Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremor
Recent research has shown that smartphones/smartwatches have a high potential to help physicians to identify and differentiate between different movement disorders. This work aims to develop Machine Learning models to improve the differential diagnosis between patients with Parkinson’s Disease and E...
- Autores:
-
Sanchez Egea, Antonio J.
Reeb, Theresa
González, Hernán Alberto
Loaiza Duque, Julián David
González Vargas, Andrés Mauricio
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/13381
- Acceso en línea:
- https://hdl.handle.net/10614/13381
- Palabra clave:
- Diagnóstico diferencial
Giroscopios
Redes de área corporal (Electrónica)
Body area networks
Differential diagnosis
Parkinson’s disease
Essential tremor
gyroscope
Kinematic analysis
Machine learning
- Rights
- openAccess
- License
- Derechos reservados - IEEE, 2020
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Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremor |
| title |
Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremor |
| spellingShingle |
Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremor Diagnóstico diferencial Giroscopios Redes de área corporal (Electrónica) Body area networks Differential diagnosis Parkinson’s disease Essential tremor gyroscope Kinematic analysis Machine learning |
| title_short |
Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremor |
| title_full |
Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremor |
| title_fullStr |
Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremor |
| title_full_unstemmed |
Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremor |
| title_sort |
Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremor |
| dc.creator.fl_str_mv |
Sanchez Egea, Antonio J. Reeb, Theresa González, Hernán Alberto Loaiza Duque, Julián David González Vargas, Andrés Mauricio |
| dc.contributor.author.spa.fl_str_mv |
Sanchez Egea, Antonio J. Reeb, Theresa González, Hernán Alberto Loaiza Duque, Julián David |
| dc.contributor.author.none.fl_str_mv |
González Vargas, Andrés Mauricio |
| dc.contributor.corporatename.spa.fl_str_mv |
IEEE Access |
| dc.subject.armarc.spa.fl_str_mv |
Diagnóstico diferencial Giroscopios Redes de área corporal (Electrónica) |
| topic |
Diagnóstico diferencial Giroscopios Redes de área corporal (Electrónica) Body area networks Differential diagnosis Parkinson’s disease Essential tremor gyroscope Kinematic analysis Machine learning |
| dc.subject.armarc.eng.fl_str_mv |
Body area networks |
| dc.subject.proposal.eng.fl_str_mv |
Differential diagnosis Parkinson’s disease Essential tremor gyroscope Kinematic analysis Machine learning |
| description |
Recent research has shown that smartphones/smartwatches have a high potential to help physicians to identify and differentiate between different movement disorders. This work aims to develop Machine Learning models to improve the differential diagnosis between patients with Parkinson’s Disease and Essential Tremor. For this purpose, we use a mobile phone’s built-in gyroscope to record the angular velocity signals of two different arm positions during the patient’s follow-up, more precisely, in rest and posture positions. To develop and to find the best classification models, diverse factors were considered, such as the frequency range, the training and testing divisions, the kinematic features, and the classification method. We performed a two-stage kinematic analysis, first to differentiate between healthy and trembling subjects and then between patients with Parkinson’s Disease and Essential Tremor. The models developed reached an average accuracy of 97.2 ± 3.7% (98.5% Sensitivity, 93.3% Specificity) to differentiate between Healthy and Trembling subjects and an average accuracy of 77.8 ± 9.9% (75.7% Sensitivity, 80.0% Specificity) to discriminate between Parkinson’s Disease and Essential Tremor patients. Therefore, we conclude, that the angular velocity signal can be used to develop Machine Learning models for the differential diagnosis of Parkinson’s disease and Essential Tremor |
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2020 |
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2020-05-11 |
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2021-10-29T16:01:03Z |
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2021-10-29T16:01:03Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_6501 |
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21693536 |
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10.1109/ACCESS.2020.2993647 |
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21693536 10.1109/ACCESS.2020.2993647 |
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https://hdl.handle.net/10614/13381 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.citationedition.spa.fl_str_mv |
Volumen 8 (2020) |
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88875 |
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88866 |
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8 |
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Loaiza Duque, J.D., Sánchez Egea, A. J., Reeb, T., González Rojas, H.A., González Vargas, A. M. (2020). Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremor. IEEE Access. (Vol. 8, pp. 88866–88875 doi: 10.1109/ACCESS.2020.2993647 |
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IEEE Xplore |
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[1] K. P. Bhatia, P. Bain, N. Bajaj, R. J. Elble, M. Hallett, E. D. Louis, J. Raethjen, M. Stamelou, C. M. Testa, and G. Deuschl, ``Consensus statement on the classi cation of tremors. From the task force on tremor of the international parkinson and movement disorder society,'' Move- ment Disorders, vol. 33, no. 1, pp. 75 87, Jan. 2018, doi: 10.1002/mds. 27121. [2] C. Bhavana, J. Gopal, P. Raghavendra, K. M. Vanitha, and V. Talasila, ``Techniques of measurement for Parkinson's tremor highlighting advantages of embedded IMU over EMG,'' in Proc. Int. Conf. Recent Trends Inf. Technol. (ICRTIT), Apr. 2016, pp. 1 5, doi: 10.1109/ICRTIT.2016. 7569560. [3] A. M. Woods, M. Nowostawski, E. A. Franz, and M. Purvis, ``Parkinson's disease and essential tremor classi cation on mobile device,'' Per- vas. Mobile Comput., vol. 13, pp. 1 12, Aug. 2014, doi: 10.1016/j.pmcj. 2013.10.002. [4] S. Barrantes, A. J. Sánchez Egea, H. A. González Rojas, M. J. Martí, Y. Compta, F. Valldeoriola, E. S. Mezquita, E. Tolosa, and J. Valls-Solè, ``Differential diagnosis between Parkinson's disease and essential tremor using the smartphone's accelerometer,'' PLoS ONE, vol. 12, no. 8, Aug. 2017, Art. no. e0183843, doi: 10.1371/journal.pone.0183843. [5] P. Locatelli and D. Alimonti, ``Differentiating essential tremor and Parkinson's disease using a wearable sensor A pilot study,'' in Proc. 7th IEEE Int. Workshop Adv. Sensors Interfaces (IWASI), Jun. 2017, pp. 213 218, doi: 10.1109/IWASI.2017.7974254. [6] D. B. Miller and J. P. O'Callaghan, ``Biomarkers of Parkinson's disease: Present and future,'' Metabolism, vol. 64, no. 3, pp. S40 S46, Mar. 2015, doi: 10.1016/j.metabol.2014.10.030. [7] S. K. Nanda, W.-Y. Lin, M.-Y. Lee, and R.-S. Chen, ``A quantitative classi cation of essential and Parkinson's tremor using wavelet transform and arti cial neural network on sEMG and accelerometer signals,'' in Proc. IEEE 12th Int. Conf. Netw., Sens. Control, Apr. 2015, pp. 399 404, doi: 10.1109/ICNSC.2015.7116070. [8] D. Surangsrirat, C. Thanawattano, R. Pongthornseri, S. Dumnin, C. Anan, and R. Bhidayasiri, ``Support vector machine classi cation of Parkinson's disease and essential tremor subjects based on temporal uctuation,'' in Proc. 38th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Aug. 2016, pp. 6389 6392, doi: 10.1109/EMBC.2016.7592190. [9] F. Papengut, J. Raethjen, A. Binder, and G. Deuschl, ``Rest tremor suppression may separate essential from parkinsonian rest tremor,'' Parkin- sonism Rel. Disorders, vol. 19, no. 7, pp. 693 697, Jul. 2013, doi: 10.1016/j.parkreldis.2013.03.013. [10] K. Uchida, M. Hirayama, F. Yamashita, N. Hori, T. Nakamura, and G. Sobue, ``Tremor is attenuated during walking in essential tremor with resting tremor but not parkinsonian tremor,'' J. Clin. Neurosci., vol. 18, no. 9, pp. 1224 1228, Sep. 2011, doi: 10.1016/j.jocn.2010.12.053. [11] M. Algarni and A. Fasano, ``The overlap between essential tremor and parkinson disease,'' Parkinsonism Rel. Disorders, vol. 46, pp. S101 S104, Jan. 2018, doi: 10.1016/j.parkreldis.2017.07.006. [12] E. Nikfekr, K. Kerr, S. Att eld, and E. D. Playford, ``Trunk movement in Parkinson's disease during rising from seated position,'' Movement Disor- ders, vol. 17, no. 2, pp. 274 282, Mar. 2002, doi: 10.1002/mds.10073. [13] G. Serrancolí, J. M. Font-Llagunes, and A. Barjau, ``A weighted cost function to deal with the muscle force sharing problem in injured subjects: A single case study,'' Proc. Inst. Mech. Eng., K, J. Multi-body Dyn., vol. 228, no. 3, pp. 241 251, Sep. 2014, doi: 10.1177/1464419314530110. [14] F. P. Bernhard, J. Sartor, K. Bettecken, M. A. Hobert, C. Arnold, Y. G. Weber, S. Poli, N. G. Margraf, C. Schlenstedt, C. Hansen, and W. Maetzler, ``Wearables for gait and balance assessment in the neurological ward study design and rst results of a prospective cross-sectional feasibility study with 384 inpatients,'' BMC Neurol., vol. 18, no. 1, p. 114, Dec. 2018, doi: 10.1186/s12883-018-1111-7. [15] D. J. Wile, R. Ranawaya, and Z. H. T. Kiss, ``Smart watch accelerometry for analysis and diagnosis of tremor,'' J. Neurosci. Methods, vol. 230, pp. 1 4, Jun. 2014, doi: 10.1016/j.jneumeth.2014.04.021. [16] G. Kramer, A. M. M. Van der Stouwe, N. M. Maurits, M. A. J. Tijssen, and J.W. J. Elting, ``Wavelet coherence analysis:Anewapproach to distinguish organic and functional tremor types,'' Clin. Neurophysiol., vol. 129, no. 1, pp. 13 20, Jan. 2018, doi: 10.1016/j.clinph.2017.10.002. [17] M. A. Raza, Q. Chaudry, S. M. T. Zaidi, and M. B. Khan, ``Clinical decision support system for Parkinson's disease and related movement disorders,'' in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), Mar. 2017, pp. 1108 1112, doi: 10.1109/ICASSP.2017.7952328. [18] H. A. González Rojas, P. C. Cuevas, E. E. Zayas Figueras, S. C. Foix, and A. J. Sánchez Egea, ``Time measurement characterization of standto- sit and sit-to-stand transitions by using a smartphone,'' Med. Biol. Eng. Comput., vol. 56, no. 5, pp. 879 888, May 2018, doi: 10.1007/s11517-017- 1728-5. [19] J. D. Loaiza Duque, A. M. González-Vargas, A. J. Sánchez Egea, and H. A. González Rojas, ``Using machine learning and accelerometry data for differential diagnosis of parkinson's disease and essential tremor,'' in Applied Computer Sciences in Engineering (Communications in Computer and Information Science), vol. 1052. Springer, 2019, pp. 368 378. [20] Sensorlog (Version 1.9.4) Mobile Application Software. Accessed: 2017. [Online]. Available: http://itunes.apple.com [21] S. Padma G, S. Umesh, U. Asokan, and T. Srinivas, ``Parkinsonian hand tremor measurement device based on ber Bragg grating sensor,'' in Proc. Int. Conf. Smart Sensors Syst. (IC-SSS), Dec. 2015, pp. 3 5, doi: 10.1109/ SMARTSENS.2015.7873611. [22] F. Hopfner and R. C. Helmich, ``The etiology of essential tremor: Genes versus environment,'' Parkinsonism Rel. Disorders, vol. 46, pp. S92 S96, Jan. 2018, doi: 10.1016/j.parkreldis.2017.07.014. [23] T. Novak and K. M. Newell, ``Physiological tremor (8 12 Hz component) in isometric force control,'' Neurosci. Lett., vol. 641, pp. 87 93, Feb. 2017, doi: 10.1016/j.neulet.2017.01.034. [24] E. D. Louis, ``Essential tremor then and now: How views of the most common tremor diathesis have changed over time,'' Parkinsonism Rel. Disorders, vol. 46, pp. S70 S74, Jan. 2018, doi: 10.1016/j.parkreldis.2017. 07.010. [25] O. Martinez Manzanera, J. W. Elting, J. H. van der Hoeven, and N. M. Maurits, ``Tremor detection using parametric and non-parametric spectral estimation methods: A comparison with clinical assessment,'' PLoS ONE, vol. 11, no. 6, Jun. 2016, Art. no. e0156822, doi: 10.1371/journal. pone.0156822. [26] I. Guyon and A. Elisseeff, ``An introduction to variable and feature selection,'' J. Mach. Learn. Res., vol. 3, pp. 1157 1182, Jan. 2003, doi: 10.1162/153244303322753616. [27] N. Kostikis, D. Hristu-Varsakelis, M. Arnaoutoglou, and C. Kotsavasiloglou, ``A smartphone-based tool for assessing parkinsonian hand tremor,'' IEEE J. Biomed. Health Informat., vol. 19, no. 6, pp. 1835 1842, Nov. 2015, doi: 10.1109/JBHI.2015.2471093. [28] I. S. Thaseen and C. A. Kumar, ``Intrusion detection model using fusion of chi-square feature selection and multi class SVM,'' J. King Saud Univ. Comput. Inf. Sci., vol. 29, no. 4, pp. 462 472, Oct. 2017, doi: 10.1016/j.jksuci.2015.12.004. [29] L. M. Gil, T. P. Nunes, F. H. S. Silva, A. C. D. Faria, and P. L. Melo, ``Analysis of human tremor in patients with parkinson disease using entropy measures of signal complexity,'' in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol., Aug. 2010, pp. 2786 2789, doi: 10.1109/IEMBS.2010. 5626365. [30] B. Zhang, F. Huang, J. Liu, and D. Zhang, ``A novel posture for better differentiation between Parkinson's tremor and essential tremor,'' Frontiers Neurosci., vol. 12, p. 317, May 2018, doi: 10.3389/fnins.2018.00317. [31] J. Marjama-Lyons and W. Koller, ``Tremor-predominant parkinson's disease,'' Drugs Aging, vol. 16, no. 4, pp. 273 278, Apr. 2000, doi: 10.2165/ 00002512-200016040-00003. [32] A. P. Duker and A. J. Espay, ``Surgical treatment of parkinson disease,'' Neurologic Clinics, vol. 31, no. 3, pp. 799 808, Aug. 2013, doi: 10.1016/ j.ncl.2013.03.007. |
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Sanchez Egea, Antonio J.8d1203677ed9f9fe4208a50977068d17Reeb, Theresa434ef56abbc04b5f0daae9ce35b9b5baGonzález, Hernán Alberto96fe37656e36ae65ae4ec3ee77efe45cLoaiza Duque, Julián Davide08298c86a6f5b7fe934bdc7695a8fdcGonzález Vargas, Andrés Mauriciovirtual::2068-1IEEE Access2021-10-29T16:01:03Z2021-10-29T16:01:03Z2020-05-1121693536https://hdl.handle.net/10614/1338110.1109/ACCESS.2020.2993647Recent research has shown that smartphones/smartwatches have a high potential to help physicians to identify and differentiate between different movement disorders. This work aims to develop Machine Learning models to improve the differential diagnosis between patients with Parkinson’s Disease and Essential Tremor. For this purpose, we use a mobile phone’s built-in gyroscope to record the angular velocity signals of two different arm positions during the patient’s follow-up, more precisely, in rest and posture positions. To develop and to find the best classification models, diverse factors were considered, such as the frequency range, the training and testing divisions, the kinematic features, and the classification method. We performed a two-stage kinematic analysis, first to differentiate between healthy and trembling subjects and then between patients with Parkinson’s Disease and Essential Tremor. The models developed reached an average accuracy of 97.2 ± 3.7% (98.5% Sensitivity, 93.3% Specificity) to differentiate between Healthy and Trembling subjects and an average accuracy of 77.8 ± 9.9% (75.7% Sensitivity, 80.0% Specificity) to discriminate between Parkinson’s Disease and Essential Tremor patients. Therefore, we conclude, that the angular velocity signal can be used to develop Machine Learning models for the differential diagnosis of Parkinson’s disease and Essential Tremor10 páginasapplication/pdfengIEEEDerechos reservados - IEEE, 2020https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremorArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Diagnóstico diferencialGiroscopiosRedes de área corporal (Electrónica)Body area networksDifferential diagnosisParkinson’s diseaseEssential tremorgyroscopeKinematic analysisMachine learningVolumen 8 (2020)88875888668Loaiza Duque, J.D., Sánchez Egea, A. J., Reeb, T., González Rojas, H.A., González Vargas, A. M. (2020). Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson’s disease and essential tremor. IEEE Access. (Vol. 8, pp. 88866–88875 doi: 10.1109/ACCESS.2020.2993647IEEE Xplore[1] K. P. Bhatia, P. Bain, N. Bajaj, R. J. Elble, M. Hallett, E. D. Louis, J. Raethjen, M. Stamelou, C. M. Testa, and G. Deuschl, ``Consensus statement on the classi cation of tremors. From the task force on tremor of the international parkinson and movement disorder society,'' Move- ment Disorders, vol. 33, no. 1, pp. 75 87, Jan. 2018, doi: 10.1002/mds. 27121.[2] C. Bhavana, J. Gopal, P. Raghavendra, K. M. Vanitha, and V. Talasila, ``Techniques of measurement for Parkinson's tremor highlighting advantages of embedded IMU over EMG,'' in Proc. Int. Conf. Recent Trends Inf. Technol. (ICRTIT), Apr. 2016, pp. 1 5, doi: 10.1109/ICRTIT.2016. 7569560.[3] A. M. Woods, M. Nowostawski, E. A. Franz, and M. Purvis, ``Parkinson's disease and essential tremor classi cation on mobile device,'' Per- vas. Mobile Comput., vol. 13, pp. 1 12, Aug. 2014, doi: 10.1016/j.pmcj. 2013.10.002.[4] S. Barrantes, A. J. Sánchez Egea, H. A. González Rojas, M. J. Martí, Y. Compta, F. Valldeoriola, E. S. Mezquita, E. Tolosa, and J. Valls-Solè, ``Differential diagnosis between Parkinson's disease and essential tremor using the smartphone's accelerometer,'' PLoS ONE, vol. 12, no. 8, Aug. 2017, Art. no. e0183843, doi: 10.1371/journal.pone.0183843.[5] P. Locatelli and D. Alimonti, ``Differentiating essential tremor and Parkinson's disease using a wearable sensor A pilot study,'' in Proc. 7th IEEE Int. Workshop Adv. Sensors Interfaces (IWASI), Jun. 2017, pp. 213 218, doi: 10.1109/IWASI.2017.7974254.[6] D. B. Miller and J. P. O'Callaghan, ``Biomarkers of Parkinson's disease: Present and future,'' Metabolism, vol. 64, no. 3, pp. S40 S46, Mar. 2015, doi: 10.1016/j.metabol.2014.10.030.[7] S. K. Nanda, W.-Y. Lin, M.-Y. Lee, and R.-S. Chen, ``A quantitative classi cation of essential and Parkinson's tremor using wavelet transform and arti cial neural network on sEMG and accelerometer signals,'' in Proc. IEEE 12th Int. Conf. Netw., Sens. Control, Apr. 2015, pp. 399 404, doi: 10.1109/ICNSC.2015.7116070.[8] D. Surangsrirat, C. Thanawattano, R. Pongthornseri, S. Dumnin, C. Anan, and R. Bhidayasiri, ``Support vector machine classi cation of Parkinson's disease and essential tremor subjects based on temporal uctuation,'' in Proc. 38th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Aug. 2016, pp. 6389 6392, doi: 10.1109/EMBC.2016.7592190.[9] F. Papengut, J. Raethjen, A. Binder, and G. Deuschl, ``Rest tremor suppression may separate essential from parkinsonian rest tremor,'' Parkin- sonism Rel. Disorders, vol. 19, no. 7, pp. 693 697, Jul. 2013, doi: 10.1016/j.parkreldis.2013.03.013.[10] K. Uchida, M. Hirayama, F. Yamashita, N. Hori, T. Nakamura, and G. Sobue, ``Tremor is attenuated during walking in essential tremor with resting tremor but not parkinsonian tremor,'' J. Clin. Neurosci., vol. 18, no. 9, pp. 1224 1228, Sep. 2011, doi: 10.1016/j.jocn.2010.12.053.[11] M. Algarni and A. Fasano, ``The overlap between essential tremor and parkinson disease,'' Parkinsonism Rel. Disorders, vol. 46, pp. S101 S104, Jan. 2018, doi: 10.1016/j.parkreldis.2017.07.006.[12] E. Nikfekr, K. Kerr, S. Att eld, and E. D. Playford, ``Trunk movement in Parkinson's disease during rising from seated position,'' Movement Disor- ders, vol. 17, no. 2, pp. 274 282, Mar. 2002, doi: 10.1002/mds.10073.[13] G. Serrancolí, J. M. Font-Llagunes, and A. Barjau, ``A weighted cost function to deal with the muscle force sharing problem in injured subjects: A single case study,'' Proc. Inst. Mech. Eng., K, J. Multi-body Dyn., vol. 228, no. 3, pp. 241 251, Sep. 2014, doi: 10.1177/1464419314530110.[14] F. P. Bernhard, J. Sartor, K. Bettecken, M. A. Hobert, C. Arnold, Y. G. 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Espay, ``Surgical treatment of parkinson disease,'' Neurologic Clinics, vol. 31, no. 3, pp. 799 808, Aug. 2013, doi: 10.1016/ j.ncl.2013.03.007.GeneralPublication6053e64e-a34d-4652-8fa2-6c0440556f15virtual::2068-16053e64e-a34d-4652-8fa2-6c0440556f15virtual::2068-1https://scholar.google.com.co/citations?user=oj5Tle8AAAAJ&hl=esvirtual::2068-10000-0001-6393-7130virtual::2068-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001345355virtual::2068-1LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/56abebc5-42e2-4d7b-8e39-aecf074810bd/download20b5ba22b1117f71589c7318baa2c560MD52TEXTAngular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson¿s disease and essential tremor.pdf.txtAngular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson¿s disease and essential tremor.pdf.txtExtracted texttext/plain45787https://red.uao.edu.co/bitstreams/b2f9986d-eb36-4c63-bbe1-eec173db478f/download63dab57faee0dc597d1ba15dde61e7dbMD54THUMBNAILAngular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson¿s disease and essential tremor.pdf.jpgAngular velocity analysis boosted by machine learning for helping in the differential diagnosis of parkinson¿s disease and essential tremor.pdf.jpgGenerated Thumbnailimage/jpeg17549https://red.uao.edu.co/bitstreams/3e9905f4-2aab-4190-965c-0a63899e852e/downloadebf40fed42881decef5e0875197d3edeMD5510614/13381oai:red.uao.edu.co:10614/133812024-03-06 09:36:46.012https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - IEEE, 2020metadata.onlyhttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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 |