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A Systematic Review of Research Dimensions Towards Dyslexia Screening Using Machine Learning

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Abstract

Dyslexia is the hidden learning disability, neurobiological in origin wherein students face hard time in accurate or fluent word recognition, connecting letters to the sounds. In India, index of dyslexia is increasing exponentially. The level of difficulty of dyslexic children varies from person to person. Their brain is normal; often very “intelligent,” but with strengths and capabilities in areas other than the language area. Henceforth, such students are suffering from low self-esteem, are bipolar in nature, have negative feelings and depression. Therefore, early detection and evaluation of dyslexic students is very important and need of the hour. In this review paper, the authors have summed up various research dimensions toward dyslexia detection. This paper principally focuses on the machine learning techniques for dyslexia screening which includes applications covering different machine learning-based approaches, game-based techniques and image processing techniques for designing various assessments and assistive tools to support and ease the problems encountered by dyslexic people. This review paper identifies various knowledge gaps, current issues and future challenges in this research domain. It mainly focuses on various machine learning applications toward detection of dyslexia.

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Abbreviations

SVM:

Support Vector Machine

DT:

Decision Tree

RF:

Random Forest

NB:

Naive Bayes

MRI:

Magnetic Resonance Images

EEG:

Electroencephalogram

SVC:

Support Vector Classifier

SD:

Standard Deviation

PSO:

Particle Swarm Optimization

CNN:

Convolutional Neural Network

ANN:

Artificial Neural Network

LR:

Logistic Regression

References

  1. A. Frid, Z. Breznitz, An SVM based algorithm for analysis and discrimination of dyslexic readers from regular readers using ERPs. In: IEEE 27th convention of electrical & electronics engineers in Israel (IEEEI), Israel. pp 1–4, (2012)

  2. A. Palacios, L. Sánchez, I. Couso, S. Destercke, An extension of the FURIA classification algorithm to low quality data through fuzzy rankings and its application to the early diagnosis of dyslexia. Neurocomputing 176, 60–71 (2016). https://doi.org/10.1016/j.neucom.2014.11.088

    Article  Google Scholar 

  3. Y. Lakretz, G. Chechik, N. Friedmann, M. Rosen-Zvi, Probabilistic graphical models of dyslexia in proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp. 1919–1928. (2015). doi: https://doi.org/10.1145/2783258.2788604.

  4. L. Rello, M. Ballesteros, Detecting readers with dyslexia using machine learning with eye tracking measures in proceedings of the 12th international web for all conference. ACM, pp.1–8, (2015). doi: https://doi.org/10.1145/2745555.2746644

  5. A.Z.A. Zainuddin, K.Y. Lee, W. Mansor, Z. Mahmoodin, Optimized KNN classify rule for EEG based differentiation between capable dyslexic and normal children. In: 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES). pp. 685–688, (2016). doi: https://doi.org/10.1109/IECBES.2016.7843537.

  6. P. Plonski et al., Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia. Hum. Brain Mapp. 38(2), 900–908 (2016). https://doi.org/10.1002/hbm.23426

    Article  Google Scholar 

  7. M.N. Benfatto, G. Seimyr, J. Ygge, T. Pansell, A. Rydberg, C. Jacobson, Screening for dyslexia using eye tracking during reading. PLoS ONE 11(12), e0165508 (2016). https://doi.org/10.1371/journal.pone.0165508

    Article  Google Scholar 

  8. H. M. Al-Barhamtoshy, D.M. Motaweh, Diagnosis of dyslexia using computation analysis. In: 2017 International Conference on Informatics, Health Technology (ICIHT), pp. 1–7, (2017). doi: https://doi.org/10.1109/ICIHT.2017.7899141

  9. I. Smyrnakis et al., RADAR: a novel fast-screening method for reading difficulties with special focus on dyslexia. PLOS ONE. 12(8), e0182597 (2017). https://doi.org/10.1371/journal.pone.0182597

    Article  Google Scholar 

  10. L. Rello, E. Romero, M. Rauschenberger, A. Ali, K. Williams, J.P. Bigham, N.C. White (2018) Screening dyslexia for English using HCI measures and machine learning. In: Proceding International Conference Digital Health. pp. 80–84. doi: https://doi.org/10.1145/3194658.3194675

  11. S.S.A. Hamid, N. Admodisastro, N. Manshor, A. Kamaruddin, A.A.A. Ghani, Dyslexia adaptive learning model: student engagement prediction using machine learning approach. In: Recent Advances on Soft Computing and Data Mining: Advances in Intelligent Systems and Computing. R. Ghazali, M. Deris, N. Nawi, J. Abawajy, (Eds.) Cham, Switzerland: Springer, pp. 372–384, (2018). doi: https://doi.org/10.1007/978-3-319-72550-5_36

  12. R.U. Khan, J.L.A. Cheng, O.Y. Bee, Machine learning and dyslexia: diagnostic and classification system (DCS) for kids with learning disabilities. Int. J. Eng. Technol. 7(3): 97–100, (2018)

  13. H. Perera, M. Shiratuddin, K. Wong, K. Fullarton, EEG signal analysis of writing and typing between adults with dyslexia and normal controls. Int. J. Interact. Multim. Artific. Intell. 5(1), 62 (2018)

    Google Scholar 

  14. F.J. Martinez-Murcia, A. Ortiz, R., Morales-Ortega, P.J. Lopez, J.L. Luque, Castillo-Barnes, J.M. Górriz, Periodogram connectivity of EEG signals for the detection of dyslexia. In: International Work-Conference on the Interplay between Natural and Artificial Computation. Springer: Cham. pp. 350–359. (2019)

  15. A Jothi Prabha, R. Bhargavi, R. Ragala Predictive Model for Dyslexia from Eye Fixation Events. Int. J. Eng. Adv. Technol. 9(13): 235–240, (2019)

  16. K. Spoon, D. Crandall, K. Siek, Towards detecting dyslexia in children’s handwriting using neural networks. In: Proc. Int. Conf. Mach. Learn. AI Social Good Workshop, (2019). pp. 1–5.

  17. K. Spoon, K. Siek, D. Crandall, M. Fillmore, Can we (and should we) use AI to detect dyslexia in children’s handwriting? In: Proc. Artif. Intell. Social Good (NeurIPS), (2019) pp. 1–6.

  18. A. Jothi Prabha, R. Bhargavi, Predictive model for dyslexia from fixations and saccadic eye movement events. Comput. Methods Prog. Biomed. 195, 105538 (2020). https://doi.org/10.1016/j.cmpb.2020.105538

    Article  Google Scholar 

  19. P. Tamboer, H.C.M. Vorst, S. Ghebreab, H.S. Scholte, Machine learning and dyslexia: classification of individual structural neuroimaging scans of students with and without dyslexia. NeuroImage Clin. 11, 508–514 (2016). https://doi.org/10.1016/j.nicl.2016.03.014

    Article  Google Scholar 

  20. X. Feng et al., Dyslexic children show atypical cerebellar activation and Cerebro-cerebellar functional connectivity in orthographic and phonological processing. The Cerebellum. 16(2), 496–507 (2016). https://doi.org/10.1007/s12311-016-0829-2

    Article  MathSciNet  Google Scholar 

  21. Z. Cui, Z. Xia, M. Su, H. Shu, G. Gong, Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach. Human Brain Mapp. 37(4), 1443–1458 (2016). https://doi.org/10.1002/hbm.23112

    Article  Google Scholar 

  22. F. Morken, T. Helland, K. Hugdahl, K. Specht, Reading in dyslexia across literacy development: a longitudinal study of effective connectivity. Neuroimage. 144, 92–100 (2017). https://doi.org/10.1016/j.neuroimage.2016.09.060

    Article  Google Scholar 

  23. A. Frid, L.M. Manevitz, Features and machine learning for correlating and classifying between brain areas and dyslexia. (2018) arXiv preprint arXiv:1812.10622

  24. Z. Rezvani, M. Zare, G. Zaric, M. Bonte, J. Tijms, M. Van der Molen, G.F. Gonzalez, Machine learning classification of dyslexic children based on EEG Local network features. Bio Rxiv. 57, 1–23 (2019)

    Google Scholar 

  25. G. Richard, M. Serrurier, Dyslexia and Dysgraphia prediction: a new machine learning approach. (2020) arXiv preprint arXiv:2005.06401

  26. L. Rello, K. Williams, A. Ali, N. Cushen White, J.P. Bigham, Dytective: towards detecting dyslexia across languages using an online game. In: Proc. W4A’16, Montreal, Canada. ACM Press (2016)

  27. L. Rello, M. Ballesteros, A. Ali, M. Serra, D. Alarc´on, J.P. Bigham, Dytective: Diagnosing risk of dyslexia with a game. In: Proc Pervasive Health’16. Cancun: Mexico, (2016)

  28. N. Zygouris, F. Vlachos, A. Dadaliaris, P. Oikonomou, G.I. Stamoulis, D. Vavougios, E. Nerantzaki, A. Striftou, The implementation of a web application for screening children with dyslexia. In: 19th international conference on interactive collaborative learning. Springer: Cham. pp. 415–423, (2016)

  29. N.C. Zygouris, F. Vlachos, A.N. Dadaliaris, E. Karagos, P. Oikonomou, A. Striftou, G.I. Stamoulis, New tasks for a dyslexia screening web application. In: international conference on interactive collaborative learning. Springer: Cham. pp. 263–271, (2018)

  30. A. Jothi Prabha, R. Bhargavi, Prediction of dyslexia from eye movements using machine learning. IETE J. Res. (2019). https://doi.org/10.1080/03772063.2019.1622461

    Article  Google Scholar 

  31. V.F. Martins, T. Lima, P.N.M. Sampaio and M. de Paiva, Mobile application to support dyslexia diagnostic and reading practice. 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), (2016), pp. 1–6, doi: https://doi.org/10.1109/AICCSA.2016.7945710.

  32. S. Zahia, B. Garcia-Zapirain, I. Saralegui, B. Fernandez-Ruanova, Dyslexia detection using 3D convolutional neural networks and functional magnetic resonance imaging. Comput. Methods Prog. Biomed. 197, 105726 (2020). https://doi.org/10.1016/j.cmpb.2020.105726

    Article  Google Scholar 

  33. S. Karande, R. Sholapurwala, M. Kulkarni, Managing specific learning disability in schools in India. Indian Pediatr. 48(7), 515–520 (2011). https://doi.org/10.1007/s13312-011-0090-1

    Article  Google Scholar 

  34. S. Singh et al., Specific learning disability: a 5 year study from India. Int. J. Contemp. Pediatr. 4(3), 863 (2017). https://doi.org/10.18203/2349-3291.ijcp20171687

    Article  Google Scholar 

  35. S. Bandla, G.D. Mandadi, A. Bhogaraju, Specific learning disabilities and psychiatric comorbidities in school children in South India. Indian J. Psychol. Med. 39(1), 76–82 (2017). https://doi.org/10.4103/0253-7176.198950

    Article  Google Scholar 

  36. A. Ortiz, Periodogram connectivity of EEG signals. Int. Work. Conf. Interp. Nat. Artif. Comput. 1, 350–359 (2019). https://doi.org/10.1007/978-3-030-19591-5

    Article  Google Scholar 

  37. A. Ortiz, P.J. López, J.L. Luque, F.J. Martínez-Murcia, D.A. Aquino-Britez, J. Ortega, An anomaly detection approach for dyslexia diagnosis using EEG Signals. Lect. Notes Comput. Sci. Include. Subser. 11486, 369–378 (2019). https://doi.org/10.1007/978-3-030-19591-5_38

    Article  Google Scholar 

  38. H. Xue et al., Resting-state EEG reveals global network deficiency in dyslexic children. Neuropsychologia. 138, 107343 (2020). https://doi.org/10.1016/j.neuropsychologia.2020.107343

    Article  Google Scholar 

  39. P. Christodoulides et al., Classification of EEG signals from young adults with dyslexia combining a brain computer interface device and an interactive linguistic software tool. Biomed. Signal Process. Control. 76(7), 103646 (2022). https://doi.org/10.1016/j.bspc.2022.103646

    Article  Google Scholar 

  40. S.K. Parmar, O.A. Ramwala, and C.N. Paunwala, Performance evaluation of SVM with non-linear kernels for EEG-based dyslexia detection. IEEE Reg. 10 Humanit. Technol. Conf. R10-HTC. (2021). doi: https://doi.org/10.1109/R10-HTC53172.2021.9641696.

  41. N.J. Gallego-Molina, A. Ortiz, F.J. Martínez-Murcia, M.A. Formoso, A. Giménez, Complex network modeling of EEG band coupling in dyslexia: an exploratory analysis of auditory processing and diagnosis. Knowledge-Based Syst. 240, 108098 (2022). https://doi.org/10.1016/j.knosys.2021.108098

    Article  Google Scholar 

  42. A. Oliaee, M. Mohebbi, S. Shirani, R. Rostami, Extraction of discriminative features from EEG signals of dyslexic children; before and after the treatment. Cogn. Neurodyn. (2022). https://doi.org/10.1007/s11571-022-09794-2

    Article  Google Scholar 

  43. G.P. Pralhad, A. Joshi, M. Chippa, G. Mishra, S. Kumar. Dyslexia prediction using machine learning. pp. 3–8 (2021)

  44. G.D. Germano, B.P. de Alexandra, C. César, S.A. Capellini, Screening protocol for early identification of Brazilian children at risk for dyslexia. Front. Psychol. 8, 1–13 (2017). https://doi.org/10.3389/fpsyg.2017.01763

    Article  Google Scholar 

  45. H. Perera, M.F. Shiratuddin, K.W. Wong, A review of electroencephalogram-based analysis and classification frameworks for dyslexia. Lect. Notes Comput. Sci. including Subser. 9950, 626–635 (2016). https://doi.org/10.1007/978-3-319-46681-1_74

    Article  Google Scholar 

  46. N.A.M. Yuzaidey, N.C. Din, M. Ahmad, N. Ibrahim, R.A. Razak, D. Harun, Interventions for children with dyslexia: A review on current intervention methods. Med. J. Malaysia 73(5), 311–320 (2018)

    Google Scholar 

  47. H. Perera, M.F. Shiratuddin, K.W. Wong, Review of EEG-based pattern classification frameworks for dyslexia. Brain Inform. (2018). https://doi.org/10.1186/s40708-018-0079-9

    Article  Google Scholar 

  48. S. Mulakaluri, G.S. Girisha, “Review: Mass Screening framework for children with dyslexia using IOT and computing analysis. EAI Endorsed Trans. Internet Thing. 6(21), 165504 (2020). https://doi.org/10.4108/eai.13-7-2018.165504

    Article  Google Scholar 

  49. D. Aquino-Brítez et al., Optimization of deep architectures for eeg signal classification: An automl approach using evolutionary algorithms. Sensors 21(6), 1–21 (2021). https://doi.org/10.3390/s21062096

    Article  Google Scholar 

  50. S. Güven, N. Friedmann, “Even in predictable orthographies: Surface dyslexia in Turkish. Sci. Stud. Read. 26, 1–25 (2022). https://doi.org/10.1080/10888438.2022.2058399

    Article  Google Scholar 

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Acknowledgements

This work is financially supported by Department of Science & Technology, Government of India, under DST INSPIRE Fellowship Scheme bearing registration Number IF190563.

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  1. Department of Computer Science, University of Kashmir, Srinagar, J&K, India

    Tabassum Gull Jan & Sajad Mohammad Khan

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Jan, T.G., Khan, S.M. A Systematic Review of Research Dimensions Towards Dyslexia Screening Using Machine Learning. J. Inst. Eng. India Ser. B 104, 511–522 (2023). https://doi.org/10.1007/s40031-023-00853-8

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