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Detection of Parkinson's disease according to age factor using Double Density 1-D Wavelet Transform

Year 2019, Issue: 17, 881 - 887, 31.12.2019
https://doi.org/10.31590/ejosat.649480

Abstract

Parkinson's is a neurological
nervous system disorder that affects movement. A disease that is closely
related to nerve cells such as Parkinson's is likely to be detected using gait
data. Nowadays, symptoms of many diseases start to appear at a very early age.
For this reason, analysis studies performed according to age factor for all
diseases have gained importance. Therefore, in this study, it was aimed to
analyze the data obtained from the subjects according to the age factor by
using Double Density 1-D Wavelet Transform (DD1DWT) and to detect Parkinson's
disease (PD) with high accuracy. The data set consists of gait data from 15
subjects, young, adult and adult patients. Firstly, DD1DWT method was applied
to data in three levels and, the approximation (CA) and detail (CD)
coefficients were obtained. Afterwards, 10 features were extracted from the
last level CA data obtained according to age factor. The extracted features were
given to 4 different decision mechanisms in the form of binary classes as
healthy young-adult patient and healthy adult-adult patient. The obtained
results were interpreted with many statistical metrics. Significant results
were obtained through expert systems and it was found that young-healthy data
could be distinguished from adult-patient data with lower error values and 100%
classification accuracy rate. ANN has proved its success for both classes with
the error values closest to zero among the decision mechanisms. Although there
are studies in this field in literature, the lack of adequate analysis of the
effect of age factor on PH increased the importance of this study. Furthermore,
the fact that some of the effective features used in this study were not used
previously in this area where PD was automatically detected by expert systems
supports the contribution of the study to the literature.

References

  • D. Braga, A. M. Madureira, L. Coelho, & R. Ajith. 2019). Automatic detection of Parkinson’s disease based on acoustic analysis of speech. Engineering Applications of Artificial Intelligence, vol. 77, pp. 148-158.
  • D. Joshi, A. Khajuria, & P. Joshi. 2017). An automatic non-invasive method for Parkinson's disease classification. Computer methods and programs in biomedicine, vol. 145, pp. 135-145.
  • F. Wahid, R. K. Begg, C. J. Hass, S. Halgamuge, & D. C. Ackland. 2015). Classification of Parkinson's disease gait using spatial-temporal gait features. IEEE journal of biomedical and health informatics, vol. 19, no. 6, pp. 1794-1802.
  • J. Hannink et al. 2016). Stride length estimation with deep learning. arXiv preprint arXiv:1609.03321.
  • T. Khan & J. Westin. (2011). Motion cues analysis for Parkinson gait recognition. in 15th International Congress of Parkinson's Disease and Movement Disorders, Toronto, Canada, 5-9 juni, 2011.
  • J. D. A. Paredes, B. Muñoz, W. Agredo, Y. Ariza-Araújo, J. L. Orozco, & A. Navarro. (2015). A reliability assessment software using Kinect to complement the clinical evaluation of Parkinson's disease. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE, pp. 6860-6863.
  • A. P. Rocha, H. Choupina, J. M. Fernandes, M. J. Rosas, R. Vaz, & J. P. S. Cunha. (2015). Kinect v2 based system for Parkinson's disease assessment. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE, pp. 1279-1282.
  • A. P. Rocha, H. Choupina, J. M. Fernandes, M. J. Rosas, R. Vaz, & J. P. S. Cunha. (2014). Parkinson's disease assessment based on gait analysis using an innovative RGB-D camera system. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: IEEE, pp. 3126-3129.
  • H.-L. Chen, G. Wang, C. Ma, Z.-N. Cai, W.-B. Liu, & S.-J. Wang. 2016). An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳ s disease. Neurocomputing, vol. 184, pp. 131-144.
  • D. Rodriguez-Martin, A. Samà, C. Pérez-López, J. Cabestany, A. Català, & A. Rodríguez-Molinero. 2015). Posture transition identification on PD patients through a SVM-based technique and a single waist-worn accelerometer. Neurocomputing, vol. 164, pp. 144-153.
  • A. Zhao, L. Qi, J. Li, J. Dong, & H. Yu. 2018). A hybrid spatio-temporal model for detection and severity rating of Parkinson’s Disease from gait data. Neurocomputing, vol. 315, pp. 1-8.
  • J. M. Hausdorff, P. L. Purdon, C. Peng, Z. Ladin, J. Y. Wei, & A. L. Goldberger. 1996). Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations. Journal of applied physiology, vol. 80, no. 5, pp. 1448-1457 https://physionet.org/content/gaitdb/1.0.0/.
  • J. M. Hausdorff et al. 1997). Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. Journal of applied physiology, vol. 82, no. 1, pp. 262-269.
  • J. M. Hausdorff, M. E. Cudkowicz, R. Firtion, J. Y. Wei, & A. L. Goldberger. 1998). Gait variability and basal ganglia disorders: stride‐to‐stride variations of gait cycle timing in Parkinson's disease and Huntington's disease. Movement disorders, vol. 13, no. 3, pp. 428-437.
  • A. Jilbab, A. Benba, & A. Hammouch. 2017). Quantification system of Parkinson’s disease. International Journal of Speech Technology, vol. 20, no. 1, pp. 143-150.
  • S. Yucelbas, S. Ozsen, C. Yucelbas, G. Tezel, S. Kuccukturk, & S. Yosunkaya. 2016). Effect of EEG time domain features on the classification of sleep stages. Indian J. Sci. Technol, vol. 9, no. 25, pp. 1-8.
  • P. Careena, M. M. S. J. Preetha, & P. Arun. 2019). Research on Murmur from Time Domain Features of Heart Sounds. International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 1S4, pp. 736-743.
  • I. W. Selesnick, "The double density DWT," in Wavelets in Signal and Image Analysis: Springer, 2001, pp. 39-66.
  • C. M. Bishop. (2006). Pattern recognition and machine learning. springer.
  • I. H. Witten, E. Frank, L. E. Trigg, M. A. Hall, G. Holmes, & S. J. Cunningham. 1999). Weka: Practical machine learning tools and techniques with Java implementations.
  • C. J. Willmott & K. Matsuura. 2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, vol. 30, no. 1, pp. 79-82.
  • P. Cichosz. (2015). Data mining algorithms: explained using R. Wiley Online Library.
  • Gepsoft. (2019). Analyzing GeneXproTools Models Statistically - Root Relative Squared Error (Access date: 10 September 2019) https://www.gepsoft.com/gxpt4kb/Chapter10/Section1/SS07.htm. .
  • J. R. Landis & G. G. Koch. 1977). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, pp. 363-374.
  • Ş. Yücelbaş, C. Yücelbaş, G. Tezel, S. Özşen, S. Küççüktürk, & Ş. Yosunkaya. 2017). Pre-determination of OSA degree using morphological features of the ECG signal. Expert Systems with Applications, vol. 81, pp. 79-87.
  • H. Apaydın, S. Özekmekçi, S. Oğuz, & İ. Zileli, "Parkinson Hastalığı Hasta Ve Yakınları İçin El Kitabı. http://parkinsondernegi.com/wp-content/uploads/2017/04/Parkinson-Hastaligi-Hasta-ve-Yakinlari-icin-El-Kitabi.pdf," İstanbul, 2013.

Çift Yoğunluklu 1-D Dalgacık Dönüşümü Kullanılarak Parkinson Hastalığının Yaş Faktörüne Göre Tespit Edilmesi

Year 2019, Issue: 17, 881 - 887, 31.12.2019
https://doi.org/10.31590/ejosat.649480

Abstract

Parkinson, hareketi etkileyen
nörolojik bir sinir sistemi rahatsızlığıdır. Parkinson gibi sinir hücreleriyle
yakından ilgisi olan bir hastalığın yürüme verileriyle tespit edilebilmesi
muhtemeldir. Günümüzde birçok hastalığa ait belirtiler çok erken yaşlarda
ortaya çıkmaya başlamıştır. Bu nedenle bütün hastalıklar için yaş faktörüne göre
gerçekleştirilen analiz çalışmaları önem kazanmıştır. Bu sebeple bu çalışmada
Çift Yoğunluklu 1-D Dalgacık Dönüşümü (ÇY1DDD) kullanılarak deneklerden elde
edilen verilerin yaş faktörüne göre analiz edilmesi ve Parkinson hastalığının
(PH) yüksek doğrulukla tespit edilmesi amaçlanmıştır. Kullanılan veri seti
genç, yetişkin ve yetişkin hasta olmak üzere 15 denekten alınan yürüyüş
verilerinden oluşmaktadır. Kaydedilen veriler üzerinde öncelikle ÇY1DDD yöntemi
üç seviye olarak uygulanmış ve yaklaşım (YK) ile detay katsayıları (DK) elde
edilmiştir. Daha sonra yaş faktörüne göre elde edilen son seviye YK
verilerinden 10 adet özellik çıkarılmıştır. Çıkarılan bu özellikler sağlıklı
genç-yetişkin hasta ve sağlıklı yetişkin-yetişkin hasta olmak üzere ikili
sınıflar şeklinde 4 farklı karar mekanizmasına verilmiştir. Elde edilen
sonuçlar birçok istatistiksel metrikle yorumlanmıştır. Uzman sistemler
sayesinde anlamlı sonuçlara ulaşılmış ve genç-sağlıklı verilerinin
yetişkin-hasta verilerinden daha düşük hata değerleri ve %100 sınıflama
doğruluğu (SD) oranı ile ayrılabildiği görülmüştür. Karar mekanizmaları
arasında ise sıfıra en yakın hata değerleriyle yapay sinir ağları (YSA), her
iki sınıf için de başarısını kanıtlamıştır. Literatürde her ne kadar bu alanda
yapılan çalışmalar bulunsa bile, yaş faktörünün PH üzerindeki etkisinin
ayrıntılı analizine yeterli derecede yer verilmemesi bu çalışmanın önemini
arttırmıştır. Bunun yanında kullanılan etkin özelliklerden bazılarının PH’nin
uzman sistemler tarafından otomatik tespit edildiği çalışma alanında daha önce
kullanılmamış olması, çalışmanın literatüre katkısını önemli ölçüde
desteklemektedir. 

References

  • D. Braga, A. M. Madureira, L. Coelho, & R. Ajith. 2019). Automatic detection of Parkinson’s disease based on acoustic analysis of speech. Engineering Applications of Artificial Intelligence, vol. 77, pp. 148-158.
  • D. Joshi, A. Khajuria, & P. Joshi. 2017). An automatic non-invasive method for Parkinson's disease classification. Computer methods and programs in biomedicine, vol. 145, pp. 135-145.
  • F. Wahid, R. K. Begg, C. J. Hass, S. Halgamuge, & D. C. Ackland. 2015). Classification of Parkinson's disease gait using spatial-temporal gait features. IEEE journal of biomedical and health informatics, vol. 19, no. 6, pp. 1794-1802.
  • J. Hannink et al. 2016). Stride length estimation with deep learning. arXiv preprint arXiv:1609.03321.
  • T. Khan & J. Westin. (2011). Motion cues analysis for Parkinson gait recognition. in 15th International Congress of Parkinson's Disease and Movement Disorders, Toronto, Canada, 5-9 juni, 2011.
  • J. D. A. Paredes, B. Muñoz, W. Agredo, Y. Ariza-Araújo, J. L. Orozco, & A. Navarro. (2015). A reliability assessment software using Kinect to complement the clinical evaluation of Parkinson's disease. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE, pp. 6860-6863.
  • A. P. Rocha, H. Choupina, J. M. Fernandes, M. J. Rosas, R. Vaz, & J. P. S. Cunha. (2015). Kinect v2 based system for Parkinson's disease assessment. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE, pp. 1279-1282.
  • A. P. Rocha, H. Choupina, J. M. Fernandes, M. J. Rosas, R. Vaz, & J. P. S. Cunha. (2014). Parkinson's disease assessment based on gait analysis using an innovative RGB-D camera system. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: IEEE, pp. 3126-3129.
  • H.-L. Chen, G. Wang, C. Ma, Z.-N. Cai, W.-B. Liu, & S.-J. Wang. 2016). An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳ s disease. Neurocomputing, vol. 184, pp. 131-144.
  • D. Rodriguez-Martin, A. Samà, C. Pérez-López, J. Cabestany, A. Català, & A. Rodríguez-Molinero. 2015). Posture transition identification on PD patients through a SVM-based technique and a single waist-worn accelerometer. Neurocomputing, vol. 164, pp. 144-153.
  • A. Zhao, L. Qi, J. Li, J. Dong, & H. Yu. 2018). A hybrid spatio-temporal model for detection and severity rating of Parkinson’s Disease from gait data. Neurocomputing, vol. 315, pp. 1-8.
  • J. M. Hausdorff, P. L. Purdon, C. Peng, Z. Ladin, J. Y. Wei, & A. L. Goldberger. 1996). Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations. Journal of applied physiology, vol. 80, no. 5, pp. 1448-1457 https://physionet.org/content/gaitdb/1.0.0/.
  • J. M. Hausdorff et al. 1997). Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. Journal of applied physiology, vol. 82, no. 1, pp. 262-269.
  • J. M. Hausdorff, M. E. Cudkowicz, R. Firtion, J. Y. Wei, & A. L. Goldberger. 1998). Gait variability and basal ganglia disorders: stride‐to‐stride variations of gait cycle timing in Parkinson's disease and Huntington's disease. Movement disorders, vol. 13, no. 3, pp. 428-437.
  • A. Jilbab, A. Benba, & A. Hammouch. 2017). Quantification system of Parkinson’s disease. International Journal of Speech Technology, vol. 20, no. 1, pp. 143-150.
  • S. Yucelbas, S. Ozsen, C. Yucelbas, G. Tezel, S. Kuccukturk, & S. Yosunkaya. 2016). Effect of EEG time domain features on the classification of sleep stages. Indian J. Sci. Technol, vol. 9, no. 25, pp. 1-8.
  • P. Careena, M. M. S. J. Preetha, & P. Arun. 2019). Research on Murmur from Time Domain Features of Heart Sounds. International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 1S4, pp. 736-743.
  • I. W. Selesnick, "The double density DWT," in Wavelets in Signal and Image Analysis: Springer, 2001, pp. 39-66.
  • C. M. Bishop. (2006). Pattern recognition and machine learning. springer.
  • I. H. Witten, E. Frank, L. E. Trigg, M. A. Hall, G. Holmes, & S. J. Cunningham. 1999). Weka: Practical machine learning tools and techniques with Java implementations.
  • C. J. Willmott & K. Matsuura. 2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, vol. 30, no. 1, pp. 79-82.
  • P. Cichosz. (2015). Data mining algorithms: explained using R. Wiley Online Library.
  • Gepsoft. (2019). Analyzing GeneXproTools Models Statistically - Root Relative Squared Error (Access date: 10 September 2019) https://www.gepsoft.com/gxpt4kb/Chapter10/Section1/SS07.htm. .
  • J. R. Landis & G. G. Koch. 1977). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, pp. 363-374.
  • Ş. Yücelbaş, C. Yücelbaş, G. Tezel, S. Özşen, S. Küççüktürk, & Ş. Yosunkaya. 2017). Pre-determination of OSA degree using morphological features of the ECG signal. Expert Systems with Applications, vol. 81, pp. 79-87.
  • H. Apaydın, S. Özekmekçi, S. Oğuz, & İ. Zileli, "Parkinson Hastalığı Hasta Ve Yakınları İçin El Kitabı. http://parkinsondernegi.com/wp-content/uploads/2017/04/Parkinson-Hastaligi-Hasta-ve-Yakinlari-icin-El-Kitabi.pdf," İstanbul, 2013.
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Cüneyt Yücelbaş 0000-0002-4005-6557

Şule Yücelbaş 0000-0002-6758-8502

Publication Date December 31, 2019
Published in Issue Year 2019 Issue: 17

Cite

APA Yücelbaş, C., & Yücelbaş, Ş. (2019). Çift Yoğunluklu 1-D Dalgacık Dönüşümü Kullanılarak Parkinson Hastalığının Yaş Faktörüne Göre Tespit Edilmesi. Avrupa Bilim Ve Teknoloji Dergisi(17), 881-887. https://doi.org/10.31590/ejosat.649480