A minireview on the use of wavelet analyses on physiological signals to diagnose and characterize ADHD
Keywords:ADHD, EEG, MRI, Physiological Signals, Wavelet Analysis.
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.
 A Polanczyk, G., et al., The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatry, 2007? 164(6): p. 942-8. https://doi.org/10.1176/ajp.2007.164.6.942.
 Doyle, A.E., et al., Diagnostic efficiency of neuropsychological test scores for discriminating boys with and without attention deficit-hyperactivity disorder. J Consult Clin Psychol, 2000. 68(3): p. 477-88. https://doi.org/10.1037/0022-006X.68.3.477.
 Gualtieri C.T. and L.G. Johnson, ADHD: Is Objective Diagnosis Possible? Psychiatry (Edgmont), 2005. 2(11): p. 9.
 Lenartowicz, A. and S.K. Loo, Use of EEG to diagnose ADHD. Curr Psychiatry Rep, 2014. 16(11): p. 498. https://doi.org/10.1007/s11920-014-0498-0.
 Dickhaus, H. and H. Heinrich, EP parametrization and classification using wavelet networks--theoretical concept and medical application. Stud Health Technol Inform, 1997. 43 Pt B: p. 541-5.
 Heinrich H., et al., Time-on-task analysis using wavelet networks in an event-related potential study on attention-deficit hyperactivity disorder. Clin Neurophysiol., 2001. 112(7): p. 7. https://doi.org/10.1016/S1388-2457(01)00541-7.
 Yordanova, J., et al., Abnormal early stages of task stimulus processing in children with attention-deficit hyperactivity disorder--evidence from event-related gamma oscillations. Clin Neurophysiol, 2001. 112(6): p. 1096-108. https://doi.org/10.1016/S1388-2457(01)00524-7.
 Gross, E., et al., Induced Eeg Gamma Oscillation Alignment Improves Differentiation between Autism and Adhd Group Responses in a Facial Categorization Task. J Neurother, 2012. 16(2): p. 78-91. https://doi.org/10.1080/10874208.2012.677631.
 Yordanova, J., et al., Increased event-related theta activity as a psychophysiological marker of comorbidity in children with tics and attention-deficit/hyperactivity disorders. Neuroimage, 2006. 32(2): p. 940-55. https://doi.org/10.1016/j.neuroimage.2006.03.056.
 Yordanova, J., et al., Comorbidity in the context of neural network properties. Behav Brain Sci, 2010. 33(2-3): p. 176-7. https://doi.org/10.1017/S0140525X1000083X.
 Yordanova, J., V. Kolev, and A. Rothenberger, Event-related oscillations reflect functional asymmetry in children with attention deficit/hyperactivity disorder. Suppl Clin Neurophysiol, 2013. 62: p. 289-301. https://doi.org/10.1016/B978-0-7020-5307-8.00018-1.
 Ghosh-Dastidar, S., H. Adeli, and N. Dadmehr, Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans Biomed Eng, 2007. 54(9): p. 1545-51. https://doi.org/10.1109/TBME.2007.891945.
 Ahmadlou, M. and H. Adeli, Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD. Clin EEG Neurosci, 2010. 41(1): p. 1-10. https://doi.org/10.1177/155005941004100103.
 Alexander, D.M., et al., Event-related wave activity in the EEG provides new marker of ADHD. Clin Neurophysiol, 2008. 119(1): p. 163-79. https://doi.org/10.1016/j.clinph.2007.09.119.
 Alexander, D.M., et al., Measurement of phase gradients in the EEG. J Neurosci Methods, 2006. 156(1-2): p. 111-28. https://doi.org/10.1016/j.jneumeth.2006.02.016.
 Lee, S.H., et al., Analysis of Attention Deficit Hyperactivity Disorder in EEG Using Wavelet Transform and Self Organizing Maps, in International Conference on Control, Automation and Systems 2010: Gyeonggi-do, Korea.
 Won-Seok, K., A method of mother wavelet function learning for DWT-based analysis using EEG signals in Sensors, 2011, IEEE, Editor 2011: Limerick, Ireland. p. 3.
 Hillard, B., et al., Neurofeedback training aimed to improve focused attention and alertness in children with ADHD: a study of relative power of EEG rhythms using custom-made software application. Clin EEG Neurosci, 2013. 44(3): p. 193-202. https://doi.org/10.1177/1550059412458262.
 Dockstader, C., et al., MEG event-related desynchronization and synchronization deficits during basic somatosensory processing in individuals with ADHD. Behav Brain Funct, 2008. 4: p. 8. https://doi.org/10.1186/1744-9081-4-8.
 Franzen, J.D., et al., Atypical coupling between posterior regions of the default mode network in attention. Psychiatry Neurosci. , 2013. 38(5): p. 7.
 Anderson, C.M., S.B. Lowen, and P.F. Renshaw, Emotional task-dependent low-frequency fluctuations and methylphenidate: Wavelet scaling analysis of 1/f-type fluctuations in fMRI of the cerebellar vermis. J Neurosci Methods, 2006. 151(1): p. 52-61. https://doi.org/10.1016/j.jneumeth.2005.09.020.
 Mairena, M.A., et al., Low frequency oscillations of response time explain parent ratings of inattention and hyperactivity/impulsivity. Eur Child Adolesc Psychiatry, 2012. 21(2): p. 101-9. https://doi.org/10.1007/s00787-011-0237-6.
 Romero SÃ¡nchez, K., et al., Wavelet analysis of MR functional data from the cerebellum. , in XIII Mexican Symposium on Medical Physics201|4, AIP Conference Proceedings: LeÃ³n, Mexico.
 GonzÃ¡lez GÃ³mez, D.I., et al., Difference between healthy children and ADHD based on wavelet spectral analysis of nuclear magnetic resonance images, in XIII Mexican Symposium on Medical Physics, A.C. Proceedings, Editor 2014: LeÃ³n, MÃ©xico. p. 4.
 SuÃ¡rez GarcÃa, J.G., et al. Diagnosis of ADHD children by wavelet analysis. in XIV International Mexican Symposium on Medical Physics. 2016. Mexico DF, Mexico: AIP Conference Proceedings. http://dx.doi.org/10.1063/1.4954096
 Reiss, P.T., et al., Wavelet-Domain Regression and Predictive Inference in Psychiatric Neuroimaging. Ann Appl Stat, 2015. 9(2): p. 1076-1101. https://doi.org/10.1214/15-AOAS829.
 Di Martino, A., et al., Decomposing intra-subject variability in children with attention-deficit/hyperactivity disorder. Biol Psychiatry, 2008. 64(7): p. 607-14. https://doi.org/10.1016/j.biopsych.2008.03.008.
 Najarian, K. and R. Splinter, Biomedical Signal and Imaging Processing. 2012, Boca Raton, Florida, USA: CRC Press. 376.
 Rafieea, J., et al., Wavelet basis functions in biomedical signal processing. Expert Systems with Applications, 2011. 38(5): p. 11. https://doi.org/10.1016/j.eswa.2010.11.050.
 Imaeda, S., et al. Analysis of ER-fMRI time series with modified mother wavelet. in International Symposium on Comunications and Infromation Technologies. 2004. Sapporo, Japan. https://doi.org/10.1109/ISCIT.2004.1413837.
 Gandhi, T., B.K. Panigrahi, and S. Anand, A comparative study of wavelet families for EEG signal classification. Neurocomputing, 2011. 74: p. 6. https://doi.org/10.1016/j.neucom.2011.04.029.
 Al-Fahoum, A.S. and A.A. Al-Fraihat, Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neurosci, 2014. 2014: p. 730218.