EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Random Forest <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> BIB001 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Random Forest <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> BIB002
Random forest is an ensemble algorithm which builds multiple models and combines the results of each model to generate the overall result (Liaw and Wiener, 2002) . It creates a collection of decision trees from randomized subsets of the training data and during classification, results from each decision tree are combined and a result is generated. Building several models increase the accuracy of the result by reducing the effect of noise and other biases. However, many decision trees will slow down the algorithm. In BIB002 BIB001 random forest technique has been used to classify ASD. 