Opportunities and challenges of supervised machine learning for the classification of motor evoked potentials according to muscles

被引:0
作者
Jonathan Wermelinger
Qendresa Parduzi
Murat Sariyar
Andreas Raabe
Ulf C. Schneider
Kathleen Seidel
机构
[1] Bern University Hospital,Department of Neurosurgery, Inselspital
[2] and University of Bern,Department of Neurosurgery
[3] Lucerne Cantonal Hospital,School of Engineering and Computer Science
[4] Bern University of Applied Sciences,undefined
来源
BMC Medical Informatics and Decision Making | / 23卷
关键词
Machine learning; Intraoperative neurophysiological monitoring; Motor evoked potential; Random forest; Time series data;
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[21]  
Raabe A(2011)Scikit-learn: Machine Learning in {P}ython J Mach Learn Res 12 2825-20
[22]  
Macdonald DB(1967)Nearest neighbor pattern classification IEEE Trans Inf Theory 13 21-239
[23]  
Skinner S(1951)Why I prefer logits to probits Biometrics 7 327-1284
[24]  
Shils J(2002)SMOTE: synthetic minority over-sampling technique J Artif Intell Res 16 321-378
[25]  
Yingling C(2019)A Review of Dimensionality Reduction Techniques for Efficient Computation Procedia Computer Sci 165 104-E900
[26]  
Asimakidou E(2021)A review of principal component analysis algorithm for dimensionality reduction J Soft Comput Data Mining 2 20-7
[27]  
Abut PA(2017)Data quality considerations for big data and machine learning: going beyond data cleaning and transformations Int J Adv Softw 10 1-1144
[28]  
Raabe A(2017)Learning from class-imbalanced data: review of methods and applications Expert Syst Appl 73 220-216
[29]  
Seidel K(2018)Ensemble learning: a survey Wiley Interdiscip Rev 8 e1249-12
[30]  
Szelényi A(2009)Learning from Imbalanced Data IEEE Trans Knowl Data Eng 21 1263-undefined