Related Papers
Scientific reports
EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach
2018 •
Helen Tager-Flusberg
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,...
Neuropsychiatric Electrophysiology
Nonlinear EEG biomarker profiles for autism and absence epilepsy
William Bosl
Biomedical Science, Engineering and Technology
Neurotechnology and Psychiatric Biomarkers
William Bosl
Frontiers in Psychiatry
How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review
William Bosl
Heliyon
Complexity analysis of the brain activity in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) due to cognitive loads/demands induced by Aristotle's type of syllogism/reasoning. A Power Spectral Density and multiscale entropy (MSE) analysis
2021 •
Artemios Pehlivanidis
Scientific Reports
Nonlinear Analysis of Visually Normal EEGs to Differentiate Benign Childhood Epilepsy with Centrotemporal Spikes (BECTS)
William Bosl
Childhood epilepsy with centrotemporal spikes, previously known as Benign Epilepsy with Centro-temporal Spikes (BECTS) or Rolandic Epilepsy, is one of the most common forms of focal childhood epilepsy. Despite its prevalence, BECTS is often misdiagnosed or missed entirely. This is in part due to the nocturnal and brief nature of the seizures, making it difficult to identify during a routine electroencephalogram (EEG). Detecting brain activity that is highly associated with BECTS on a brief, awake EEG has the potential to improve diagnostic screening for BECTS and predict clinical outcomes. For this study, 31 patients with BECTS were retrospectively selected from the BCH Epilepsy Center database along with a contrast group of 31 patients in the database who had no form of epilepsy and a normal EEG based on a clinical chart review. Nonlinear features, including multiscale entropy and recurrence quantitative analysis, were computed from 30-second segments of awake EEG signals. Differen...
Frontiers in Psychiatry
A biomarker discovery framework for childhood anxiety
William Bosl
IntroductionAnxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach to extracting nonlinear features of the electroencephalogram (EEG), with the goal of discovering differences in brain electrodynamics that distinguish children with anxiety disorders from healthy children. Additionally, we examined whether this approach could distinguish children with externalizing disorders from healthy children and children with anxiety.MethodsWe used a novel supervised tensor factorization method to extract latent factors from repeated multifrequency nonlinear EEG measures in a longitudinal sample of children assessed in infancy and at ages 3, 5, and 7 year...
Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study
massimo buscema, enzo grossi
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 7–14; mean value = 10.4) and ten typically developing subjects (4 males; 6 females; age range 7–12; 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 ma-trixes 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.
EEG-Derived Neurophenotypes
2016 •
William Bosl
In this chapter, scalp electrophysiological measurements using electroencephalographs or EEGs are examined in light of new developments in complex systems theory. At the most fundamental level, brain function is electrical. The neural network that comprises the brain and peripheral nervous system, along with all the specialized cellular structures for propagating electrical impulses, is designed to support exquisitely fine control over the electrical patterns that determine all thought and behavior. It is not an exaggeration to say that the most fundamental medium of the mind is an electric field. Measurements of brain electrical activity may thus in principle contain information about cognitive phenotypes, if recurring patterns can be found that reliably correlate with them. The brain meets the mathematical definition of a complex dynamical system and EEG measurements are time series or signals produced by local clusters of neurons in this system. This chapter presents a methodolog...
BMC Medicine
EEG complexity as a biomarker for autism spectrum disorder risk
2011 •
William Bosl
