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A novel approach for detection of dyslexia using convolutional neural network with EOG signals

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Abstract

Dyslexia is a learning disability in acquiring reading skills, even though the individual has the appropriate learning opportunity, adequate education, and appropriate sociocultural environment. Dyslexia negatively affects children’s educational development; hence, early detection is highly important. Electrooculogram (EOG) signals are one of the most frequently used physiological signals in human–computer interfaces applications. EOG is a method based on the examination of the electrical potential of eye movements. The advantages of EOG-based systems are non-invasive, affordable, easy to record, and can be processed in real time. In this paper, a novel 1D CNN approach using EOG signals is proposed for the diagnosis of dyslexia. The proposed approach aims to diagnose dyslexia using EOG signals that are recorded simultaneously during reading texts, which are prepared in different typefaces and fonts. EOG signals were recorded from both horizontal and vertical channels, thus comparing the success of vertical and horizontal EOG signals in detecting dyslexia. The proposed approach provided an effective classification without requiring any hand-crafted feature extraction techniques. The proposed method achieved classifier accuracy of 98.70% and 80.94% for horizontal and vertical channel EOG signals, respectively. The results show that the EOG signals-based approach gives successful results for the diagnosis of dyslexia.

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References

  1. Lyon GR, Shaywitz SE, Shaywitz BA (2003) A definition of dyslexia. Ann Dyslexia 53(1):1–14

    Article  Google Scholar 

  2. American Psychiatric Association (2013) A.P. Association, diagnostic and statistical manual of mental disorders: DSM-5, Washington, DC: American psychiatric association

  3. Shaywitz SE (1998) Dyslexia. N Engl J Med 338(5):307–312. https://doi.org/10.1056/NEJM199801293380507

    Article  CAS  PubMed  Google Scholar 

  4. Pratt HD, Patel DR (2007) Learning disorders in children and adolescents. Pri Care: Clin Office Practice 34(2):361–374. https://doi.org/10.1016/j.pop.2007.04.014

    Article  Google Scholar 

  5. Rayner K (1998) Eye movements in reading and information processing: 20 years of research. Psychol Bull 124(3):372. https://doi.org/10.1037/0033-2909.124.3.372

    Article  CAS  PubMed  Google Scholar 

  6. Parker AJ, Slattery TJ, Kirkby JA (2019) Return-sweep saccades during reading in adults and children. Vision Res 155:35–43. https://doi.org/10.1016/j.visres.2018.12.007

    Article  PubMed  Google Scholar 

  7. Eden GF, Stein JF, Wood HM, Wood FB (1994) Differences in eye movements and reading problems in dyslexic and normal children. Vision Res 34(10):1345–1358. https://doi.org/10.1016/0042-6989(94)90209-7

    Article  CAS  PubMed  Google Scholar 

  8. Hoeft F, Hernandez A, McMillon G, Taylor-Hill H, Martindale JL, Meyler A, Gabrieli JD (2006) Neural basis of dyslexia: a comparison between dyslexic and nondyslexic children equated for reading ability. J Neurosci 26(42):10700–10708. https://doi.org/10.1523/JNEUROSCI.4931-05.2006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Pavlidis GT (1985) Eye movements in dyslexia: their diagnostic significance. J Learn Disabil 18(1):42–50. https://doi.org/10.1177/002221948501800109

    Article  CAS  PubMed  Google Scholar 

  10. Biscaldi M, Fischer B, Aiple F (1994) Saccadic eye movements of dyslexic and normal reading children. Perception 23(1):45–64. https://doi.org/10.1068/p230045

    Article  CAS  PubMed  Google Scholar 

  11. Schleicher R, Galley N, Briest S, Galley L (2008) Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired? Ergonomics 51(7):982–1010. https://doi.org/10.1080/00140130701817062

    Article  CAS  PubMed  Google Scholar 

  12. Morimoto CH, Mimica MR (2005) Eye gaze tracking techniques for interactive applications. Comput Vis Image Underst 98(1):4–24. https://doi.org/10.1016/j.cviu.2004.07.010

    Article  Google Scholar 

  13. Ryu J, Lee M, Kim DH (2019) EOG-based eye tracking protocol using baseline drift removal algorithm for long-term eye movement detection. Expert Syst Appl 131:275–287. https://doi.org/10.1016/j.eswa.2019.04.039

    Article  Google Scholar 

  14. Banerjee A, Datta S, Pal M, Konar A, Tibarewala DN, Janarthanan R (2013) Classifying electrooculogram to detect directional eye movements. Procedia Technol 10:67–75. https://doi.org/10.1016/j.protcy.2013.12.338

    Article  Google Scholar 

  15. Aungsakun SS, Phinyomark A, Phukpattaranont P, Limsakul C (2012) Development of robust electrooculography (EOG)-based human-computer interface controlled by eight-directional eye movements. Int J Phys Sci 7(14):2196–2208. https://doi.org/10.5897/IJPS11.1486

    Article  Google Scholar 

  16. Lee KR, Chang WD, Kim S, Im CH (2016) Real-time “eye-writing” recognition using electrooculogram. IEEE Trans Neural Syst Rehabil Eng 25(1):37–48. https://doi.org/10.1109/TNSRE.2016.2542524

    Article  CAS  Google Scholar 

  17. Hua H, Krishnaswamy P, Rolland JP (2006) Video-based eyetracking methods and algorithms in head-mounted displays. Opt Express 14(10):4328–4350. https://doi.org/10.1364/OE.14.004328

    Article  PubMed  Google Scholar 

  18. Teng G, He Y, Zhao H, Liu D, Xiao J, Ramkumar S (2020) Design and development of human computer interface using electrooculogram with deep learning. Artif Intell Med 102:101765. https://doi.org/10.1016/j.artmed.2019.101765

    Article  PubMed  Google Scholar 

  19. Banerjee A, Pal M, Tibarewala DN, & Konar A (2015). Electrooculogram based blink detection to limit the risk of eye dystonia. In 2015 Eighth Int Conference Adv Pattern Recognition (ICAPR) (1–6). IEEE. https://doi.org/10.1109/ICAPR.2015.7050712

  20. Latifoğlu F, Esas MY, Demirci E (2020) Diagnosis of attention-deficit hyperactivity disorder using EOG signals a new approach. Biomed Eng Biomedizinische Technik 65(2):149–164. https://doi.org/10.1515/bmt-2019-0027

    Article  Google Scholar 

  21. López A, Ferrero F, Postolache O (2019) An affordable method for evaluation of ataxic disorders based on electrooculography. Sensors 19(17):3756. https://doi.org/10.3390/s19173756

    Article  PubMed Central  Google Scholar 

  22. Economou SG, Stefanis CN (1978) Changes of electrooculogram (EOG) in Parkinson’s disease. Acta Neurol Scand 58(1):44–52. https://doi.org/10.1111/j.1600-0404.1978.tb02858.x

    Article  CAS  PubMed  Google Scholar 

  23. Banerjee A, Rakshit A, & Tibarewala DN (2016) Application of electrooculography to estimate word count while reading text. In 2016 Int Conference Syst Med Biol (ICSMB) (174–177). IEEE. https://doi.org/10.1109/ICSMB.2016.7915115

  24. Latifoğlu F, İleri R, Demirci E, Altıntop ÇG (2020) Detection of reading movement from EOG signals. In 2020 IEEE Int Symp Med Meas App (MeMeA) (1–5). IEEE. https://doi.org/10.1109/MeMeA49120.2020.9137290

  25. Latifoğlu F, İleri R, Demirci E (2021) Assessment of dyslexic children with EOG signals: determining retrieving words/re-reading and skipping lines using convolutional neural networks. Chaos, Solitons Fractals 145:110721. https://doi.org/10.1016/j.chaos.2021.110721

    Article  Google Scholar 

  26. Tabkara M, Latifoğlu F, Ceran K, Çavga SH, Yaprak M, Altintop ÇG (2017). Electrooculography based font and punto analysis. In 2017 Medical Technologies National Congress (TIPTEKNO) (1–4). IEEE. https://doi.org/10.1109/TIPTEKNO.2017.8238082

  27. Arel I, Rose DC, Karnowski TP (2010) Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE Comput Intell Mag 5(4):13–18. https://doi.org/10.1109/MCI.2010.938364

    Article  Google Scholar 

  28. Yan LC, Yoshua B, Geoffrey H (2015) Deep learning nature 521(7553):436–444

    Google Scholar 

  29. Bengio Y (2009) Learning deep architectures for AI. Now Publishers Inc.

    Book  Google Scholar 

  30. Ravì D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ (2016) Deep learning for health informatics. IEEE J Biomed Health Inform 21(1):4–21. https://doi.org/10.1109/JBHI.2016.2636665

    Article  PubMed  Google Scholar 

  31. Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Programs Biomed 161:1–13. https://doi.org/10.1016/j.cmpb.2018.04.005

    Article  PubMed  Google Scholar 

  32. Santamaria-Granados L, Munoz-Organero M, Ramirez-Gonzalez G, Abdulhay E, Arunkumar NJIA (2018) Using deep convolutional neural network for emotion detection on a physiological signals dataset (AMIGOS). IEEE Access 7:57–67. https://doi.org/10.1109/ACCESS.2018.2883213

    Article  Google Scholar 

  33. Baloglu UB, Talo M, Yildirim O, San Tan R, Acharya UR (2019) Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recogn Lett 122:23–30. https://doi.org/10.1016/j.patrec.2019.02.016

    Article  Google Scholar 

  34. Ullah I, Hussain M, Aboalsamh H (2018) An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst Appl 107:61–71. https://doi.org/10.1016/j.eswa.2018.04.021

    Article  Google Scholar 

  35. Ortiz A, Munilla J, Gorriz JM, Ramirez J (2016) Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease. Int J Neural Syst 26(07):1650025. https://doi.org/10.1142/S0129065716500258

    Article  PubMed  Google Scholar 

  36. Islam J, Zhang Y (2018) Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain informatics 5(2):1–14. https://doi.org/10.1186/s40708-018-0080-3

    Article  CAS  Google Scholar 

  37. Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014). A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188. arXiv:1404.2188v1

  38. LeCun Y, Bengio Y, Hinton G (2015) Deep learning nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  CAS  PubMed  Google Scholar 

  39. Pinaya WHL, Vieira S, Garcia-Dias R, Mechelli A. (2020). Convolutional neural networks. In Machine learning, Academic Press. (173–191). https://doi.org/10.1016/B978-0-12-815739-8.00010-9

  40. Yıldırım Ö, Pławiak P, Tan RS, Acharya UR (2018) Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med 102:411–420. https://doi.org/10.1016/j.compbiomed.2018.09.009

    Article  PubMed  Google Scholar 

  41. Hammad M, Zhang S, Wang K (2019) A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication. Futur Gener Comput Syst 101:180–196. https://doi.org/10.1016/j.future.2019.06.008

    Article  Google Scholar 

  42. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278. https://doi.org/10.1016/j.compbiomed.2017.09.017

    Article  PubMed  Google Scholar 

  43. Yildirim O, Baloglu UB, Acharya UR (2019) A deep learning model for automated sleep stages classification using PSG signals. Int J Environ Res Public Health 16(4):599. https://doi.org/10.3390/ijerph16040599

    Article  PubMed Central  Google Scholar 

  44. Startsev M, Agtzidis I, Dorr M (2019) 1D CNN with BLSTM for automated classification of fixations, saccades, and smooth pursuits. Behav Res Methods 51(2):556–572. https://doi.org/10.3758/s13428-018-1144-2

    Article  PubMed  Google Scholar 

  45. Rayner K, Fischer MH (1996) Mindless reading revisited: eye movements during reading and scanning are different. Percept Psychophys 58(5):734–747. https://doi.org/10.3758/BF03213106

    Article  CAS  PubMed  Google Scholar 

  46. Schmeisser ET, McDonough JM, Bond M, Hislop PD, Epstein AD (2001) Fractal analysis of eye movements during reading. Optom Vis Sci 78(11):805–814

    Article  CAS  Google Scholar 

  47. Zahia S, Garcia-Zapirain B, Saralegui I, Fernandez-Ruanova B (2020) Dyslexia detection using 3D convolutional neural networks and functional magnetic resonance imaging. Comput Methods Programs Biomed 197:105726

    Article  Google Scholar 

  48. Tamboer P, Vorst HCM, Ghebreab S, Scholte HS (2016) Machine learning and dyslexia classification of individual structural neuro-imaging scans of students with and without dyslexia. NeuroImage Clinical 11:508–514. https://doi.org/10.1016/j.nicl.2016.03.014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  50. Al-Barhamtoshy HM, Motaweh DM (2017). Diagnosis of dyslexia using computation analysis. In 2017 Int Conference Informatics, Health & Technology (ICIHT) (1–7). IEEE. https://doi.org/10.1109/ICIHT.2017.7899141

  51. Andreadis II, Giannakakis GA, Papageorgiou C, & Nikita KS (2009). Detecting complexity abnormalities in dyslexia measuring approximate entropy of electroencephalographic signals. In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (6292–6295). IEEE https://doi.org/10.1109/IEMBS.2009.5332798

  52. Rezvani Z, Zare M, Žarić G, Bonte M, Tijms J, Van der Molen MW, González GF (2019). Machine learning classification of dyslexic children based on EEG local network features. BioRxiv 569996. https://doi.org/10.1101/569996

  53. Rello L, & Ballesteros M (2015). Detecting readers with dyslexia using machine learning with eye tracking measures. In Proceedings of the 12th International Web for All Conference (1–8). https://doi.org/10.1145/2745555.2746644

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Isa IS, Rahimi WNS, Ramlan SA, Sulaiman SN (2019) Automated detection of dyslexia symptom based on handwriting image for primary school children. Procedia Computer Science 163:440–449. https://doi.org/10.1016/j.procs.2019.12.127

    Article  Google Scholar 

  56. İleri R, Latifoğlu F, & Demirci E (2020). New method to diagnosis of dyslexia using 1D-CNN. In 2020 Medical Technologies Congress (TIPTEKNO) (1–4).IEEE. https://doi.org/10.1109/TIPTEKNO50054.2020.9299241

  57. Saboo S, Dhanesha H, Ardeshna R, Sheth S, Kher R (2016) Study and experimentation of electrooculogram signals using Biopac MP36 Acquisition System. https://doi.org/10.13140/RG.2.1.4418.8408

  58. Billones RKC, Bedruz RAR, Arcega ML, Eustaqio GA, Guehring D, Tupaz RP, ... Dadios EP (2018). Digital eye strain and fatigue recognition using electrooculogram signals and ultrasonic distance measurements. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (1–6). IEEE https://doi.org/10.1109/HNICEM.2018.8666298

  59. Chang WD, Cha HS, Im CH (2016) Removing the interdependency between horizontal and vertical eye-movement components in electrooculograms. Sensors 16(2):227. https://doi.org/10.3390/s16020227

    Article  PubMed  PubMed Central  Google Scholar 

  60. İleri R (2020) Determination of the most appropriate writing characters for the education of children with dyslexia by using EOG signals. Erciyes University, Kayseri, Turkey, Biomedical Engineering Department, p 121

    Google Scholar 

  61. Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377. https://doi.org/10.1016/j.patcog.2017.10.013

    Article  Google Scholar 

  62. O'Shea K, & Nash R (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.

  63. Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48. https://doi.org/10.1016/j.neucom.2015.09.116

    Article  Google Scholar 

  64. Zhu W, Zeng N, Wang N (2010) Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG Proc: Health Care and Life Sci Baltimore, Maryland 19:67

    CAS  Google Scholar 

  65. Postolache O, Girão PS, Postolache G. (2013). Pervasive sensing and m-health: vital signs and daily activity monitoring. In Pervasive and mobile sensing and computing for healthcare (1–49). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32538-0_1

  66. Płoński P, Gradkowski W, Altarelli I, Monzalvo K, van Ermingen-Marbach M, Grande M, Jednoróg K (2017) Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia. Hum Brain Mapp 38(2):900–908. https://doi.org/10.1002/hbm.23426

    Article  PubMed  Google Scholar 

  67. Beaton A (2004) Dyslexia, reading and the brain: a sourcebook of psychological and biological research

    Book  Google Scholar 

  68. Mohamad NB, Lee KY, Mansor W, Mahmoodin Z, Fadzal CCW, Mohamad S, Amirin S. (2014). Spectral analysis based brain imaging of normal, poor dyslexic, and capable dyslexic children in reading, writing and spelling task. In 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES) (653–656). IEEE. https://doi.org/10.1109/IECBES.2014.7047585

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Funding

This study has been supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK) (Grant Number 119E055).

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  1. Department of Biomedical Engineering, Engineering Faculty, Erciyes University, 39039, Kayseri, Turkey

    Ramis Ileri & Fatma Latifoğlu

  2. Department of Child and Adolescent Psychiatry, Erciyes University School of Medicine, Kayseri, Turkey

    Esra Demirci

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  1. Ramis Ileri
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Ileri, R., Latifoğlu, F. & Demirci, E. A novel approach for detection of dyslexia using convolutional neural network with EOG signals. Med Biol Eng Comput 60, 3041–3055 (2022). https://doi.org/10.1007/s11517-022-02656-3

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