Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification.

Emine Yaman,Abdulhamit Subasi

doi:10.1155/2019/9152506

Emine Yaman, Abdulhamit Subasi

Open Access

https://doi.org/10.1155/2019/9152506

Copy DOI

Abstract

The neuromuscular disorders are diagnosed using electromyographic (EMG) signals. Machine learning algorithms are employed as a decision support system to diagnose neuromuscular disorders. This paper compares bagging and boosting ensemble learning methods to classify EMG signals automatically. Even though ensemble classifiers' efficacy in relation to real-life issues has been presented in numerous studies, there are almost no studies which focus on the feasibility of bagging and boosting ensemble classifiers to diagnose the neuromuscular disorders. Therefore, the purpose of this paper is to assess the feasibility of bagging and boosting ensemble classifiers to diagnose neuromuscular disorders through the use of EMG signals. It should be understood that there are three steps to this method, where the step number one is to calculate the wavelet packed coefficients (WPC) for every type of EMG signal. After this, it is necessary to calculate statistical values of WPC so that the distribution of wavelet coefficients could be demonstrated. In the last step, an ensemble classifier used the extracted features as an input of the classifier to diagnose the neuromuscular disorders. Experimental results showed the ensemble classifiers achieved better performance for diagnosis of neuromuscular disorders. Results are promising and showed that the AdaBoost with random forest ensemble method achieved an accuracy of 99.08%, F-measure 0.99, AUC 1, and kappa statistic 0.99.

Highlights

Academic Editor: Nasimul Noman e neuromuscular disorders are diagnosed using electromyographic (EMG) signals
It is essential to carry out analysis of motor unit action potential (MUAP), quantitatively, to detect these variabilities in the abnormal patterns. e wavelet transform mainly employed for the analysis of the time series shows nonstationary characteristics [9,10,11]. e appropriate feature extraction technique is needed for achieving a better classification performance
When we checked classification accuracies of ensemble methods, LADTree gave minimum performance with 88.33% (Table 2) in bagging ensemble learning method, and for all of other ensemble learning methods, minimum performance is achieved by NB, 89.54% in AdaBoost method (Table 3) and 88.54% in MultiBoosting method (Table 4). e best performance is achieved by Random Forests (RF) with 98.54% classification accuracy from single classifiers

Summary

Introduction

Academic Editor: Nasimul Noman e neuromuscular disorders are diagnosed using electromyographic (EMG) signals. EMG signals deliver information about the functioning and status of the muscles that can be utilized for the diagnosis of neuromuscular disorders such as myopathy and neuropathy. Discrimination of EMG signals is crucial to diagnose the neuromuscular disorders Numerous attributes, such as the quality of the signals, the efficiency of the feature extraction methods and classifiers, and the training and testing datasets, may influence the accuracy of EMG signal classification. E complication is to create an accurate and effective decision support system which keeps crucial discriminatory information to achieve better classification accuracy In this respect, it is necessary to conduct a systematic analysis of EMG signals in order to obtain an efficient classification of EMG. This paper analyzes the effectiveness of single classifiers with bagging and boosting ensemble learning algorithms for the EMG signal classification. Ensemble classifiers are inherently parallel, so they can be more effective at training and test phases if they can approach multiple processors [31]

Objectives

Methods

Results

Discussion

Conclusion