Differentiation of cirrhotic patients with and without hepatic encephalopathy from the analysis of fine motor patterns: A pilot study with the Leap Motion Controller

Authors

DOI:

https://doi.org/10.33448/rsd-v10i7.16749

Keywords:

Hepatic Encephalopathy; Movement; Pilot projects.

Abstract

Aim: analyze the motor precision of cirrhotic patients with or without hepatic encephalopathy (HE), in different severities, through the geospatial capture of the hands. Methodology: The target audience was patients at the Gastroenterology Service of a tertiary hospital in Northeastern Brazil. Data were collected from three groups of patients (A, unidentified EH; B, grade I EH and; C, grade II EH). Motricity data collection was performed with the Leap Motion Controller (LMC). The collected data were composed by the position of 16 points of the hands in three dimensions that, in sequence, were converted into distance between two points. Results: 60 patients with a mean age of 54.6 (± 14.7) years were included. The Kruskal-Wallis and Dunn tests indicated differences in the medians of the variables for the three groups (p < 0.05). The graphical representations show a difference in motor precision between the groups in an index of 100% of the variables, with variations with a tendency of C > B > A in 87.5% of the cases. The frequency of movement of the fingers, of both hands, had the potential to differentiate the groups. Direction is more discriminating than position and speed. Conclusion: The results suggest the possibility of differentiating the classes of patients and that the progression of motor deviation is one of the complications of the worsening of the disease.

References

Agarwal R, & Baid R. Asterixis. (2016). Journal of Postgraduate Medicine. 62 (2). Recuperado em: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4944342.

Alghamdi M, Al-Mallah M, Keteyian S, Brawner C, Ehrman J, & Sakr S. (2017). Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: the henry ford exercise testing (fit) project. Plos One. 12 (7); 179805. Recuperado em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179805.

Amodio P. (2018). Hepatic encephalopathy: Diagnosis and management. Liver Int. 38 (6); 966-75. Recuperado em: https://onlinelibrary.wiley.com/doi/full/10.1111/liv.13752

Bajaj JS, Hafeezullah M, Hoffmann RG, Varma RR, Franco J, Binion DG, Hammeke TA, & Saeian K. (2008). Navigation skill impairment: another dimension of the driving difficulties in minimal hepatic encephalopathy. Hepatology. 47 (2); 596-604. Recuperado em: https://aasldpubs.onlinelibrary.wiley.com/doi/full/10.1002/hep.22032.

Bittencout PL, Strauss E, Terra C, Alvares-da-Silva MR, Martinelli ALC, Mattos AA, Campos AC, Santos BC, Oliveira CPD, Marroni CP, Lobato CMDO, Marroni CA, Almeida DFG, Parise ER, Soares EC, Correa EBD, Barros FMDR, Silva FMDQ, Pandullo FL, Carrilho FJ, Souto FJD, Porta G, Souza GAD, Silva GF, Schulz GJ, Rosa H, Coelho HS, Pereira JL, Costa MA, Souza MPD, Capacci MDLL, Kondo M, Barros MFA, Pessoa MG, Massarolo PCB, Paraná Filho R, & Santos R. (2011) Encefalopatia Hepática: Relatório da 1ª Reunião Monotemática da Sociedade Brasileira de Hepatologia. Gastroenterologia e Endoscopia Digestiva. 2011; 30 (nd); 10-34. Recuperado em: http://sbhepatologia.org.br/pdf/encefalopatia/ged.pdf.

Butt AH, Rovini E, Dolciotti C, De Petris G, Bongioanni P, Carboncini MC, & Cavallo F. (2018). Objective and automatic classification of Parkinson disease with Leap Motion controller. Biomedical Engineering Online. 17 (1). Recuperado em: https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-018-0600-7.

Campagna F, Montagnese S, Ridola L, Senzolo M, Schiff S, Rui MD, Paquale C, Nardelli S, Pentassuglio I, Merkel C, Angeli P, Riggio O, & Amodio O. (2017). The animal naming test: an easy tool for the assessment of hepatic encephalopathy. Hepatology. 66 (1); 198-218. Recuperado em: https://aasldpubs.onlinelibrary.wiley.com/doi/abs/10.1002/hep.29146.

Casula M, Rangarajan N, & Shields P. (2020). The potential of working hypotheses for deductive exploratory research. Qual Quant. 54 (5-6). Recuperado em: https://link.springer.com/article/10.1007%2Fs11135-020-01072-9

Chen QF, Chen HJ, Liu J, Sun T, & Shen QT. (2016). Machine learning classification of cirrhotic patients with and without minimal hepatic encephalopathy based on regional homogeneity of intrinsic brain activity. Plos One. 11 (3); 1-15. Recuperado em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0151263.

Colombini G, Duradoni M, Carpi F, Vagnoli L, &Guazzini. (2021). LEAP Motion Technology and Psychology: A Mini-Review on Hand Movements Sensing for Neurodevelopmental and Neurocognitive Disorders. Enviromental Research and Public Health. 18(8); 4006-4014; Recuperado em: https://www-ncbi-nlm-nih.ez68.periodicos.capes.gov.br/pmc/articles/PMC8069152/

Cortés-Pérez I, Zagalaz-Anula N, Montoro-Cárdenas D, Lomas-Veja R, Obrero-Gaitán E, & Osuna-Pérez MC. (2021). Leap Motion Controller Video Game-Based Therapy for Upper Extremity Motor Recovery in Patients with Central Nervous System Diseases: A Systematic Review with Meta-Analysis. Sensors.21(6); 2065-70. Recuperado em: https://www.mdpi.com/1424-8220/21/6/2065

Dzikri AI, & Kurniawan DE. (2018). Hand Gesture Recognition for Game 3D Object Using The Leap Motion Controller with Backpropagation Method. 2018 International Conference On Applied Engineering (Icae). 1-5. Recuperado em: https://ieeexplore.ieee.org/document/8579400.

Guna J, Jakus G, Pogačnik M, Tomažič S, & Sodnik J. (2014). An analysis of the precision and reliability of the leap motion sensor and its suitability for static and dynamic tracking. Sensors. 14(2); 3702-20. Recuperado em: https://www.mdpi.com/1424-8220/14/2/3702/htm

Guzsvinecz T, Szucs V, & Sik-Lanyl C. (2019). Suitability of the Kinect Sensor and Leap Motion Controller—A Literature Review. Sensors. 19 (5); 1072-1086. Recuperado em: https://www-ncbi-nlm-nih.ez68.periodicos.capes.gov.br/pmc/articles/PMC6427122/.

Jiao Y, Teng G, & Wang X. (2013). Predictive model for minimal hepatic encephalopathy based on cerebral functional connectivity. International Conference on Biomedical Engineering and Informatics. 6 (1); 541-45. Recuperado em: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5599725.

Jiao Y, Wang XH, Chen R, Tang TY, Zhy XQ, & Teng GJ. (2017) Predictive models of minimal hepatic encephalopathy for cirrhotic patients based on large-scale brain intrinsic connectivity networks. Scientific Reports. 7(1); 1-12. Recuperado em: https://www.nature.com/articles/s41598-017-11196-y

Kim S, Park S, & Lee O. (2020). Development of a Diagnosis and Evaluation System for Hemiplegic Patients Post-Stroke Based on Motion Recognition Tracking and Analysis of Wrist Joint Kinematics. Sensors. 20 (16); 4548. Recuperado em: https://www.mdpi.com/1424-8220/20/16/4548.

Lizardo JL. (2020). O desafio de produzir uma pesquisa no meio de uma pandemia: o olhar de uma pesquisadora estrangeira no Brasil. Educação, Comunicação e Tecnologia. 2 (2), 25-33. Recuperado em: https://revista.uemg.br/index.php/sciasedcomtec/article/view/5020.

Nardone R, Taylor AC, Höller Y, Brigo F, Piergiorgio L, & Trinka E. (2016). Minimal hepatic encephalopathy: A review. Neuroscience Research. 111(ni); 1-12. Recuperado em: https://www.sciencedirect.com/science/article/abs/pii/S0168010216300499?via%3Dihub.

Pinho M, Cerqueira R, & Peixoto B. (2011). Pontuação psicométrica da encefalopatia hepática: Dados da normalização para a população portuguesa. Acta Médica Portuguesa. 24 (2); 319-26. Recuperado em: https://www.actamedicaportuguesa.com/revista/index.php/amp/article/viewFile/1485/1071.

Piovesan A, & Temporini E.R. (1995) Pesquisa exploratória: procedimento metodológico para o estudo de fatores humanos no campo da saúde pública. Rev. Saúde Pública. 29 (4); 318-325. Recuperado de: https://www.scielo.br/j/rsp/a/fF44L9rmXt8PVYLNvphJgTd/?lang=pt&format=pdf.

Pires MR, & Marinho RT. (2016). Impacto biopsicossocial da encefalopatia hepática. Dissertação de mestrado, Faculdade de Medicina, Universidade de Lisboa. Recuperado em: https://repositorio.ul.pt/bitstream/10451/29101/1/MarianaRPires.pdf.

Sotil EU, Gottstein J, Ayala E, Randolph C, & Blei AT. (2009). The impact o preoperative overt encephalopathy on neurocognitive function after liver transplantation. Liver Transplant. 15 (2); 184-192. Recuperado em: https://aasldpubs.onlinelibrary.wiley.com/doi/full/10.1002/lt.21593.

Sui Y, Wei Y, & Zhao D. (2015). Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE. Computational and Mathematical Methods In Medicine. 1-13. Recuperado em: https://www.hindawi.com/journals/cmmm/2015/368674.

The K, Armitage P, Tesfayne S, Selvarajah D, & Wilkinson ID. (2020). Imbalanced learning: improving classification of diabetic neuropathy from magnetic resonance imaging. Plos One. 15 (12); 243907. Recuperado em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0243907.

Turlapati VPK, & Prusty MR. (2020). Outlier-SMOTE: a refined oversampling technique for improved detection of covid-19. Intelligence-Based Medicine. 3(4); 100023. Recuperado em: https://www.sciencedirect.com/science/article/pii/S2666521220300235?via%3Dihub.

Vilstrup H, Amodio P, Bajaj J, Cordoba J, Ferenci P, Mullen KD, Weissenborn K, & Wong P. (2014). Hepatic encephalopathy in chronic liver disease: 2014 Practice Guideline by the American Association for the Study of Liver Diseases and the European Association for the Study of the Liver. Hepatology. 60 (2); 715-35. Recuperado em: http://www.clubepatologiospedalieri.it/wp-content/uploads/2017/01/Linee-guida-AASLD-Encefalopatia-Epatica-2014.pdf.

Vivar G, Almanza-Ojeda DL, Cheng I, Gomez JC, Andrade-Lucio JA, & Ibarra-Manzano MA. (2019). Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson’s Disease Patients. Sensors. 19 (9); 2072. Recuperado em: https://www.mdpi.com/1424-8220/19/9/2072.

Vysocký A, Grushko S, Oščádal P, Kot T, Babjak J, Jánoš R, Sukop M, & Bobovský Z. (2020). Analysis of Precision and Stability of Hand Tracking with Leap Motion Sensor. Sensors. 20 (15); 4088-93. Recuperado em: https://www.mdpi.com/1424-8220/20/15/4088

Wang Z, Wang P, Xing L, Mei L, Zhao J, & Zhang T. (2017). Leap Motion-based virtual reality training for improving motor functional recovery of upper limbs and neural reorganization in subacute stroke patients. Neural Regeneration Research. 12 (11); 1823-28. Recuperado em: https://www.nrronline.org/article.asp?issn=1673-5374;year=2017;volume=12;issue=11;spage=1823;epage=1831;aulast=Wang.

Weichert F, Bachmann D, Rudak B, & Fisseler D. (2013). Analysis of the accuracy and robustness of the Leap Motion Controller. Sensors. 13 (5); 6380-93. Recuperado em om: https://www.mdpi.com/1424-8220/13/5/6380/htm.

Wijdicks EFM. Hepatic encephalopathy. New England Journal of Medicine. 2016; 375(17); 1660-70. Recuperado em: https://www.nejm.org/doi/10.1056/NEJMra1600561.

Published

30/06/2021

How to Cite

MAIA, M. M.; PESSOA, F. S. R. de P. .; OLIVEIRA, C. P.; NOBRE, P. H. P. .; SALGUEIRO, C. C. de M. . Differentiation of cirrhotic patients with and without hepatic encephalopathy from the analysis of fine motor patterns: A pilot study with the Leap Motion Controller . Research, Society and Development, [S. l.], v. 10, n. 7, p. e48310716749, 2021. DOI: 10.33448/rsd-v10i7.16749. Disponível em: https://www.rsdjournal.org/index.php/rsd/article/view/16749. Acesso em: 19 apr. 2024.

Issue

Section

Health Sciences