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01.Computational Strategies in the Evaluation of Attention (pp. 155-187)
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Computational Strategies in the Evaluation of Attention (pp. 155-187) $25.00
Authors:  Robeva, Raina (Sweet Briar College, Sweet Briar, Virginia) Kovatchev, Boris; Penberthy, Jennifer Kim; Cox, Daniel; Breton, Marc (University of Virginia, Charlottesville, Virginia)
Abstract:
Attention Deficit/Hyperactivity Disorder (ADHD) is the most common developmental disorder of childhood, affecting 3 - 5% of children in the United States, and often continuing into adulthood. ADHD is associated with multiple serious complications, including poor school achievement, substance abuse, automobile accidents, etc. The symptoms of ADHD are manifestations of disruption of selfregulation, including dysregulation of inhibition and attention. These manifestations take the form of psychological symptoms, disturbances in cognitive performance, behavioral dysregulation, and electroencephalographic (EEG) deviations. Therefore, these manifestations are observed across various domains and time frames, and are proposed to be separate factors, or components, of an underlying disruption of self-regulation. These components are assessed by various tests, each with a different sensitivity and specificity. Currently there is no single objective procedure to diagnose and quantify ADHD, making it difficult to determine who does, and does not have, the disorder, what medication and what dose of medication is optimally effective, or whether the condition is changing with maturation. This chapter presents several recently developed computational procedures that have been shown to improve the assessment of ADHD. The first set of procedures refers to analysis of (EEG) data. Three distinct algorithms are discussed, each looking at the effect of ADHD on brain waves at a different time scale: the Engagement Index, originally developed by NASA and working on a time scale of seconds, the Alpha Blockade Index working on a scale of 2-3 minutes, and the Consistency Index working on a time scale of half hour. Further, we present a sequential probability model using Bayesian inference to combine disparate (biometric or psychometric) tests into a single assessment. This procedure allows for a stepwise increasingly precise evaluation and a concurrent decrease of the number of unclassified cases. We illustrate the procedures using data from several studies conducted at the University of Virginia and Sweet Briar College. We conclude that advanced computational analysis of traditional data could result in an assessment of ADHD that is superior to the traditional assessment procedures. 


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Computational Strategies in the Evaluation of Attention (pp. 155-187)