PANDAS: Paediatric Attention-Deficit/Hyperactivity Disorder Application Software

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a common neuropsychiatric disorder that impairs social, academic and occupational functioning in children, adolescents and adults. In South Africa, youth prevalence of ADHD is estimated as 10%. It is therefore necessary to further investigate methods that objectively diagnose, treat and manage the disorder. The aim of the study was to develop a novel method that could be used as an aid to provide screening for ADHD. The study comprised of a beta-testing phase that included 30 children (19 non-ADHD and 11 ADHD) between the ages of 5 and 16 years old. The strategy was to use a tablet-based game that gathered real-time user data during game-play. This data was then used to train a linear binary support vector machine (SVM). The objective of the SVM was to differentiate between an ADHD individual versus a non-ADHD individual. A feature set was extracted from the gathered data and sequential forward selection (SFS) was performed to select the most significant features. The test set accuracy of 85.7% and leave-one-out cross-validation (LOOCV) accuracy of 83.5% were achieved. Overall, the classification accuracy of the trained SVM was 86.5%. Finally, the sensitivity of the model was 75% and this was seen as a moderate result. Since the sample size was fairly small, the results of the classifier were only seen as suggestive rather than conclusive. Therefore, the performance of the classifier was indicative that a quantitative tool could indeed be developed to perform screening for ADHD.

Dilud: A Mobile Application to Reinforce Rote Learning in Elementary School Children with Attention Deficit Hyperactivity Disorder

“…Another application is PANDAS, which aims to detect children's ADHD quickly through a game. Authors of PANDAS concluded that the model used in their game helped detect ADHD in children with an accuracy of 86.5% and was easy for children to use [11].…”

“…The recommendation to add more games in the following updates is highlighted because when it compares to the Say-It & Learn application, it has more interactive games focused on improving children's cognitive areas [12]. While the PANDAS application presented an interactive game to measure the WM of children, generating a high value in efficiency and usability [11]. Likewise, in this sense, the badge system, a new module not implemented in the reviewed applications, contributed significantly to children's usability experience and satisfaction, according to the comments received by parents.…”

Special Issue on “Human Health Engineering”

“…Contributions can focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing of physiological variables [24][25][26][27][28][29][30][31], contributions describing research on the modelling of biological signals [32][33][34][35][36][37][38], papers highlighting health monitoring applications [39][40][41][42], and finally examples of control applications for human health [43][44][45][46][47][48]. In comparison to biomedical engineering, we envision that the field of human health engineering also covers applications on healthy humans (e.g., sports, sleep, and stress) and thus not only contributes to develop technology for curing patients or supporting chronically ill people, but also for disease prevention and optimizing human well-being more generally.…”

“…The clinical relevance of such a model is that it allows simulation and evaluation of heart activity and dynamics under different types of pacemaker couplings. The study of Mwamba et al [35] contributed to the diagnosis and management of attention-deficit/hyperactivity disorder (ADHD) by developing a tablet-based application (app) software using support vector machine (SVM) approaches from machine learning. Their approach demonstrated that (serious) games in combination with modelling approaches from the field of artificial intelligence can offer a useful tool for first line screening of children for ADHD by their parents and/or teachers.…”