Elucidating factors influencing machine learning algorithm prediction in spasticity assessment: a prospective observational study

References

• Biering-Sørensen F, Nielsen J, Klinge K. 2006. Spasticity-assessment: a review. Spinal Cord. 44(12):708–722.
   PubMed Web of Science ®Google Scholar
• Blackburn M, van Vliet P, Mockett SP. 2002. Reliability of measurements obtained with the modified Ashworth Scale in the lower extremities of people with stroke. Phys Therapy. 82(1):25–34.
   PubMed Web of Science ®Google Scholar
• Blonde L, Khunti K, Harris SB, Meizinger C, Skolnik NS. 2018. Interpretation and impact of real-world clinical data for the practicing clinician. Adv Ther. 35(11):1763–1774.
   PubMed Web of Science ®Google Scholar
• Chandra S, Afsharipour B, Rymer W, Suresh N. 2020. Precise quantification of the time course of voluntary activation capacity following Botulinum toxin injections in the biceps brachii muscles of chronic stroke survivors. J NeuroEngineering Rehabil. 17(1):1–17.
   PubMed Web of Science ®Google Scholar
• Chang EY, Ghosh N, Yanni D, Lee S, Alexandru D, Mozaffar T. 2013. A review of spasticity treatments: pharmacological and interventional approaches. Crit Rev Phys Rehabil Med. 25:1–2.
   Google Scholar
• Escaldi SV, Cuccurullo SJ, Terzella M, Petagna AM, Strax TE. 2012. Assessing competency in spasticity management: a method of development and assessment. Am J Phys Med Rehabil. 91(3):243–253.
   PubMed Web of Science ®Google Scholar
• Fleuren JFM, Voerman GE, Erren-Wolters CV, Snoek GJ, Rietman JS, Hermens HJ, Nene AV. 2010. Stop using the Ashworth Scale for the assessment of spasticity. J Neurol Neurosurg Psychiatry. 81(1):46–52.
   PubMed Web of Science ®Google Scholar
• Gharbaoui I, Kania K, Cole P, editors. 2016. Spastic paralysis of the elbow and forearm. Seminars in plastic surgery (Vol. 30, No. 01, pp. 39–44). Thieme Medical Publishers.
   Google Scholar
• Gudivada V, Apon A, Ding J. 2017. Data quality considerations for big data and machine learning: going beyond data cleaning and transformations. Int J Adv Softw. 10(1):1–20.
   Google Scholar
• Keenan MAE, Haider TT, Stone LR. 1990. Dynamic electromyography to assess elbow spasticity. J Hand Surg Am. 15(4):607–614.
   PubMedGoogle Scholar
• Kim J-Y, Park G, Lee S-A, Nam Y. 2020. Analysis of machine learning-based assessment for elbow spasticity using inertial sensors. Sensors. 20(6):1622.
   PubMed Web of Science ®Google Scholar
• Kumar RT, Pandyan AD, Sharma AK. 2006. Biomechanical measurement of post-stroke spasticity. Age Ageing. 35(4):371–375.
   PubMed Web of Science ®Google Scholar
• Lee H-M, Huang Y-Z, Chen JJ, Hwang I-S. 2002. Quantitative analysis of the velocity related pathophysiology of spasticity and rigidity in the elbow flexors. J Neurol Neurosurg Psychiatry. 72(5):621–629.
   PubMed Web of Science ®Google Scholar
• Lonini L, Shawen N, Ghaffari R, Rogers J, Jayarman A. editors. 2017. Automatic detection of spasticity from flexible wearable sensors. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers.
   Google Scholar
• Lynn B-O, Erwin A, Guy M, Herman B, Davide M, Ellen J, Anne C, Kaat D. 2013. Comprehensive quantification of the spastic catch in children with cerebral palsy. Res Dev Disabil. 34(1):386–396.
   PubMed Web of Science ®Google Scholar
• Mehrholz J, Wagner K, Meißner D, Grundmann K, Zange C, Koch R, Pohl M. 2005. Reliability of the Modified Tardieu Scale and the Modified Ashworth Scale in adult patients with severe brain injury: a comparison study. Clin Rehabil. 19(7):751–759.
   PubMed Web of Science ®Google Scholar
• Mukherjee A, Chakravarty A. 2010. Spasticity mechanisms–for the clinician. Front Neurol. 1:149.
   PubMedGoogle Scholar
• Naghdi S, Ansari NN, Abolhasani H, Mansouri K, Ghotbi N, Hasson S. 2014. Electrophysiological evaluation of the Modified Tardieu Scale (MTS) in assessing poststroke wrist flexor spasticity. NeuroRehabilitation. 34(1):177–184.
   PubMed Web of Science ®Google Scholar
• Nielsen JB, Crone C, Hultborn H. 2007. The spinal pathophysiology of spasticity-from a basic science point of view. Acta Physiol (Oxf). 189(2):171–180.
   PubMed Web of Science ®Google Scholar
• Obermeyer Z, Emanuel EJ. 2016. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med. 375(13):1216–1219.
   PubMed Web of Science ®Google Scholar
• Park J-H, Kim Y, Lee K-J, Yoon Y-S, Kang SH, Kim H, Park H-S. 2019. Artificial neural network learns clinical assessment of spasticity in modified Ashworth Scale. Arch Phys Med Rehabil. 100(10):1907–1915.
   PubMed Web of Science ®Google Scholar
• Pisano F, Miscio G, Del Conte C, Pianca D, Candeloro E, Colombo R. 2000. Quantitative measures of spasticity in post-stroke patients. Clin Neurophysiol. 111(6):1015–1022.
   PubMed Web of Science ®Google Scholar
• Rivelis Y, Morice K. Spasticity. 2018.
   Google Scholar
• Sheean G, McGuire JR. 2009. Spastic hypertonia and movement disorders: pathophysiology, clinical presentation, and quantification. PM R. 1(9):827–833.
   PubMedGoogle Scholar
• Singh P, Joshua AM, Ganeshan S, Suresh S. 2011. Intra-rater reliability of the modified Tardieu scale to quantify spasticity in elbow flexors and ankle plantar flexors in adult stroke subjects. Ann Indian Acad Neurol. 14(1):23.
   PubMed Web of Science ®Google Scholar
• Sloan R, Sinclair E, Thompson J, Taylor S, Pentland B. 1992. Inter-rater reliability of the modified Ashworth Scale for spasticity in hemiplegic patients. Int J Rehabil Res. 15(2):158–161.
   PubMedGoogle Scholar
• Tang C, Ji J, Tang Y, Gao S, Tang Z, Todo Y. 2020. A novel machine learning technique for computer-aided diagnosis. Eng Appl Artif Intell. 92:103627.
   Web of Science ®Google Scholar
• Wang W, Kiik M, Peek N, Curcin V, Marshall IJ, Rudd AG, Wang Y, Douiri A, Wolfe CD, Bray B, et al. 2020. A systematic review of machine learning models for predicting outcomes of stroke with structured data. PLoS One. 15(6):e0234722.
   PubMed Web of Science ®Google Scholar
• Zhang R, Wang Y, Liu B, Song G, Zhou X, Fan S, Pan X. 2014. Clinical data quality problems and countermeasure for real world study. Front Med. 8(3):352–357.
   PubMed Web of Science ®Google Scholar
• Zhang X, Tang X, Zhu X, Gao X, Chen X, Chen X. 2019. A regression-based framework for quantitative assessment of muscle spasticity using combined EMG and inertial data from wearable sensors. Front Neurosci. 13:398.
   PubMed Web of Science ®Google Scholar