ANALYSIS OF INVOLUNTARY MOVEMENTS OF PATIENTS WITH TREMOR SYMPTOMS UNDER THE INFLUENCE OF COGNITIVE INFLUENCES
DOI:
https://doi.org/10.32782/mathematical-modelling/2024-7-2-14Keywords:
tremor, EEG, cognitive influences, motor control, graphics tablet, essential tremor, Parkinson’s diseaseAbstract
This study introduces a cutting-edge digital approach to analyzing the relationship between involuntary movements and brain activity in patients with neurological disorders associated with tremor, such as Parkinson's disease. The research was conducted on real patients, offering a practical perspective on how cognitive influences impact motor control and brain function. To achieve this, patients were asked to draw spirals on a Huion KAMVAS Pro 16 graphics tablet, a device equipped with a touch-sensitive screen and stylus, allowing for precise tracking of movement. Simultaneously, their brain activity was monitored using the NEUROCOM EEG system, with electrodes positioned on the posterior region of the head-an area strongly involved in motor coordination. This dual setup ensured synchronized data collection of motor performance and neural dynamics. The primary goal of the study was to uncover connections between involuntary movements, observed as tremorinduced irregularities in the spiral drawings, and specific patterns of brain activity recorded through EEG. By comparing the data collected in medicated and unmedicated states, significant variations in tremor severity and brain function were identified. Key regions of the brain involved in motor regulation were identified, shedding light on the mechanisms that underlie tremor development and its modulation under different conditions. This approach offers a groundbreaking perspective on the diagnosis and treatment of tremor-related conditions. Unlike traditional methods, which often rely on subjective assessments and are limited in capturing real-time neural activity, this method provides a more objective and detailed analysis of motor impairments. By integrating precise movement data from the graphics tablet with neurophysiological signals from EEG, the study demonstrates the potential for creating more effective, personalized treatment strategies for conditions like Parkinson’s disease. The findings open new avenues for leveraging digital tools in clinical research, enabling a deeper understanding of how motor and cognitive processes interact in patients with tremor symptoms.
References
Haubenberger D., Kalowitz D., Nahab F. B, Toro C., Ippolito D., Luckenbaugh D. A., Wittevrongel L., Hallett M. Validation of Digital Spiral Analysis as Outcome Parameter for Clinical Trials in Essential Tremor. Movement Disorders. 2011. Vol. 26. Issue 11. P. 2073−2080.
Electroencephalography complex NEUROKOM, NEUROLAB. Instructions for medicalapplication AINC.941311.001 I1 U 33.1-02066769-001-2002.
Rajaraman V., Jack D., Adamovich S. V., Hening W., Sage J., Poizner H. A Novel Quantitative Method for 3D Measurement of Parkinsonian Tremor. Clinical Neurophysiology. 2000. Vol. 11. Issue 2. P. 187−369.
Wang J.-S., Chuang F.-C. An Accelerometer-Based Digital Pen with a Trajectory Recognition Algorithm for Handwritten Digit and Gesture Recognition. IEEE Transactions on Industrial Electronics. 2012. Vol. 59. Issue 7. P. 2998−3007. DOI: 10.1109/TIE.2011.2167895.
Xie H., Wang Z. Mean frequency derived via Huang-Hilbert transform with application tofatigue EMG signal analysis. Comput Meth Progr Biomed, 2006. 82. p. 114–20.
Louis E. D., Gillman A., Böschung S., Hess C. W., Yu Q., Pullman S. L. High width Variability during Spiral Drawing: Further Evidence of Cerebellar Dysfunction in Essential Tremor. Cerebellum. 2012. Vol. 11. Issue 4. P. 872−879. DOI: 10.1007/s12311-011-0352-4.
Legrand A.P., Rivals I., Richard A., Apartis E., Roze E., Vidailhet M., Meunier S., Hainque E. New insight in spiral drawing analysis methods – Application to action tremor quantification.J Clinical Neurophysiology. 2017. 128 (10), pp. 1823–1834.
Mudryk I., Petryk M. Hybrid artificial intelligence systems for complex neural network analysisof abnormal neurological movements with multiple cognitive signal nodes. 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP) : Conference, Lviv, 21-25 August 2020. P. 108–111.
Viviani P., Burkhard P.R., Chiuvé S.C., dell’Acqua C.C., Vindras P. Velocity control in Parkinson’s disease: a quantitative analysis of isochrony in scribbling movements. Exp Brain 2009. 194. p. 259–283.
Khimich A.N., Petryk M.R., Mykhalyk D.N., Boyko I.V., Popov A.V., Sydoruk. V.A. Methods for mathematical modeling and identification of complex processes and systems based onvisoproductive computing (neuro- and nanoporous cyber-physical systems with feedback, models with sparse structure data, parallel computing). Monograph, Kiev: National Academy of Sciences of Ukraine. Glushkov Institute of Cybernetics. 2019. 176 p. ISBN: 978-966-02-9188-1.
Salarian А., Russmann H., Wider C., Burkhard P.R., Vingerhoets F.J., Aminian K. Quantificationof tremor and bradykinesia in Parkinson's disease using a novel ambulatory monitoring system, Biomedical Engineering, IEEE Transactions on, 2007. 54. Jg., Nr. 2, pp. 313–322.
Bhidayasiri R., Mari Z. Digital phenotyping in Parkinson's disease: Empowering neurologistsfor measurement-based care. Parkinsonism Relat Disord. 2020 Nov; 80. P. 35–40. DOI: 10.1016/j.parkreldis.2020.08.038.
Lo G., Suresh A. R., Stocco L., González-Valenzuela S., and Leung V. C. A wireless sensor system for motion analysis of Parkinson's disease patients, (PERCOM Workshops), 2011 IEEE International Conference on. IEEE, pp. 372–375.
