Application of imaging techniques to objectify the Finger Tapping test used in the diagnosis of Parkinson's disease

• Autorzy: Jakubowski Jacek , Potulska-Chromik Anna , Chmielińska Jolanta , Nojszewska Monika , Kostera-Pruszczyk Anna

Identyfikatory

DOI

10.24425/bpasts.2023.144886

• Języki publikacji: EN

Abstrakty

EN

Finger tapping is one of the standard tests for Parkinson's disease diagnosis performed to assess the motor function of patients' upper limbs. In clinical practice, the assessment of the patient's ability to perform the test is carried out visually and largely depends on the experience of clinicians. This article presents the results of research devoted to the objectification of this test. The methodology was based on the proposed measurement method consisting in frame processing of the video stream recorded during the test to determine the time series representing the distance between the index finger and the thumb. Analysis of the resulting signals was carried out in order to determine the characteristic features that were then used in the process of distinguishing patients with Parkinson's disease from healthy cases using methods of machine learning. The research was conducted with the participation of 21 patients with Parkinson's disease and 21 healthy subjects. The results indicate that it is possible to obtain the sensitivity and specificity of the proposed method at the level of approx. 80 %. However, the patients were in the so-called ON phase when symptoms are reduced due to medication, which was a much greater challenge compared to analyzing signals with clearly visible symptoms as reported in related works.

Słowa kluczowe

EN

image processing; medical diagnosis; Parkinson’s disease; finger tapping test

PL

przetwarzanie obrazu; diagnoza medyczna; choroba Parkinsona; test stukania palcem

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2023
• Tom: Vol. 71, nr 2
• Strony: art. no. e144886
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Jakubowski Jacek

• Faculty of Electronics, Military University of Technology, Warsaw, Poland

• https://orcid.org/Faculty+of+Electronics%2C+Military+University+of+Technology%2C+Warsaw%2C+Poland+0000-0003-2235-2712+

autor

Potulska-Chromik Anna

• Department of Neurology, Medical University of Warsaw, Warsaw, Poland

• https://orcid.org/0000-0002-8332-1090

autor

Chmielińska Jolanta

• Faculty of Electronics, Military University of Technology, Warsaw, Poland

• https://orcid.org/0000-0003-2579-4313

autor

Nojszewska Monika

• Department of Neurology, Medical University of Warsaw, Warsaw, Poland

• https://orcid.org/0000-0003-4231-5636

autor

Kostera-Pruszczyk Anna

• Department of Neurology, Medical University of Warsaw, Warsaw, Poland

• https://orcid.org/0000-0002-7309-1914

Bibliografia

• [1] A. Berardelli, J.C. Rothwell, P.D. Thompson, and M. Hallett, “Pathophysiology of bradykinesia in Parkinson’s disease,” Brain, vol. 124, no. 11, pp. 2131–2146, 2001, doi: 10.1093/brain/124.11.2131.
• [2] J.A. Obeso, “The Unified Parkinson’s Disease Rating Scale (UPDRS): Status and recommendations,” Mov. Disord., vol. 18, pp. 738–750, 2003.
• [3] International Parkinson and Movement Disorder Society. “MDS Rating Scales.” [Online]. Available: https://www.movementdisorders.org/MDS/MDS-Rating-Scales/MDS-Unified-Parkinsons-Disease-Rating-Scale-MDS-UPDRS.htm [Accessed: 19 Feb. 2023].
• [4] W. Chen et al., “A Survey on Hand Pose Estimation with Wearable Sensors and Computer-Vision-Based Methods,” Sensors, vol. 20, p. 1074, 2020, doi: 10.3390/s20041074.
• [5] M. Djuric-Jovicic, N. Jovičić, A. Roby-Brami, M. Popović, V. Kostić, and A. Djordjevic, “Quantification of Finger-Tapping Angle Based on Wearable Sensors,” Sensors, vol. 17, no. 203, 2017, doi: 10.3390/s17020203.
• [6] V. Bobić, M. Djurić-Jovičić, N. Dragašević, M. Popović, V. Kostić, and G. Kvaščev, “An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors,” Sensors, vol. 19, no. 11, p. 2644, 2019, doi: 10.3390/s19112644.
• [7] M. Monje, G. Foffani, J. Obeso, and Á. Sánchez-Ferro, “New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson’s Disease,” Annu. Rev. Biomed. Eng., vol. 4, no. 21, pp. 111–143, 2019, doi: 10.1146/annurev-bioeng-062117-121036.
• [8] S. Jomyo, A. Furui, T. Matsumoto, T. Tsunoda, and T. Tsuji, “A Wearable Finger-Tapping Motion Recognition System Using Biodegradable Piezoelectric Film Sensors,” in Proc. 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021, pp. 6982–6986, doi: 10.1109/EMBC46164.2021.9630643.
• [9] R. Krupicka, Z. Szabo, and M. Jirina, “Motion Camera System for Measuring Finger Tapping in Parkinson’s Disease,” in Proc. 5th European Conference of the International Federation for Medical and Biological Engineering (IFMBE), 2011, doi: 10.1007/978-3-642-23508-5_220.
• [10] C. Lainscsek et al., “Finger tapping movements of Parkinson’s disease patients automatically rated using nonlinear delay differential equations,” Chaos, vol. 22, p. 3444, 2012, doi: 10.1063/1.3683444.
• [11] M. Kuhn and K. Johnson. Applied predictive modeling. New York: Springer, 2013, doi: 10.1007/978-1-4614-6849-3.
• [12] T. Khan, D. Nyholm, J.Westin, and M. Dougherty, “A computer vision framework for finger-tapping evaluation in Parkinson’s disease,” Artif. Intell. Med., vol. 60, no. 1, pp. 27–40, 2014, doi: 10.1016/j.artmed.2013.11.004.
• [13] K. Białek et al., “Selected problems of image data preprocessing used to perform examination in Parkinson’s disease,” Proc. SPIE 11442, Radioelectronic Systems Conference, 2019, p. 114420G, doi: 10.1117/12.2565138.
• [14] M. Monje et al., “Remote Evaluation of Parkinson’s Disease Using a Conventional Webcam and Artificial Intelligence,” Front. Neurol., vol. 23, no. 12, 2021, doi: 10.3389/fneur.2021.742654.
• [15] S. Bambach, S. Lee, D.J. Crandall, and C. Yu, “Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions,” in Proc. IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1949–1957, doi: 10.1109/ICCV.2015.226.
• [16] Z. Cao, G. Hidalgo, T. Simon, S. -E.Wei, and Y. Sheikh, “Open-Pose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields,” in IEEE Trans. Pattern Anal. Mach. Intell., 2021, vol. 43, no. 1, pp. 172–186, doi: 10.1109/TPAMI.2019.2929257.
• [17] B. Das, K. Daoudi, J. Klempir, and J. Rusz, “Towards disease-specific speech markers for differential diagnosis in parkinsonism,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2019, pp. 5846–5850.
• [18] E. Majda-Zdancewicz, A. Potulska-Chromik, J. Jakubowski, M. Nojszewska, and A. Kostera-Pruszczyk, “Deep Learning vs. Feature Engineering in the Assessment of Voice Signals for Diagnosis in Parkinson’s Disease,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, p. e137347, 2021, doi: 10.24425/bpasts.2021.137347.
• [19] F. Amato, L. Borzi, G. Olmo, and J.R. Orozco-Arroyave, “An algorithm for Parkinson’s disease speech classification based on isolated words analysis,” Health Inf. Sci. Syst., vol. 9, p. 32, 2021, doi: 10.1007/s13755-021-00162-8.
• [20] J. Carrón, Y. Campos-Roca, M. Madruga, and C.J. Perez, “A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions,” Biomed. Eng. On-Line, vol. 20, p. 114, 2021, doi: 10.1186/s12938-021-00951-y.
• [21] M.T. Angelillo, D. Impedovo, G. Pirlo, and G. Vessio, “Performance-Driven Handwriting Task Selection for Parkinson’s Disease Classification,” in Lecture Notes in Computer Science, vol. 11946, 2019, doi: 10.1007/978-3-030-35166-3_20.
• [22] C.D. Rios-Urregoa, J.C. Vásquez-Correaab, J.F. Vargas-Bonillaa, E. Nöthb, F. Loperac, and J.R. Orozco-Arroyaveab, “Analysis and evaluation of handwriting in patients with Parkinson’s disease using kinematic, geometrical, and non-linear features,” Comput. Meth. Programs Biomed., vol. 173, 2019, pp. 43–52, doi: 10.1016/j.cmpb.2019.03.005.
• [23] F. Vogel, S. Holm, and O.C. Lingjærde, “Spectral moments and time domain representation of photoacoustic signals used for detection of crude oil in produced water,” in Proc. Norwegian Signal Processing. Conf. (NORSIG-01), 2001.
• [24] Y. Qiu, G. Zhou, Q. Zhao, and A. Cichocki, “Comparative study on the classification methods for breast cancer diagnosis,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 66, no 6, pp. 841–848, 2018, doi: 10.24425/bpas.2018.125931.
• [25] W. Yunzhu and C. Yunli, “A new feature extraction algorithm based on Fisher linear discriminant analysis,” in Proc. International Conference on Control, Automation and Robotics (ICCAR), 2017, pp. 24–26, doi: 10.1109/ICCAR.2017.7942729.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).