Research Article
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Year 2023, Volume: 6 Issue: 1, 35 - 43, 31.05.2023
https://doi.org/10.34088/kojose.1126113

Abstract

References

  • [1] Channa A., Baqai A., Ceylan R., 2019. Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning. (IJACSA) International Journal of Advanced Computer Science and Applications, 10(6).
  • [2] Li D., Xie Q., Jin Q., Hirasawa K., 2016. A Sequential Method using Multiplicative Extreme Learning Machine for Epileptic Seizure Detection. Neurocomputing, 214, pp. 692-707.
  • [3] Yıldırım M. and Yıldız A. 2017. Farklı zaman ölçekli EEG işaretlerinden epilepsi nöbetinin otomatik tespiti. Dicle University Journal of Engineering, 8(4), pp. 745-757.
  • [4] Banaie M., Pooyan M., Mikaili M., 2011. Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes. Expert Systems with Applications, 38(6), pp. 7359-7363.
  • [5] Fahn S. 2003. Description of Parkinson's disease as a clinical syndrome. Annals of the New York Academy of Sciences, 991(1), pp. 1-14.
  • [6] Sveinbjornsdottir S. 2016. The clinical symptoms of Parkinson's disease. Journal of neurochemistry, 139, pp. 318-324.
  • [7] Jane Y. N., Nehemiah H. K., Arputharaj K. 2016. A Q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson’s disease. Journal of biomedical informatics, 60, pp. 169-176.
  • [8] Medeiros L., Almeida H., Dias L., Perkusich M., Fischer R. 2016. A gait analysis approach to track Parkinson's disease evolution using principal component analysis. In 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), pp. 48-53. June.
  • [9] Baby M. S., Saji A. J., Kumar C. S. 2017. Parkinsons disease classification using wavelet transform-based feature extraction of gait data. In 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1-6, April.
  • [10] Lee S. H., Lim J. S. 2012. Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction. Expert Systems with Applications, 39(8), pp. 7338-7344.
  • [11] Lim C. M., Ng H., Yap T. T. V., Ho C. C. 2015. Gait analysis and classification on subjects with Parkinson’s disease. Jurnal Teknologi, 77(18).
  • [12] Perumal S. V., Sankar R. 2016. Gait and tremor assessment for patients with Parkinson’s disease using wearable sensors. Ict Express, 2(4), pp. 168-174.
  • [13] Abdulhay E., Arunkumar N., Narasimhan K., Vellaiappan E., Venkatraman V. 2018. Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease. Future Generation Computer Systems, 83, pp. 366-373.
  • [14] Andrei A. G., Tăuțan A. M., Ionescu B. 2019. Parkinson’s disease detection from gait patterns. In 2019 E-Health and Bioengineering Conference (EHB), pp. 1-4, November.
  • [15] Özel E., Tekin R., Kaya Y., 2021. Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease. Traitement du Signal, 38(3).
  • [16] Ertuğrul Ö. F., Kaya Y., Tekin, R., Almalı M. N., 2016. Detection of Parkinson's disease by shifted one dimensional local binary patterns from gait. Expert Systems with Applications, 56, pp. 156-163.
  • [17] Hoang N. S., Cai Y., Lee C. W., Yang Y. O., Chui C. K., Chua M. C. H., 2019. Gait classification for Parkinson's Disease using Stacked 2D and 1D Convolutional Neural Network. In 2019 International Conference on Advanced Technologies for Communications (ATC), pp. 44-49, October.
  • [18] Johri A., Tripathi A., 2019. Parkinson disease detection using deep neural networks. In 2019 Twelfth International Conference on Contemporary Computing (IC3), pp. 1-4, August.
  • [19] Goldberger A., Amaral L., Glass L., Hausdorff J., Ivanov P. C., Mark R., Stanley H. E. 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online], 101 (23), pp. e215–e220.
  • [20] Frenkel-Toledo S., Giladi N., Peretz C., Herman T., Gruendlinger L., Hausdorff J. M., 2005. Effect of gait speed on gait rhythmicity in Parkinson's disease: variability of stride time and swing time respond differently. Journal of neuroengineering and rehabilitation, 2(1), pp. 1-7.
  • [21] Frenkel‐Toledo S., Giladi N., Peretz C., Herman T., Gruendlinger L., Hausdorff J. M., 2005. Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson's disease. Movement disorders: official journal of the Movement Disorder Society, 20(9), pp. 1109-1114.
  • [22] Hausdorff J. M., Lowenthal J., Herman T., Gruendlinger L., Peretz C., Giladi N., 2007. Rhythmic auditory stimulation modulates gait variability in Parkinson's disease. European Journal of Neuroscience, 26(8), pp. 2369-2375.
  • [23] Yogev G., Giladi N., Peretz C., Springer S., Simon E. S., Hausdorff J. M., 2005. Dual tasking, gait rhythmicity, and Parkinson's disease: which aspects of gait are attention demanding? European journal of neuroscience, 22(5), pp. 1248-1256.
  • [24] Gümüşçü A., 2019. Giyilebilir Yürüyüş Analiz Sensörü ile Kişi Sınıflandırmasının Öznitelik Seçme Algoritmaları ile İyileştirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 31(2), pp. 463-471.

A Low-Dimensional Feature Vector Representation for Gait-based Parkinson’s Disease Detection

Year 2023, Volume: 6 Issue: 1, 35 - 43, 31.05.2023
https://doi.org/10.34088/kojose.1126113

Abstract

Thanks to the developing technology, Parkinson's disease can be detected by using datasets which are obtained from different sources. Gait activity analysis is one of the methods used to detect Parkinson’s disease. The gait activity of Parkinson's disease differs from the gait of a normal person. In this study, a support vector machine-based classification method using low-dimensional feature vector representation is proposed to detect Parkinson's disease. Pressure sensors placed under the foot are divided into 3 categories, placed on the heel of the foot, the center of the foot, and the toe. Average stance duration, average stride duration, and average distance are extracted from the heel of the foot and toe. The frequency value obtained from the center of the foot during the walking period is used. Only 4 feature values having O(n) time complexity are used for the classification process. Experimental results point out that the proposed method can compete with similar studies proposed in the literature, even under these few features. According to the experimental results, high classification performance, up to 85%, is obtained under the whole dataset. Moreover, superior classification performance, up to 91%, is obtained when the datasets are evaluated individually.

References

  • [1] Channa A., Baqai A., Ceylan R., 2019. Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning. (IJACSA) International Journal of Advanced Computer Science and Applications, 10(6).
  • [2] Li D., Xie Q., Jin Q., Hirasawa K., 2016. A Sequential Method using Multiplicative Extreme Learning Machine for Epileptic Seizure Detection. Neurocomputing, 214, pp. 692-707.
  • [3] Yıldırım M. and Yıldız A. 2017. Farklı zaman ölçekli EEG işaretlerinden epilepsi nöbetinin otomatik tespiti. Dicle University Journal of Engineering, 8(4), pp. 745-757.
  • [4] Banaie M., Pooyan M., Mikaili M., 2011. Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes. Expert Systems with Applications, 38(6), pp. 7359-7363.
  • [5] Fahn S. 2003. Description of Parkinson's disease as a clinical syndrome. Annals of the New York Academy of Sciences, 991(1), pp. 1-14.
  • [6] Sveinbjornsdottir S. 2016. The clinical symptoms of Parkinson's disease. Journal of neurochemistry, 139, pp. 318-324.
  • [7] Jane Y. N., Nehemiah H. K., Arputharaj K. 2016. A Q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson’s disease. Journal of biomedical informatics, 60, pp. 169-176.
  • [8] Medeiros L., Almeida H., Dias L., Perkusich M., Fischer R. 2016. A gait analysis approach to track Parkinson's disease evolution using principal component analysis. In 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), pp. 48-53. June.
  • [9] Baby M. S., Saji A. J., Kumar C. S. 2017. Parkinsons disease classification using wavelet transform-based feature extraction of gait data. In 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1-6, April.
  • [10] Lee S. H., Lim J. S. 2012. Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction. Expert Systems with Applications, 39(8), pp. 7338-7344.
  • [11] Lim C. M., Ng H., Yap T. T. V., Ho C. C. 2015. Gait analysis and classification on subjects with Parkinson’s disease. Jurnal Teknologi, 77(18).
  • [12] Perumal S. V., Sankar R. 2016. Gait and tremor assessment for patients with Parkinson’s disease using wearable sensors. Ict Express, 2(4), pp. 168-174.
  • [13] Abdulhay E., Arunkumar N., Narasimhan K., Vellaiappan E., Venkatraman V. 2018. Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease. Future Generation Computer Systems, 83, pp. 366-373.
  • [14] Andrei A. G., Tăuțan A. M., Ionescu B. 2019. Parkinson’s disease detection from gait patterns. In 2019 E-Health and Bioengineering Conference (EHB), pp. 1-4, November.
  • [15] Özel E., Tekin R., Kaya Y., 2021. Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease. Traitement du Signal, 38(3).
  • [16] Ertuğrul Ö. F., Kaya Y., Tekin, R., Almalı M. N., 2016. Detection of Parkinson's disease by shifted one dimensional local binary patterns from gait. Expert Systems with Applications, 56, pp. 156-163.
  • [17] Hoang N. S., Cai Y., Lee C. W., Yang Y. O., Chui C. K., Chua M. C. H., 2019. Gait classification for Parkinson's Disease using Stacked 2D and 1D Convolutional Neural Network. In 2019 International Conference on Advanced Technologies for Communications (ATC), pp. 44-49, October.
  • [18] Johri A., Tripathi A., 2019. Parkinson disease detection using deep neural networks. In 2019 Twelfth International Conference on Contemporary Computing (IC3), pp. 1-4, August.
  • [19] Goldberger A., Amaral L., Glass L., Hausdorff J., Ivanov P. C., Mark R., Stanley H. E. 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online], 101 (23), pp. e215–e220.
  • [20] Frenkel-Toledo S., Giladi N., Peretz C., Herman T., Gruendlinger L., Hausdorff J. M., 2005. Effect of gait speed on gait rhythmicity in Parkinson's disease: variability of stride time and swing time respond differently. Journal of neuroengineering and rehabilitation, 2(1), pp. 1-7.
  • [21] Frenkel‐Toledo S., Giladi N., Peretz C., Herman T., Gruendlinger L., Hausdorff J. M., 2005. Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson's disease. Movement disorders: official journal of the Movement Disorder Society, 20(9), pp. 1109-1114.
  • [22] Hausdorff J. M., Lowenthal J., Herman T., Gruendlinger L., Peretz C., Giladi N., 2007. Rhythmic auditory stimulation modulates gait variability in Parkinson's disease. European Journal of Neuroscience, 26(8), pp. 2369-2375.
  • [23] Yogev G., Giladi N., Peretz C., Springer S., Simon E. S., Hausdorff J. M., 2005. Dual tasking, gait rhythmicity, and Parkinson's disease: which aspects of gait are attention demanding? European journal of neuroscience, 22(5), pp. 1248-1256.
  • [24] Gümüşçü A., 2019. Giyilebilir Yürüyüş Analiz Sensörü ile Kişi Sınıflandırmasının Öznitelik Seçme Algoritmaları ile İyileştirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 31(2), pp. 463-471.
There are 24 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Articles
Authors

Emin Ölmez 0000-0002-7544-770X

Orhan Akbulut 0000-0003-0096-0688

Ahmet Sertbaş 0000-0001-8166-1211

Early Pub Date May 31, 2023
Publication Date May 31, 2023
Acceptance Date October 4, 2022
Published in Issue Year 2023 Volume: 6 Issue: 1

Cite

APA Ölmez, E., Akbulut, O., & Sertbaş, A. (2023). A Low-Dimensional Feature Vector Representation for Gait-based Parkinson’s Disease Detection. Kocaeli Journal of Science and Engineering, 6(1), 35-43. https://doi.org/10.34088/kojose.1126113