A Probabilistic Classification Procedure Based on Response Time Analysis Towards a Quick Pre-Diagnosis of Student’s Attention Deficit

Abstract

A classification methodology based on an experimental study is proposed towards a fast pre-diagnosis of attention deficit. Our sample consisted of school-aged children between 8 and 12 years from Valencia, Spain. The study was based on the response time (RT) to visual stimuli in computerized tasks. The process of answering consecutive questions usually follows an ex-Gaussian distribution of the RTs. Specifically, we seek to propose a simple automatic classification scheme of children based on the most recent evidence of the relationship between RTs and ADHD. Specifically, the prevalence percentage and reported evidence for RTs in relation to ADHD or to attention deficit symptoms were taken as reference in our study. We explain step by step how to go from the computer-based experiments and through the data analysis. Our desired aim is to provide a methodology to determine quickly those children who behave differently from the mean child in terms of response times and thus are potential candidates to be diagnosed for ADHD or any another cognitive disorder related to attention deficit. This is highly desirable as there is an urgent need for objective instruments to diagnose attention deficit symptomatology. Most of the methodologies available nowadays lead to an overdiagnosis of ADHD and are not based on direct measurement but on interviews of people related to the child such as parents or teachers. Although the ultimate diagnosis must be made by a psychologist, the selection provided by a methodology like ours could allow them to focus on assessing a smaller number of candidates which would help save time and other resources.

Percentile Study of χ Distribution. Application to Response Time Data

“…We are particularly interested in applications to biomedical and collective behavior contexts. In this respect, and in line with our previous works [3,4], we will carry out here a percentile study of the χ distribution. Taking as our reference the most commonly used percentiles, we will report on the percentile ratios, such that the χ PDF can be easily identifiable when exploring data samples.…”

“…In the second part of this work we will focus on a percentile study of the MB distribution (k = 3), which is in line with our previous works on reaction times to visual stimuli [3,4]. In addition to the aforementioned percentile combinations (labeled as 1 to 7 in Section 2.1), the following ones covering a broader area under the curve will be determined: 8 (p0.5-p50-p99.5), 9 (p0.7-p50-p99.3), 10 (p2-p50-p98), 11 (p7-p50-p93), 12 (p15-p50-p85), and 13 (p40-p50-p60).…”

“…The participants were asked to respond to visual stimuli within a few hundred milliseconds. We have proven in our previous work [3] that the collective behavior of individuals at short times is ruled by an MB distribution which depends on a single parameter B, namely, a measure of the dispersion of the distribution.…”

“…In a recent work [3], we discovered the MB distribution (k = 3) when studying the reaction times of a collective of 168 children. A correspondence between a system of brains and a system of identical, independent particles forming the ideal gas was evidenced.…”

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

“…Artificial Intelligence (AI) plays a major role in modern medicine and is key in automating tasks such as image registration [1][2][3], diagnosis [4,5] and prognosis [6,7], thus allowing for doctors and practitioners to make faster and more accurate decisions [8]. Indeed, recent advances in AI have made it possible for expert systems [9] and machine learning techniques [1,10] to outperform human decision-making over some repetitive procedures and are definitely more accurate at spotting hidden patterns in medical images or signals.…”