A minireview on the use of wavelet analyses on physiological signals to diagnose and characterize ADHD

Abstract

Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent psychological disorders in pediatric patients. The actual golden standard of ADHD diagnosis is based on conclusions derived from clinical questionnaires. Nowadays, there is no quantitative measurement performed with any imaging system (MRI, PET, EEG, etc.) that can be considered as a golden standard for this diagnosis. This issue, is highlighted by the existence of international competitions focused on the production of a technological (quantitative) solution capable of complementing ADHD diagnosis (ADHD-200 Global Competition). Wavelet analysis, on the other hand, is a flexible mathematical tool that can be used for information and data processing. Its advantage over other types of mathematical transformations is its ability to decompose a signal into two parameters (frequency and time). Based on the prevalence of ADHD and the extra functionality of wavelet tools, this review will try to answer the following question: How have wavelet analyses been used to complement diagnosis and characterization of ADHD? It will be shown that applications were not casual and limited to time-frequency decomposition, noise removal or down sampling of signals, but were pivotal for construction of learning networks, specific parameterization of signals or calculations of connectivity between brain nodes.

Predictive Modelling of The Dynamic Patterns of Thinking in Attention-Deficit/Hyperactivity Disorder: Diagnostic Accuracy of Spatiotemporal Fractal Measures

“…This review included other existing methods proposed to diagnose ADHD, that were not covered in the former systematic review. To make the indirect comparison possible, this in-house scoping review included only diagnostic accuracy studies that reported the chosen metrics of diagnostic for methods based on; electroencephalography and eventrelated potentials (Marcano et al, 2017;Loo et al, 2016;Snyder et al, 2015;Mohammadi et al, 2016;Gloss et al, 2016;Biederman et al, 2017;Gamma and Kara, 2016;Marcano et al, 2018;Manouilenko et al, 2017), structural and functional neuroimaging (Iannaccone et al, 2015;Rangarajan et al, 2014;de Celis Alonso et al, 2017;Qureshi et al, 2017;Serrallach et al, 2016;Hasaneen et al, 2017;Tan et al, 2017b;Uddin et al, 2017), simulated virtual reality and computer games (Negut et al, 2017(Negut et al, , 2016Berger et al, 2017;Faraone et al, 2016), and peripheral biochemical markers (Faraone et al, 2014;Scassellati and Bonvicini, 2015;Scassellati et al, 2012;Thome et al, 2012). According with this complementary "scoping review", the method presented in this research, seems to outperforms all the diagnostic accuracy metrics reported in the trials scrutinised in the aforementioned review.…”

The Dynamic Patterns Of Thinking In Attention-Deficit/Hyperactivity Disorder (ADHD): Diagnostic Accuracy Of Euclidean, Multifractal And Lacunarity Measures

“…This review included other existing methods proposed to diagnose ADHD, that were not covered in the former systematic review. To make the indirect comparison possible, this in-house scoping review included only diagnostic accuracy studies that reported the chosen metrics of diagnostic for methods based on; electroencephalography and event-related potentials(Marcano et al, 2017;Loo et al, 2016;Mohammadi et al, 2016;Gloss et al, 2016;Biederman et al, 2017;Gamma and Kara, 2016;Marcano et al, 2018;Manouilenko et al, 2017), structural and functional neuroimaging(Iannaccone et al, 2015;Rangarajan et al, 2014;de Celis Alonso et al, 2017;Qureshi et al, 2017;Serrallach et al, 2016;Hasaneen et al, 2017;Uddin et al, 2017;Tan et al, 2017), simulated virtual reality and computer games(Negut et al, 2017(Negut et al, , 2016Berger et al, 2017;Faraone et al, 2016), and peripheral biochemical markersScassellati and Bonvicini, 2015;. According with this complementary "scoping review",…”