Pubmed du 14/06/21
1. Baygin M, Dogan S, Tuncer T, Datta Barua P, Faust O, Arunkumar N, Abdulhay EW, Emma Palmer E, Rajendra Acharya U. Automated ASD detection using hybrid deep lightweight features extracted from EEG signals. Computers in biology and medicine. 2021; 134: 104548.
BACKGROUND: Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model. MATERIALS AND METHOD: We propose a hybrid lightweight deep feature extractor to obtain high classification performance. The system was designed and tested with a big EEG dataset that contained signals from autism patients and normal controls. (i) A new signal to image conversion model is presented in this paper. In this work, features are extracted from EEG signal using one-dimensional local binary pattern (1D_LBP) and the generated features are utilized as input of the short time Fourier transform (STFT) to generate spectrogram images. (ii) The deep features of the generated spectrogram images are extracted using a combination of pre-trained MobileNetV2, ShuffleNet, and SqueezeNet models. This method is named hybrid deep lightweight feature generator. (iii) A two-layered ReliefF algorithm is used for feature ranking and feature selection. (iv) The most discriminative features are fed to various shallow classifiers, developed using a 10-fold cross-validation strategy for automated autism detection. RESULTS: A support vector machine (SVM) classifier reached 96.44% accuracy based on features from the proposed model. CONCLUSIONS: The results strongly indicate that the proposed hybrid deep lightweight feature extractor is suitable for autism detection using EEG signals. The model is ready to serve as part of an adjunct tool that aids neurologists during autism diagnosis in medical centers.
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2. Iannuzzi D, Fell L, Luberto C, Goshe BM, Perez G, Park E, Crute S, Kuhlthau K, Traeger L. Challenges and Growth: Lived Experience of Adolescents and Young Adults (AYA) with a Sibling with ASD. Journal of autism and developmental disorders. 2021.
Adolescent and young adult (AYA) siblings of individuals with autism experience unique challenges that can promote both growth and emotional maladjustment. This study explored sibling and parent reports of siblings’ lived experiences and identified learning, stressors, and concerns from those experiences. 20 neurotypical (NT) AYA siblings (ages 13-24), and 21 parents were interviewed. Themes that emerged from the data analysis included: (1) learning, empathy, and compassion (2) relationship between the degree of functional impairment and the nature of the sibling relationship; (3) reluctance to share information about siblings with peers; (4) hypervigilance associated with unpredictable behavior; (5) worries and concerns about the future. These findings contribute to the existing literature on the impact and nature of neurotypical siblings’ lived experience.
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3. Lindström ER, Lemons CJ. Teaching Reading to Students with Intellectual and Developmental Disabilities: An Observation Study. Research in developmental disabilities. 2021; 115: 103990.
BACKGROUND: Growing evidence supports the efficacy of multicomponent, explicit, phonics-based reading instruction for students with intellectual and developmental disabilities (IDD). However, little is known about the implementation of such instruction. AIMS: The purpose of this observation study was to describe the content and quality of reading instruction provided to kindergarten through third grade students with IDD in self-contained classrooms. METHODS AND PROCEDURES: Researchers observed seven special education teachers and their seventeen students, examined teacher perspectives via survey and interview, and reviewed student Individualized Education Programs. Researchers coded 2,901 minutes of instruction for content, grouping, materials, instructional quality, engagement, and time spent reading connected text, using a tool adapted for the IDD population. OUTCOMES: Observed instructional content focused on phonics/word study, followed by vocabulary and comprehension, then other areas. Within the already small classes, instruction was generally delivered individually or in small groups. Instructional quality and engagement varied by activity. CONCLUSIONS AND IMPLICATIONS: Study findings suggest a need for greater systematic investigation of content and methods pertaining to reading instruction for students with IDD, instructional quality and engagement, and connections to student outcomes.
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4. Mello C, Rivard M, Morin D, Patel S, Morin M. Symptom Severity, Internalized and Externalized Behavioral and Emotional Problems: Links with Parenting Stress in Mothers of Children Recently Diagnosed with Autism. Journal of autism and developmental disorders. 2021.
The present study examined which aspects of the child’s behavior and clinical profile accounted for three dimensions of parenting stress: related to parenting roles, to interactions with the child, and to the child. Measures of adaptive behavior, intellectual functioning, autism symptom severity, and challenging behavior and emotional difficulties were examined as predictors of parenting stress in 157 mothers of children recently diagnosed with autism. Children’s emotional problems and aggressive behavior were most predictive of parenting distress, whereas autism symptoms along with emotional problems and aggressive behavior, respectively, were linked to stress pertaining to interactions and to the child. These findings underscore the need for comprehensive and complementary interventions that focus on children’s behavior and symptoms but also on parent adjustment.
