EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Logistic Regression <s> Logistic regression and artificial neural networks are the models of choice in many medical data classification tasks. In this review, we summarize the differences and similarities of these models from a technical point of view, and compare them with other machine learning algorithms. We provide considerations useful for critically assessing the quality of the models and the results based on these models. Finally, we summarize our findings on how quality criteria for logistic regression and artificial neural network models are met in a sample of papers from the medical literature. <s> BIB001 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Logistic Regression <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> BIB002 </s> EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review <s> Logistic Regression <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> BIB003
Logistic regression has been used in the field of statistics starting from the 19th century. In machine learning, logistic regression is a popular algorithm for binary classification problems, similar to classifying ASD and no-ASD BIB001 . When the model is trained, values for the weights and bias are learned. The core function used is a sigmoid function. The output value will be in the range of 0 to 1. By setting a threshold value T 0 , output values above T 0 are classified to be class and output values below T 0 are classified to be the other class. In this context, the two classes are ASD and no-ASD. Logistic regression is simple, easy to implement and does not require extreme computational power. Authors of BIB003 BIB002 have used logistic regression to diagnose ASD.