Detection of attention deficit hyperactivity disorder based on EEG signals using Least Square Support Vector Machine (LS-SVM)

References

• Ahmadlou M, Adeli H. 2010. Wavelet-synchronization methodology: a new approach for EEG-Based diagnosis of ADHD. Clin EEG Neurosci. 41(1):1–10. doi: 10.1177/155005941004100103
   PubMed Web of Science ®Google Scholar
• Allahverdy A, Khorrami Moghadam A, Reza Mohammadi M, Motie Nasrabadi A. 2016. Detecting adhd children using the attention continuity as nonlinear feature of eeg. Front Biomed Technol. 3(1–2):28–33.
   Google Scholar
• Allahverdy A, Moti Nasrabadi A, Reza Mohammadi M. 2011. Detecting ADHD children using symbolic dynamic of nonlinear features of EEG. In 2011 19th Iranian Conference on Electrical Engineering, 17-19 May 2011, Tehran, Iran, IEEE, 1–4.
   Google Scholar
• Barry RJ, Clarke AR, Johnstone SJ. 2003. A review of electrophysiology in attention-deficit/hyperactivity disorder: i. qualitative and quantitative electroencephalography. Clin Neurophysiol. 114(2):171–183. doi: 10.1016/S1388-2457(02)00362-0
   PubMed Web of Science ®Google Scholar
• Berrar D. 2019. Cross-validation.
   Google Scholar
• Castellanos FX, Lee PP, Sharp W, Jeffries NO, Greenstein DK, Clasen LS, Blumenthal JD, James RS, Ebens CL, Walter JM, Zijdenbos A 2002. Developmental trajectories of brain volume abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. Jama. 288(14):1740–1748. doi: 10.1001/jama.288.14.1740.
   PubMed Web of Science ®Google Scholar
• Catherine Joy R, Thomas George S, Albert Rajan A, Subathra MSP. 2022. Detection of Adhd from eeg signals using different entropy measures and ann. Clin EEG Neurosci. 53(1):12–23. doi: 10.1177/15500594211036788
   PubMed Web of Science ®Google Scholar
• Chen H, Chen W, Song Y, Sun L, Li X. 2019. EEG characteristics of children with attention-deficit/hyperactivity disorder. Neuroscience. 406:444–456. doi: 10.1016/j.neuroscience.2019.03.048.
   PubMed Web of Science ®Google Scholar
• Chen L, Chen P, Zhao S, Luo Z, Chen W, Pei Y, Zhao H, Jiang J, Xu M, Yan Y. 2021. Adaptive asynchronous control system of robotic arm based on augmented reality-assisted brain–computer interface. J Neural Eng. 18(6):66005. doi: 10.1088/1741-2552/ac3044.
   Web of Science ®Google Scholar
• Chen H, Song Y, Xiaoli L. 2019. A deep learning framework for identifying children with ADHD using an EEG-Based brain network. Neurocomputing. 356:83–96. doi:10.1016/j.neucom.2019.04.058.
   Web of Science ®Google Scholar
• Dhar P, Dutta S, Mukherjee V, Dhar A, Das P. 2019. Cross-wavelet transform as a new prototype for classification of EEG signals. Comput Methods Biomech Biomed Eng: Imaging Vis. 7(3):348–358. doi: 10.1080/21681163.2018.1523749.
   Web of Science ®Google Scholar
• Einizade A, 2020. Detecting ADHD children based on EEG signals using graph signal processing techniques. 27th National and 5th International Iranian Conference of Biomedical Engineering, ICBME 2020, Tehran, Iran November: 264–270.
   Google Scholar
• Ekhlasi A, Motie Nasrabadi A, Mohammadi M. 2021. Classification of the children with ADHD and healthy children based on the directed phase transfer entropy of EEG signals. Front Biomed Technol. 8(2):115–122. doi: 10.18502/fbt.v8i2.6515
   Google Scholar
• Faris NN, Saber Miften F. 2022. An intelligence model for detection of PCOS based on K‐means coupled with LS‐SVM. Concurr Comput Pract And Exp. 34(21):e7139. doi: 10.1002/cpe.7139
   Web of Science ®Google Scholar
• Fonseca LC, Tedrus GMAS, Moraes CD, Machado ADV, Almeida MPD, Oliveira DOFD. 2008. Epileptiform abnormalities and quantitative EEG in children with attention-deficit/hyperactivity disorder. Arq Neuropsiquiatr. 66:462–467. doi:10.1590/S0004-282X2008000400004.
   PubMed Web of Science ®Google Scholar
• Hand DJ. 2007. Principles of data mining. Drug Safety. 30(7):621–622. doi: 10.2165/00002018-200730070-00010
   PubMed Web of Science ®Google Scholar
• Han J, Kamber M, Pei J. 2011. Data Mining: Concepts and Techniques [Internet]. Waltham.
   Google Scholar
• Jain AK. 2010. Data clustering: 50 years beyond K-Means. Pattern Recognit Lett. 31(8):651–666. doi: 10.1016/j.patrec.2009.09.011
   Web of Science ®Google Scholar
• Jain AK, Duin RPW, Mao J. 2000. Statistical pattern recognition: a review. Ieee T Pattern Anal. 22(1):4–37. doi: 10.1109/34.824819
   Web of Science ®Google Scholar
• Jiang J, Wang C, Wu J, Qin W, Xu M, Yin E. 2020. Temporal combination pattern optimization based on feature selection method for motor imagery BCIs. Front Hum Neurosci. 14:231. doi: 10.3389/fnhum.2020.00231.
   PubMed Web of Science ®Google Scholar
• Kessler RC, Adler LA, Berglund P, Green JG, McLaughlin KA, Fayyad J, Russo LJ, Sampson NA, Shahly V, Zaslavsky AM, et al. 2014. The effects of temporally secondary co-morbid mental disorders on the associations of DSM-IV ADHD with adverse outcomes in the US national comorbidity survey replication adolescent supplement (NCS-A). Psychol Med. 44(8):1779–1792. doi: 10.1017/S0033291713002419.
   PubMed Web of Science ®Google Scholar
• King JA, 2003. Neural substrates underlying impulsivity. Ann N Y Acad Sci. 1008(1):160–169. doi: 10.1196/annals.1301.017.
   PubMedGoogle Scholar
• Kohavi R, Kohavi R. 1998. Glossary of Terms.” Special issue on applications of machine learning and the knowledge discovery process. Mach Learn. 30(271):127–132. doi: 10.1023/A:1007442505281
   Google Scholar
• Laura D-V, Ruffini G, Camprodon JA. 2020. Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG. Front Neurosci. 14(April):1–12. doi: 10.3389/fnins.2020.00251
   PubMedGoogle Scholar
• Lenzi F, Cortese S, Harris J, Masi G. 2018. Pharmacotherapy of emotional dysregulation in adults with ADHD: a systematic review and meta-analysis. Neurosci Biobehav Rev. 84:359–367. doi:10.1016/j.neubiorev.2017.08.010.
   PubMed Web of Science ®Google Scholar
• Lwin WM, Aung T, Khaing Wai K. 2019. Applications of wavelet transform. In Proceedings of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfe. 1:12–15. [accessed 2019 Jan 26].
   Google Scholar
• Maya-Piedrahita MC, Cárdenas-Peña D, Orozco-Gutierrez AA. 2021. Diagnosis of Attention Deficit and Hyperactivity Disorder (ADHD) using hidden markov models. In 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands, IEEE, 1205–1209.
   Google Scholar
• Miften FS, Diykh M, Abdulla S, Green JH. 2020. Epileptic seizures detection based on non-linear characteristics coupled with machine learning techniques.
   Google Scholar
• Moghaddari M, Zolfy Lighvan M, Danishvar S. 2020. Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG. Comput Methods Programs Biomed. 197. doi: 10.1016/j.cmpb.2020.105738.
   PubMed Web of Science ®Google Scholar
• Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. 2016. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Engine Letters. 6(2):66–73. doi: 10.1007/s13534-016-0218-2
   Web of Science ®Google Scholar
• Moore IV, Hyatt E. 2013. Visualization of EEG activity for stimulating sleep research. Comput Methods Biomech Biomed Eng: Imaging Vis. 1(4):225–233. doi: 10.1080/21681163.2013.805047
   Google Scholar
• Mueller A, Candrian G, Grane VA, Kropotov JD, Ponomarev VA, Baschera GM. 2011. Discriminating between ADHD Adults and controls using independent ERP components and a support vector machine: a validation study. Nonlinear Biomed Phys. 5(1):1–18. doi: 10.1186/1753-4631-5-5.
   PubMedGoogle Scholar
• Nasrabadi AM, Allahverdy A, Samavati M, Reza Mohammadi M. 2020. EEG data for ADHD/control children. IEEE Dataport, Iran.
   Google Scholar
• Onan A. 2018a. An ensemble scheme based on language function analysis and feature engineering for text genre classification. J Inf Sci. 44(1):28–47. doi: 10.1177/0165551516677911
   Web of Science ®Google Scholar
• Onan A. 2018b. Biomedical text categorization based on ensemble pruning and optimized topic modelling. Comput Math Method M. 2018. doi: 10.1155/2018/2497471.
   PubMed Web of Science ®Google Scholar
• Onan A. 2019a. Consensus clustering-based undersampling approach to imbalanced learning. Sci Program-Neth. 2019. doi: 10.1155/2019/5901087.
   Web of Science ®Google Scholar
• Onan A. 2019b. Topic-enriched word embeddings for sarcasm identification. Software Engine Meth Intel Algor: Procee 8th Comp Sci On-Line Conf 2019. 1(8):293–304. Springer.
   Google Scholar
• Onan A. 2019c. Two-stage topic extraction model for bibliometric data analysis based on word embeddings and clustering. IEEE Access. 7:145614–145633. doi:10.1109/ACCESS.2019.2945911.
   Web of Science ®Google Scholar
• Onan A. 2020. Mining opinions from instructor evaluation reviews: a deep learning approach. Comput Appl Eng Educ. 28(1):117–138. doi: 10.1002/cae.22179
   Web of Science ®Google Scholar
• Onan A. 2021a. Sentiment analysis on massive open online course evaluations: a text mining and deep learning approach. Comput Appl Eng Educ. 29(3):572–589. doi: 10.1002/cae.22253
   Web of Science ®Google Scholar
• Onan A. 2021b. Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurr Comput Pract And Exp. 33(23):e5909. doi: 10.1002/cpe.5909
   Web of Science ®Google Scholar
• Onan A. 2022. Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification. J King Saud Univ-Comp And Inform Sci. 34(5):2098–2117. doi: 10.1016/j.jksuci.2022.02.025
   Web of Science ®Google Scholar
• Onan A, Korukoğlu S. 2017. A feature selection model based on genetic rank aggregation for text sentiment classification. J Inf Sci. 43(1):25–38. doi: 10.1177/0165551515613226
   Web of Science ®Google Scholar
• Onan A, Korukoğlu S, Bulut H. 2016. Ensemble of keyword extraction methods and classifiers in text classification. Expert Syst Appl. 57:232–247. doi:10.1016/j.eswa.2016.03.045.
   Web of Science ®Google Scholar
• Onan A, Korukoğlu S, Bulut H. 2017. A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification. Inf Process Manag. 53(4):814–833. doi: 10.1016/j.ipm.2017.02.008
   Web of Science ®Google Scholar
• Ramos R, Valdez-Salas B, Zlatev R, Schorr Wiener M, Bastidas Rull JM. 2017. The discrete wavelet transform and its application for noise removal in localized corrosion measurements. Int J Corros. 2017:1–7. doi:10.1155/2017/7925404.
   Web of Science ®Google Scholar
• Saraiva AA, Castro FM, Nascimento RC, de Melo RT, Moura Sousa JV, Valente A, Fonseca Ferreira NM. 2020. Electroencephalography applied compression algorithms qualitative analysis. Comput Methods Biomech Biomed Eng: Imaging Vis. 8(4):367–373. doi: 10.1080/21681163.2019.1673212.
   Web of Science ®Google Scholar
• Saroja B, SelwinMich Priyadharson A. 2019. Adaptive pillar K-Means clustering-based colon cancer detection from biopsy samples with outliers. Comput Methods Biomech Biomed Eng: Imaging Vis. 7(1):1–11. doi: 10.1080/21681163.2017.1350603
   Google Scholar
• Suykens JAK, Vandewalle J. 1999. Least squares support vector machine classifiers. Neural Process Lett. 9(3):293–300. doi: 10.1023/A:1018628609742
   Web of Science ®Google Scholar
• Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J. 2002. Least squares support vector machines. Singapore: World Scientific Publishing. doi: 10.1142/5089.
   Google Scholar
• Taghibeyglou B, Hasanzadeh N, Bagheri F, Jahed M. 2020. ADHD diagnosis in children using common spatial pattern and nonlinear analysis of filter banked EEG. 2020 28th Iranian Conference on Electrical Engineering, ICEE, Iran 2020: 0–4.
   Google Scholar
• Thapar A, Cooper M, Jefferies R, Stergiakouli E. 2012. What causes attention deficit hyperactivity disorder? Arch Dis Child. 97(3):260–265. doi: 10.1136/archdischild-2011-300482
   PubMed Web of Science ®Google Scholar
• Ukkola O, Bouchard C. 2001. Clustering of metabolic abnormalities in obese individuals: the role of genetic factors. Ann Med. 33(2):79–90. doi: 10.3109/07853890109002062
   PubMed Web of Science ®Google Scholar
• Vahid A, Bluschke A, Roessner V, Stober S, Beste C. 2019. Deep learning based on event-related EEG differentiates children with ADHD from healthy controls. J Clin Med. 8(7):1055. doi: 10.3390/jcm8071055
   PubMed Web of Science ®Google Scholar