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Multi-class Model MOV-OVR for Automatic Evaluation of Tremor Disorders in Huntington’s Disease

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Information and Communication Technology and Applications (ICTA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1350))

Abstract

The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. This article proposes a method for assessing the symptoms of tremor in patients at an early stage of Huntington’s disease (Huntington’s syndrome, Huntington’s chorea, HD). This approach includes the development of a data collection methodology using smartphones or tablets, data labelling for Support vector machine (SVM) model, multiple-class classification strategy, training the SVM, automatic selection of model parameters, and selection of training and test data sets. More than 3000 data records were obtained during research from subjects and patients with HD in Lithuania. The proposed SVM model achieved an accuracy of 97.09% in relation to 14 different classes, which were built according to the Shoulson-Fahn Total Functional Capacity (TFC) scale for assessing the patient’s tremor condition.

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References

  1. Nguyen, H.H.P., Angela Cenci, M. (eds.): Behavioral Neurobiology of Huntington’s Disease and Parkinson’s Disease. CTBN, vol. 22. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46344-4

    Book  Google Scholar 

  2. Cambray, S., Cattaneo, E.: Huntington’s disease: how could stem cells help? EuroStemCell project from European Union’s Horizon 2020 research and innovation program. Stem Cells fact sheets. https://www.eurostemcell.org/stem-cell-factsheets. Accessed 28 Oct 2020

  3. Roos, R.A.C.: Huntington’s disease: a clinical review. Orphanet J. Rare Dis. 5, 40 (2010)

    Article  Google Scholar 

  4. Coordination center of Huntington in Lithuania at Vilnius Hospital of Santara clinic. https://www.santa.lt/index.php?option=com_content&view=article&id=2041&catid=178&Itemid=129. Accessed 28 Oct 2020

  5. Jones, C., et al.: The societal cost of Huntington’s disease: are we underestimating the burden? Eur. J. Neurol. 23, 1588–1590 (2016)

    Article  Google Scholar 

  6. Wiecki, T.V., et al.: A computational cognitive biomarker for early-stage Huntington’s disease. PLoS ONE 11(2), e0148409 (2016)

    Article  Google Scholar 

  7. Barnat, M., et al.: Huntington’s disease alters human neurodevelopment. Science 369(6505), 787–793 (2020)

    Article  Google Scholar 

  8. DiFiglia, M.: An early start to huntington’s disease. Science 369(6505), 771–772 (2020)

    Article  Google Scholar 

  9. Engin, M., et al.: The classification of human tremor signals using artificial neural network. Expert Syst. Appl. 33(3), 754–761 (2007)

    Article  Google Scholar 

  10. Bourouis, A., Feham, M., Hossain, M.A., Zhang, L.: An intelligent mobile based decision support system for retinal disease diagnosis. Decis. Support Syst. 59, 341–350 (2014)

    Article  Google Scholar 

  11. Papaioannou, V., Economou, G.P.K., Tsakalidis, A.: A robust smart device app assisting medical diagnosis. Artif. Intell. Res. 5(2), 82 (2016)

    Article  Google Scholar 

  12. Wu, D., et al.: Prediction of Parkinson’s disease tremor onset using a radial basis function neural network based on particle swarm optimization. Int. J. Neural Syst. 20(02), 109–116 (2010)

    Article  Google Scholar 

  13. Połap, D., Woźniak, M., Damaševičius, R., Maskeliūnas, R.: Bio-inspired voice evaluation mechanism. Appl. Soft Comput. J. 80, 342–357 (2019)

    Article  Google Scholar 

  14. Almeida, J.S., et al.: Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques. Pattern Recogn. Lett. 125, 55–62 (2019)

    Article  Google Scholar 

  15. Azad, C., Jain, S., Jha, V.K.: Design and analysis of data mining based prediction model for Parkinsons disease. Issues 1(1), 181–189 (2014)

    Google Scholar 

  16. Jutel, A., Lupton, D.: Digitizing diagnosis: a review of mobile applications in the diagnostic process. Diagnosis 2(2), 89–96 (2015)

    Article  Google Scholar 

  17. Kailas, A., Chong, C.C., Watanabe, F.: From mobile phones to personal wellness dashboards. IEEE Pulse 1(1), 57–63 (2010)

    Article  Google Scholar 

  18. Manini, A., Trojaniello, D., Cereatti, A., Sabatini, A.M.: A machine learning framework for gait classification using inertial sensors: application to elderly, post-stroke and Huntington’s disease patients. Sensors 16, 134 (2016)

    Article  Google Scholar 

  19. Bennasar, M., et al.: Huntington’s disease assessment using tri axis accelerometers. Procedia Comput. Sci. 96, 1193–1201 (2016)

    Article  Google Scholar 

  20. Rajeswari, N., Chandrakala, S.: Generative model-driven feature learning for dysarthric speech recognition. Biocybernetics Biomed. Eng. 36(4), 553–561 (2016)

    Article  Google Scholar 

  21. Martins, M., Santos, C., Costa, L., Frizera, A.: Feature reduction and multi-classification of different assistive devices according to the gait pattern. Disabil. Rehabil. Assistive Technol. 11(3), 202–218 (2016)

    Article  Google Scholar 

  22. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J., Hervás, R., Rodríguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 216–223. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35395-6_30

    Chapter  Google Scholar 

  23. Shoulson, I., Fahn, S.: Huntington disease: clinical care and evaluation. Neurology 29, 1–3 (1979)

    Article  Google Scholar 

  24. Gelšvartas, J., Simutis, R., Maskeliūnas, R.: User adaptive text predictor for mentally disabled Huntington’s patients. Comput. Intell. Neurosci. 2016, 1–6 (2016)

    Article  Google Scholar 

  25. Gelšvartas, J., Lauraitis, A., Maskeliūnas, R., Simutis, R.: Review of assistive technologies for disabled people. In: Proceedings of International Conference Biomedical Engineering, pp. 142–146 (2016)

    Google Scholar 

  26. Lauraitis, A., Maskeliūnas, R.: Investigation of predicting functional capacity level for huntington disease patients. In: Damaševičius, R., Mikašytė, V. (eds.) ICIST 2017. CCIS, vol. 756, pp. 142–149. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67642-5_12

    Chapter  Google Scholar 

  27. Lauraitis, A., Maskeliūnas, R., Damaševičius, R.: ANN and fuzzy logic based model to evaluate huntington disease symptoms. J. Healthc. Eng. 2018, 4581272 (2018)

    Article  Google Scholar 

  28. Lauraitis, A., Maskeliunas, R., Damasevicius, R., Polap, D., Wozniak, M.: A smartphone application for automated decision support in cognitive task based evaluation of central nervous system motor disorders. IEEE J. Biomed. Health Inf. 23(5), 1865–1876 (2019)

    Article  Google Scholar 

  29. Lauraitis, A., Maskeliunas, R., Damaševičius, R., Krilavičius, T.: Detection of speech impairments using cepstrum, auditory spectrogram and wavelet time scattering domain features. IEEE Access 8, 96162–96172 (2020)

    Article  Google Scholar 

  30. Lauraitis, A., Maskeliūnas, R., Damaševičius, R., Krilavičius, T.: A mobile application for smart computer-aided self-administered testing of cognition, speech, and motor impairment. Sensors 20(11), 3236 (2020)

    Article  Google Scholar 

  31. Da Silva, F.N., et al.: More than just tapping: index finger-tapping measures procedural learning in schizophrenia. Schizophr. Res. 137(1–3), 234–240 (2012)

    Article  Google Scholar 

  32. Barut, Ç., Kızıltan, E., Gelir, E., Köktürk, F.: Advanced analysis of finger-tapping performance: a preliminary study. Balkan Med. J. 30(2), 167 (2013)

    Google Scholar 

  33. Zhang, L., et al.: A study of tapping by the unaffected finger of patients presenting with central and peripheral nerve damage. Front. Hum. Neurosci. 9, 260 (2015)

    Google Scholar 

  34. Zham, P., Kumar, D.K., Dabnichki, P., Arjunan, S.P., Raghav, S.: Distinguishing different stages of Parkinson’s disease using composite index of speed and pen-pressure of sketching a spiral. Front. Neurol. 8, 435 (2017)

    Article  Google Scholar 

  35. Zham, P., Arjunan, S.P., Raghav, S., Kumar, D.K.: Efficacy of guided spiral drawing in the classification of Parkinson’s disease. IEEE J. Biomed. Health Inf. 22(5), 1648–1652 (2017)

    Article  Google Scholar 

  36. Memedi, M., et al.: Automatic spiral analysis for objective assessment of motor symptoms in Parkinson’s Disease. Sensors 15(9), 23727–23744 (2015)

    Article  Google Scholar 

  37. Sadikov, A., et al.: Parkinson check smart phone app. In: Frontiers in Artificial Intelligence and Applications, pp. 1213–1214 (2014)

    Google Scholar 

  38. Kendall, D.G.: A survey of the statistical theory of shape. Stat. Sci. 4(2), 87–99 (1989)

    Article  MathSciNet  Google Scholar 

  39. Qin Y., Li, D., Zhang, A.: A new SVM multiclass incremental learning algorithm. Math. Probl. Eng. 2015, Article ID 745815 (2015)

    Google Scholar 

  40. Gunn, S.R.: Support vector machines for classification and regression. Technical report, University of Southampton (1998)

    Google Scholar 

  41. Xu, J.: An extended one-versus-rest support vector machine for multi-label classification. Neurocomputing 74(17), 3114–3124 (2011)

    Article  Google Scholar 

  42. Sun, Z., Guo, Z., Wang, X., Liu, J., Liu, S.: Fast extended one-versus-rest multi-label SVM classification algorithm based on approximate extreme points. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 265–278. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55753-3_17

    Chapter  Google Scholar 

  43. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27 (2011)

    Article  Google Scholar 

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Acknowledgement

We acknowledge the helpful and kind support from Prof. Andrew Lawrinson (Valley University Silica Real Time Generated Research Center). We also acknowledge Prof. Piotr Ivanovich Sosnin (Ulyanovsk State University) for his visionary comments, and Prof. Alex. Iwanow (Bulgarian-Silesian Joint Academy of Sciences) for innovative Pareto optimization idea.

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Authors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Rytis Maskeliunas & Andrius Lauraitis

  2. Vytautas Magnus University, Kaunas, Lithuania

    Robertas Damasevicius

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

Authors
  1. Rytis Maskeliunas
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  2. Andrius Lauraitis
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  3. Robertas Damasevicius
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  4. Sanjay Misra
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Corresponding author

Correspondence to Robertas Damasevicius .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. Federal University of Technology Minna, Minna, Nigeria

    Bilkisu Muhammad-Bello

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Maskeliunas, R., Lauraitis, A., Damasevicius, R., Misra, S. (2021). Multi-class Model MOV-OVR for Automatic Evaluation of Tremor Disorders in Huntington’s Disease. In: Misra, S., Muhammad-Bello, B. (eds) Information and Communication Technology and Applications. ICTA 2020. Communications in Computer and Information Science, vol 1350. Springer, Cham. https://doi.org/10.1007/978-3-030-69143-1_1

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  • DOI: https://doi.org/10.1007/978-3-030-69143-1_1

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  • Print ISBN: 978-3-030-69142-4

  • Online ISBN: 978-3-030-69143-1

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