full-band EEG for distinguishing autistic children from healthy control children are determined based on Analysis of Variation (ANOVA). Finally, the selected features are used as input to an Enhanced Probabilistic Neural Network classifier to make an accurate diagnosis of ASD based on the detected differences in the regional functional connectivity of autistic and healthy EEGs. The methodology is validated using EEG data obtained from 9 autistic and 9 healthy children. The ANOVA test showed high ability of the regional Fuzzy SLs in low frequency bands, delta and theta, as well as alpha band for discriminating the two groups. A high classification accuracy of 95.5% was achieved for distinguishing autistic EEGs from healthy EEGs. It is concluded that the methodology presented in this paper can be used as an effective tool for diagnosis of the autism. Further, the regional Fuzzy SLs discovered in this research can be used as reliable markers in neurofeedback treatments to improve neuronal plasticity and connectivity in autistic patients. <s> BIB004 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact. Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls. Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P < 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz). Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks. <s> BIB005 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice. <s> BIB006 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> Feature extraction is a process to extract information from the electroencephalogram (EEG) signal to represent the large dataset before performing classification. This paper is intended to study the use of discrete wavelet transform (DWT) in extracting feature from EEG signal obtained by sensory response from autism children. In this study, DWT is used to decompose a filtered EEG signal into its frequency components and the statistical feature of the DWT coefficient are computed in time domain. The features are used to train a multilayer perceptron (MLP) neural network to classify the signals into three classes of autism severity (mild, moderate and severe). The training results in classification accuracy achieved up to 92.3% with MSE of 0.0362. Testing on the trained neural network shows that all samples used for testing is being classified correctlyARPN Journal of Engineering and Applied Sciences <s> BIB007 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> Autism is a devastating disease affecting 12% of newborns. Its incidence is steadily increasing.A growing body of evidence points out that to effectively treat autism, the earliest possible detection is directly proportional to successful therapy. This clearly implies that the availability of an accurate and relatively inexpensive diagnostic method for early diagnosis should be one of the medical community's highest priorities.The relevant involvement of the cerebral cortex in substantially altering cortical circuitry explains the unique pattern of deficits and strengths that characterize cognitive functioning. On the other, this makes the potential usefulness of EEG recording plausible as a biomarker of these abnormalities.Despite this plausibility, very few studies have attempted to use EEG recording in diagnosing autism.Traditional EEG measures apply frequency domain analysis, assume that EEG is stationary and employ linear feature extraction. Novel approaches based on artificial adaptive systems like those employed by us, apply data driven time& frequency domain analysis, assume that EEG is not stationary and use nonlinear feature extraction.To our knowledge, this is the first study that applies an artificial adaptive system to extract interesting features in computerized EEG that distinguishes ASD children from typically developing ones.The results are extremely interesting and open new avenues for confirmatory clinical studies in this important field. Background. Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people. The presence of deviant patterns in simple resting state EEG recordings in autism, consistent with the atypical organization of the cerebral cortex present, prompted us in applying this potent analytical systems in search of a EEG signature of the disease.Aim of the study. The aim of the study is to assess how effectively this methodology distinguishes subjects with autism from typically developing ones.Methods. Fifteen definite ASD subjects (13 males; 2 females; age range 714; mean value =10.4) and ten typically developing subjects (4 males; 6 females; age range 712; mean value 9.2) were included in the study. Patients received Autism diagnoses according to DSM-V criteria, subsequently confirmed by the ADOS scale. A segment of artefact-free EEG lasting 60 seconds was used to compute input values for subsequent analyses. MS-ROM/I-FAST coupled with a well-documented evolutionary system able to select predictive features (TWIST) created an invariant features vector input of EEG on which supervised machine learning systems acted as blind classifiers.Results. The overall predictive capability of machine learning system in sorting out autistic cases from normal control amounted consistently to 100% with all kind of systems employed using training-testing protocol and to 84% - 92.8% using Leave One Out protocol. The similarities among the ANN weight matrixes measured with apposite algorithms were not affected by the age of the subjects. This suggests that the ANNs do not read age-related EEG patterns, but rather invariant features related to the brain's underlying disconnection signature.Conclusion. This pilot study seems to open up new avenues for the development of non-invasive diagnostic testing for the early detection of ASD. <s> BIB008 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> Autism spectrum is one of the serious disorders influencing mental capacities and hence behavior. The present work investigates the potential of detecting irregularities of EEG activity and connectivity in Autism Spectrum by studying the inter-channel stability of EEG components point-by-point pulsation as well as by frequency 3D mapping. First, Empirical Mode Decomposition is used to find the Intrinsic Mode Functions of every EEG channel. Second, the Phase derivatives of analytic intrinsic modes are then calculated. Third, each phase derivative function obtained for an intrinsic mode is plotted versus the phase derivative function of the counterpart intrinsic mode in another channel. Finally, the stability loops found out are then analyzed. Furthermore, 3D mapping of EEG frequency bands (Delta, Theta, Alpha and Beta) is conducted and examined. <s> BIB009 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements. <s> BIB010 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> Autism Spectrum Disorder is a developmental disorder that often impairs a child’s normal development of the brain. Early Diagnosis is crucial in the long term treatment of ASD, but this is challenging due to the lack of a proper objective measures. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. This paper presents EEG and Eye movement data for the diagnosis of ASD using machine learning algorithms. There are number of studies on classification of ASD using EEG or Eye tracking data. However, all of them simply use either Eye movements or EEG data for the classification. In our study we combine Eye movements and EEG data to develop an efficient methodology for diagnosis. This paper presents several models based on EEG, and eye movements for the diagnosis of ASD. <s> BIB011 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Wavelet-based Analysis <s> Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which affects a person’s cognition and behaviour. It is a lifelong condition which cannot be cured completely using any intervention to date. However, early diagnosis and follow-up treatments have a major impact on autistic people. Unfortunately, the current diagnostic practices, which are subjective and behaviour dependent, delay the diagnosis at an early age and makes it harder to distinguish autism from other developmental disorders. Several works of literature explore the possible behaviour-independent measures to diagnose ASD. Abnormalities in EEG can be used as reliable biomarkers to diagnose ASD. This work presents a low-cost and straightforward diagnostic approach to classify ASD based on EEG signal processing and learning models. Possibilities to use a minimum number of EEG channels have been explored. Statistical features are extracted from noise filtered EEG data before and after Discrete Wavelet Transform. Relevant features and EEG channels were selected using correlation-based feature selection. Several learning models and feature vectors have been studied and possibilities to use the minimum number of EEG channels have also been explored. Using Random Forest and Correlation-based Feature Selection, an accuracy level of 93% was obtained. <s> BIB012
Wavelet is a rapidly decaying oscillation with a zeromean value. There are two types of wavelet transforms, continuous wavelet transform (CWT) and discrete wavelet transform (DWT). DWT has been frequently used for denoising signals. Denoising using DWT is a three-step process: (1) decompose, (2) discard and (3) reconstruct. Initially, the signal is filtered using a low pass and a high pass filter and the outputs are called approximation coefficients and detail coefficients, respectively. Signal decomposition using DWT is shown in Fig. 2 BIB010 O X ASD classification using EEG and eye movement BIB011 X X Classifying ASD using MS-ROM/I-FAST algorithm BIB008 X ASD diagnosis using DWT, Shannon entropy and ANN  O X X X Wavelet-based ASD classification BIB007 O X X ASD diagnosis utilizing brain connectivity BIB006 X X O Fuzzy synchronization likelihood methodology for ASD diagnosis BIB004 O ASD diagnosis based on improved visibility graph fractality  O X EEG as a biomarker for distinguishing ASD children BIB003 X Classification of ASD using fractal dimensions BIB002 O X Frequency 3D mapping and interchannel stability of EEG as indicators towards ASD diagnosis BIB009 X X O O Diagnosing ASD utilizing EEG spectral coherence BIB005 X X X X ASDGenus: channel optimised classification using EEG BIB012 X O X The high-frequency band (detail coefficients) contains most of the noise and useful information as well. The useful information needs to be preserved while removing the noise. A threshold value is chosen and the coefficients with magnitudes less than the threshold value are discarded. The signal is then reconstructed based on the new coefficients (inverse DWT). The low pass subband is decomposed further at multiple levels for further analysis. Table 1 states the five frequency bands and noise separated using DWT, as the initial step of noise removal. In BIB001 and  , techniques based on wavelet transformation to denoise ASD using EEG signals have been proposed. Daubechies wavelet was used in BIB010  and Coifman wavelet was used in BIB004 , to perform DWT. CWT was used in BIB006 . However, in these studies, wavelets were used for signal decomposition instead of noise removal.