The Application of the Machine Learning Method in Electromyographic Data

Abstract

This paper studies the application of machine learning in the analysis and diagnosis of electromyography data. Firstly, 2,352 electromyography examination reports have been recorded from Sichuan Provincial Hospital of Traditional Chinese Medicine for ten months. The data cleaning has been conducted based on the specific-designed inclusion criteria. Next, two data sets have been established, containing 575 facial motor nerve conduction study reports and 233 auditory brainstem response reports, respectively. And then, four machine learning algorithms including random forest, linear regression, support vector machine and logistic regression have been employed to the data sets. The performance comparisons of accuracy and recall rate among different algorithms indicate that the random forest algorithm has the optimal performance over the other two in both data sets. Moreover, the comparisons have been carried out in the cases with and without deviation standardization for each algorithm, and the results demonstrate that the deviation standardization has a certain effect on the accuracy improvement. Additionally, it is found that the random forest algorithm can present the ranking of the features in order of importance. Consequently, the random forest is proven to be an optimal algorithm for computer-aided diagnosis systems. Furthermore, it is worth mentioning that the feature ranking in order of importance can facilitate clinical diagnosis and has a certain clinical potential in diagnosis and diagnostic assessment.

Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning

“…Recently, machine learning based methods have found widespread use in healthcare, revolutionizing the area of medical diagnosis [ 25 , 26 , 27 ]. To this end, previous attempts for the automatic diagnosis of neuropathies and CTS through machine learning NCS signal processing have shown promising results [ 28 , 29 ]. However, these studies are focused exclusively in the analysis of motor nerve conduction parameters, having — in the case of CTS — the inherent drawback of the wide variation of compound muscle action potentials (CMAP) related to muscle contraction and hand position [ 30 ].…”

An Intelligent Data Mining-Based Fault Detection and Classification Strategy for Microgrid

“…Logistic regression is based on regression analysis and is used to detect the fault in the microgrid for the proposed scheme. It is employed to investigate the relationship between numerous independent variables [43], [44]. It predicts the output of an estimated expected value from a categorical dependent binary variable [45].…”

Predicting Patent Transactions Using Patent-Based Machine Learning Techniques

“…For modelling, we used five different machine learning techniques-logistic regression (LR) [43,51], naive Bayes classifier (NB) [44], support vector machine (SVM) [45][46][47], random forest (RF) [48,54], and deep neural network (DNN) [49] to compare the results. The brief explanation of five machine learning techniques is provided in Appendix A.…”

“…Logistic regression is a common probabilistic nonlinear regression model and its dependent variables can only be either 0 or 1 [51]. In this model, a single outcome variable Yi (i = 1,..., n) follows a Bernoulli distribution that takes on the value 1 with probability πi and 0 with probability 1 − πi .…”