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A method for online pattern recognition of abnormal eye movements

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Abstract

A doctor could say that a patient is sick while he/she is healthy or could say that the patient is healthy while he/she is sick, by mistake. So it is important to generate a system that can give a good diagnosis, in this case for abnormal eye movements. An abnormal eye movement is when the patient wants to move the eye to up or down and the eye does not move or the eye moves to other place. In this paper, a method for the pattern recognition is used to provide a better diagnosis for patients related with the abnormal eye movements. The real data of signals of two eye movements (up and down) of patients are obtained using a mindset ms-100 system. A new method that uses one intelligent algorithm for online pattern recognition is proposed. The difference between the proposed method and the previous works is that, in other works, both behaviors (up and down) are trained with one intelligent algorithm, while in this work, up behavior is trained with one intelligent algorithm and down behavior is trained with other intelligent algorithm; it is because the multi-output system can always be decomposed into a collection of single-output systems with the advantage to use different parameters for each one if necessary. The intelligent algorithm used by the proposed method could be any of the following: the adaline network denoted as AN, the multilayer neural network denoted as NN, or the Sugeno fuzzy inference system denoted as SF. So the comparison results of the proposed method using each of the intelligent algorithms for online pattern recognition of two eye movements are presented.

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References

  1. Arabacı H, Bilgin O (2010) Automatic detection and classification of rotor cage faults in squirrel cage induction motor. Neural Comput Appl 19:713–723

    Article  Google Scholar 

  2. Bouchachia A (2010) An evolving classification cascade with self-learning. Evol Syst 1(3):143–160

    Article  Google Scholar 

  3. Cong W, Hill DJ (1997) Deterministic learning and rapid dynamic pattern recognition. IEEE Trans Neural Netw 18(3):617–630

    Google Scholar 

  4. Chu JU, Moon I, Mun MS (2007) A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand. IEEE/ASME Trans Mechatron 12(3):282–290

    Article  Google Scholar 

  5. Chu JU, Moon I, Lee YJ, Kim SK, Mun MS (2006) A feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand a real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand. IEEE Trans Biomed Eng 53(11):2232–2239

    Article  Google Scholar 

  6. Daneshyari M (2010) Chaotic neural network controlled by particle swarm with decaying chaotic inertia weight for pattern recognition. Neural Comput Appl 19:637–645

    Article  Google Scholar 

  7. Duda RO, Hart PE, Stork DG (2001) Pattern classification, ISBN: 0-471-05669-3. Wiley, New York

    Google Scholar 

  8. Duer S (2010) Diagnostic system for the diagnosis of a reparable technical object, with the use of an artificial neural network of RBF type. Neural Comput Appl 19:691–700

    Article  Google Scholar 

  9. Ertel D, Pflederer T, Achenbach S, Kalendera WA (2009) Real-time determination of the optimal reconstruction phase to control ECG pulsing in spiral cardiac CT. Phys Med 25(3):122–127

    Article  Google Scholar 

  10. Fukumi M, Omatu S, Takeda F, Kosaka T (1992) Rotation-invariant neural patern recognition system with application to coin recognition. IEEE Trans Neural Netw 3(2):272–279

    Article  Google Scholar 

  11. Fukumi M, Omatu S, Nishikawa Y (1997) Rotation-invariant neural patern recognition system estimating a rotating angle. IEEE Trans Neural Netw 8(3):568–581

    Article  Google Scholar 

  12. Gurgen F, Guler N, Varol F (2000) Antenatal fetal risk assessment by blood-flow velocity waveforms. IEEE Eng Med Biol 19(6):88–93

    Google Scholar 

  13. Hardalac F, Barisci N, Ergun U (2004) Classification of aorta insufficiency and stenosis using mlp neural network and neuro-fuzzy system. Physica Med XX(4):155–166

    Google Scholar 

  14. Haykin S (1994) Neural networks—a comprehensive foundation. Macmillan, New York

    MATH  Google Scholar 

  15. Hilera JR, Martínez VJ (1995) Redes neuronales artificiales, fundamentos, modelos y aplicaciones. Adison Wesley Iberoamericana, USA. ISBN: 0-201-87895-X

  16. Hisada M, Ozawa S, Zhang K, Kasabov N (2010) Incremental linear discriminant analysis for evolving feature spaces in multitask pattern recognition problems. Evol Syst 1(1):17–28

    Article  Google Scholar 

  17. Hori J, Sakano K, Saitoh Y (2006) Development of a communication support device controlled by eye movements and voluntary eye blink. IEICE Trans Inf Syst E89-D(6):1790–1797

  18. Iglesias JA, Angelov P, Ledezma A, Sanchis A (2010) An evolving classification of agents behaviors: a general approach. Evol Syst 1(3):161–172

    Article  Google Scholar 

  19. Imai K, Ikeda M, Enchi Y, Niimi T (2010) A detection method for streak artifacts and radiological noise in a non-uniform region in a CT image. Phys Med 26(3):157–165

    Article  Google Scholar 

  20. Jain AK, Duin RPW (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37

    Article  Google Scholar 

  21. Jang JSR, Sun CT (1996) Neuro-fuzzy and soft computing. Prentice Hall, NJ

    Google Scholar 

  22. Joshi A, Ramakrisman N, Houstis EN, Rice JR (1997) On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques. IEEE Trans Neural Netw 8(1):18–31

    Article  Google Scholar 

  23. Kamruzzaman J, Begg RK (2006) Support vector machines and other pattern recognition approaches to the diagnosis of cerebral palsy gait. IEEE Trans Biol Eng 53(12):2479–2490

    Google Scholar 

  24. Keshtkar A, Mesbahi A, Rastaa SH, Keshtkar A (2010) The feasibility of computational modelling technique to detect the bladder cancer. Phys Med 26(1):34–37

    Article  Google Scholar 

  25. Kisielewicz K, Truszkiewicz A, Wach S, Wasilewska–Radwańska M (2010) Evaluation of dose area product vs. patient dose in diagnostic X-ray units. Phys Med 27(2):117–120

    Google Scholar 

  26. Laubach M (2003) Verifaying neuronal codes: the statistical pattern recognition approach. In: Proceedings of the 25th annual international conference IEEE EMBS, pp 2147–2150

  27. Lemos A, Caminhas W, Gomide F (2011) Fuzzy evolving linear regression trees. Evol Syst 2(1):1–15

    Article  Google Scholar 

  28. Liu C (2006) Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE Trans Pattern Anal Mach Intell 28(5):725–737

    Article  Google Scholar 

  29. Malathi V, Marimuthu NS, Baskar S (2010) A comprehensive evaluation of multicategory classification methods for fault classification in series compensated transmission line. Neural Comput Appl 19:595–600

    Article  Google Scholar 

  30. Owners manual for the MINDSET, Nolan computer systems, 1997–2000

  31. Nguwi Y, Cho S (2010) Emergent self-organizing feature map for recognizing road sign images. Neural Comput Appl 19:601–615

    Article  Google Scholar 

  32. Pedrycz W (2010) Evolvable fuzzy systems: some insights and challenges. Evol Syst 1(2):73–82

    Article  MathSciNet  Google Scholar 

  33. Peralta JO, de Paralta MTC (2002) Security PIDS with physical sensors, real time pattern recognition, and continuous patrol. IEEE Trans Syst Man Cybern Part C Appl Rev 32(4):340–346

    Article  Google Scholar 

  34. Prabhakar R, Laviraj MA, Haresh KP, Julka PK, Rath GK (2010) Impact of patient setup error in the treatment of head and neck cancer with intensity modulated radiation therapy. Phys Med 26(1):26–33

    Article  Google Scholar 

  35. Rubio JJ (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309

    Article  MathSciNet  Google Scholar 

  36. Rubio JJ, Vázquez DM, Pacheco J (2010) Backpropagation to train an evolving radial basis function neural network. Evol Syst 1(3):173–180

    Article  Google Scholar 

  37. Rubio JJ, Angelov P, Pacheco J (2011) Uniformly stable backpropagation algorithm to train a feedforward neural network. IEEE Trans Neural Netw 22(3):356–366

    Article  Google Scholar 

  38. Seker H, Evans H, Aydin N, Yazgan E (2001) Compensatory fuzzy neural networks-based intelligent detection doppler ultrasound waveforms. IEEE Trans Inf Technol Biomed 5(3):187–194

    Google Scholar 

  39. Song X, Wang P (2011) Flexible array self-organizing map neural network and its application in complex pattern recognition. Neural Comput Appl 20:9–16

    Article  MathSciNet  Google Scholar 

  40. Sun N, Ji Z, Zou C, Zhao L (2010) Two-dimensional canonical correlation analysis and its application in small sample size face recognition. Neural Comput Appl 19:377–382

    Article  Google Scholar 

  41. Tsimboukakis N, Tambouratzis G (2010) A comparative study on authorship attribution classification tasks using both neural network and statistical methods. Neural Comput Appl 19:573–582

    Article  Google Scholar 

  42. Wang LX (1997) A course in fuzzy systems and control. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  43. Yamagishi K, Hori J, Miyakama M (2006) Development of EOG-based communication system controlled by eight-directional eye movements. In: Proceedings of the 28th IEEE EMBS annual international conference, pp 2574–2577

  44. Zannoli R, Corazza I, Branzi A (2009) Mechanical simulator of the cardiovascular system. Phys Med 25(2):94–100

    Article  Google Scholar 

  45. Zeinali A, Hashemi B, Akhlaghpoor S (2010) Noninvasive prediction of vertebral body compressive strength using nonlinear finite element method and an image based technique. Phys Med 26(2):88–97

    Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the editor and to the reviewers for their valuable comments and insightful suggestions, which help to improve this research significantly. The authors thank the Secretaria de Investigacion y Posgrado and the Comision de Operacion y Fomento de Actividades Academicas del IPN and the Consejo Nacional de Ciencia y Tecnologia for their help in this research.

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Authors and Affiliations

  1. Sección de Estudios de Posgrado e Investigación, ESIME Azcapotzalco, Instituto Politécnico Nacional, Av. de las Granjas no. 682, Col. Santa Catarina, 02250, Mexico, D.F., Mexico

    José de Jesús Rubio

  2. Ingeniería en Comunicaciones y Electrónica, ESIME Zacatenco, Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional s/n, Col. Lindavista, 07738, Mexico, D.F., Mexico

    Floriberto Ortiz-Rodriguez, Carlos R. Mariaca-Gaspar & Julio C. Tovar

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  1. José de Jesús Rubio
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  2. Floriberto Ortiz-Rodriguez
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  3. Carlos R. Mariaca-Gaspar
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  4. Julio C. Tovar
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Correspondence to José de Jesús Rubio.

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de Jesús Rubio, J., Ortiz-Rodriguez, F., Mariaca-Gaspar, C.R. et al. A method for online pattern recognition of abnormal eye movements. Neural Comput & Applic 22, 597–605 (2013). https://doi.org/10.1007/s00521-011-0705-4

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  • DOI: https://doi.org/10.1007/s00521-011-0705-4

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