Abstract
The process of diagnosing tremor patients often leads to misdiagnoses. Therefore, existing technical methods for analysing tremor are needed to more effectively distinguish between different diseases. For this purpose, a system has been developed that classifies measured tremor signals in real time. To achieve this, the hand tremor of 561 subjects has been measured in different hand positions. Acceleration and surface electromyography are recorded during the examination. For this study, data from subjects with Parkinson’s Disease, Essential Tremor, and physiological tremor are considered. In a first signal analysis feature extraction is performed, and the resulting features are examined for their discriminative value. In a second step, three classification models based on different pattern recognition techniques are developed to classify the subjects with respect to their tremor type. With a trained decision tree, the three tremor types can be classified with a relative diagnostic accuracy of 83.14%. A neural network achieves 84.24% and the combination of both classifiers yields a relative diagnostic accuracy of 85.76%. The approach is promising and involving more features of the recorded time series will improve the discriminative value.
-
Research funding: G.D., G.S. and C.B. are supported by the Deutsche Forschungsgemeinschaft (SFB 1261, TP B2, B6, B9, B10, T1, Z2).
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: Authors state no conflict of interest regarding this work.
-
Informed consent: Not applicable.
-
Ethical approval: The research related to human use has complied with all the relevant national regulations, institutional policies, and in accordance with the tenets of the Helsinki Declaration, and has been approved by the author’s Institutional Review Board or equivalent committee (Ethikkommission der Medizinischen Fakultät der Christian-Albrechts-Universität zu Kiel).
References
1. Deuschl, G, Bain, P, Brin, M. Consensus statement of the movement disorder society on tremor. Mov Disord 1998;13:2–23. https://doi.org/10.1002/mds.870131303.Search in Google Scholar
2. Collaborators GPsD. Global, regional and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 2019;18:459–80.10.1016/S1474-4422(18)30499-XSearch in Google Scholar
3. Bathia, KP, Bain, P, Bajaj, N, Elble, RJ, Hallett, M, Louis, ED, et al.. Consensus statement on the classification of tremors. From the task force on tremor of the International Parkinson and Movement Disorder Society. Mov Disord 2018;33:75–87. https://doi.org/10.1002/mds.27121.Search in Google Scholar PubMed PubMed Central
4. Deuschl, G, Raethjen, J, Lindemann, M, Krack, P. The pathophysiology of tremor. Muscle Nerve 2001;24:716–35. https://doi.org/10.1002/mus.1063.Search in Google Scholar PubMed
5. Rizzo, G, Copetti, M, Arcuti, S, Martino, D, Fontana, A, Logroscino, G. Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis. Neurology 2016;86:566–76. https://doi.org/10.1212/wnl.0000000000002350.Search in Google Scholar PubMed
6. Timmer, J, Gantert, C, Deuschl, G, Honerkamp, J. Characteristics of hand tremor time series. Biol Cybern 1993;70:75–80. https://doi.org/10.1007/bf00202568.Search in Google Scholar PubMed
7. Hömberg, V, Hefter, H, Reiners, K, Freund, HJ. Differential effects of changes in mechanical limb properties on physiologic and pathologic tremor. J Neurol Neurosurg Psychiatr 1987;50:568–79. https://doi.org/10.1136/jnnp.50.5.568.Search in Google Scholar PubMed PubMed Central
8. Deutsche Gesellschaft für Neurologie (DGN), Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften (AWMF). S3-Leitlinie idiopathisches Parkinson-Syndrom. Langversion. 2016. Available from: https://www.awmf.org/uploads/tx_szleitlinien/030-010k_S3_Parkinson_Syndrome_Idiopathisch_2016-06-abgelaufen.pdf [Accessed Nov 2020].Search in Google Scholar
9. Veluvolu, KC, Ang, WT. Estimation and filtering of physiological tremor for real-time compensation in surgical robotics applications. Int J Med Robot Comput Assist Surg MRCAS 2010;6:334–42. https://doi.org/10.1002/rcs.340.Search in Google Scholar PubMed
10. Jankovic, J. Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatr 2008;79:368–76. https://doi.org/10.1136/jnnp.2007.131045.Search in Google Scholar PubMed
11. Jeon, H, Lee, W, Park, H, Lee, HJ, Kim, SK, Kim, HB, et al.. Automatic classification of tremor severity in Parkinson’s disease using a wearable device. Sensors 2017;17:2067. https://doi.org/10.3390/s17092067.Search in Google Scholar PubMed PubMed Central
12. Surangsrirat, D, Thanawattano, C, Pongthornseri, R, Dumnin, S, Anan, C, Bhidayasiri, R. Support vector machine classification of Parkinson’s disease and essential tremor subjects based on temporal fluctuation. In: 2016 38th Annual international conference of the IEEE Engineering in Medicine and Biology Society. Orlando: (EMBC); 2016.10.1109/EMBC.2016.7592190Search in Google Scholar
13. Deuschl, G, Krack, P, Lauk, M, Timmer, J. Clinical neurophysiology of tremor. J Clin Neurophysiol 1996;13:110–21. https://doi.org/10.1097/00004691-199603000-00002.Search in Google Scholar
14. Lauk, M, Timmer, J, Lücking, CH, Honerkamp, J, Deuschl, G. A software for recording and analysis of human tremor. Comput Methods Progr Biomed 1999;60:65–77. https://doi.org/10.1016/s0169-2607(99)00012-7.Search in Google Scholar
15. McManus, L, Lowery, M, Merletti, R, Søgaard, K, Besomi, M, Clancy, EA, et al.. Consensus for experimental design in electromyography (CEDE) project: terminology matrix. J Electromyogr Kinesiol 2021;59:102565. https://doi.org/10.1016/j.jelekin.2021.102565.Search in Google Scholar PubMed
16. Digital signal processing and system theory. Real-time framework. Available from: https://dss-kiel.de/index.php/research/realtime-framework [Accessed 10 Nov 2020].Search in Google Scholar
17. Journée, HL. Demodulation of amplitude modulated noise: a mathematical evaluation of a demodulator for pathological tremor EMG’s. IEEE Trans Biomed Eng 1983;30:304–8.10.1109/TBME.1983.325120Search in Google Scholar
18. Besomi, M, Hodges, PW, Van Dieën, J, Carson, RG, Clancy, EA, Disselhorst-Klug, C, et al.. Consensus for experimental design in electromyography (CEDE) project: electrode selection matrix. J Electromyogr Kinesiol 2019;48:128–44. https://doi.org/10.1016/j.jelekin.2019.07.008.Search in Google Scholar PubMed
19. Al-Labadi, L, Zarepour, M. Two-sample Kolmogorov–Smirnov test using a Bayesian nonparametric approach. Math Methods Stat 2017;26:212–25. https://doi.org/10.3103/s1066530717030048.Search in Google Scholar
20. Daneault, JF, Carignan, B, Codère, CÉ, Sadikot, AF, Duval, C. Using a smart phone as a standalone platform for detection and monitoring of pathological tremors. Front Hum Neurosci 2013;6:357. https://doi.org/10.3389/fnhum.2012.00357.Search in Google Scholar PubMed PubMed Central
21. Ding, H, Qian, B, Li, Y, Tang, Z. A method combining LPC-based cepstrum and harmonic product spectrum for pitch detection. In: International conference on Intelligent Information Hiding and Multimedia. USA: Pasadena; 2006.10.1109/IIH-MSP.2006.265059Search in Google Scholar
22. Lyons, NI, Hutcheson, K. C20. Comparing diversities: gini’s index. J Stat Comput Simulat 1978;8:75–8. https://doi.org/10.1080/00949657808810250.Search in Google Scholar
23. Chollet, F. Keras. Available from: https://keras.io [Accessed 1 Mar 2021].Search in Google Scholar
24. Abadi, M, Barham, P, Chen, J, Chen, Z, Davis, A, Dean, J, et al.. Tensorflow: a system for large-scale machine learning. In: 12th USENIX symposium on Operating Systems Design and Implementation (OSDI 16). Savannah: USENIX Association; 2016:265–83 pp.Search in Google Scholar
25. Simundic, AM. Measures of diagnostic accuracy: basic definitions. Med Biol Sci 2009;19:203–11.Search in Google Scholar
26. Zhang, J, Xing, Y, Ma, X, Feng, L. Differential diagnosis of Parkinson disease, essential tremor, and enhanced physiological tremor with the tremor analysis of EMG. Parkinson’s Dis 2017;2017:1–4. https://doi.org/10.1155/2017/1597907.Search in Google Scholar PubMed PubMed Central
27. di Biase, L, Brittain, JS, Shah, SA, Pedrosa, DJ, Cagnan, H, Mathy, A, et al.. Tremor stability index: a new tool for differential diagnosis in tremor syndromes. Brain J Neurol 2017;140:1977–86. https://doi.org/10.1093/brain/awx104.Search in Google Scholar PubMed PubMed Central
© 2022 Walter de Gruyter GmbH, Berlin/Boston
