Wavelet applications in medicine
IEEE Spectrum
Handbook of Chemometrics and Qualimetrics: Part A
Handbook of Chemometrics and Qualimetrics: Part A
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Neural Networks in a Softcomputing Framework
Neural Networks in a Softcomputing Framework
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Cluster and Classification Techniques for the Biosciences
Cluster and Classification Techniques for the Biosciences
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Fuzzy support vector machine for multi-class text categorization
Information Processing and Management: an International Journal
A two-stage method for MUAP classification based on EMG decomposition
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
A novel method for automated EMG decomposition and MUAP classification
Artificial Intelligence in Medicine
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
Expert Systems with Applications: An International Journal
Wavelets and filter banks: theory and design
IEEE Transactions on Signal Processing
IEEE Transactions on Information Technology in Biomedicine
Classification of EMG signals using combined features and soft computing techniques
Applied Soft Computing
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Computer Methods and Programs in Biomedicine
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders
Computers in Biology and Medicine
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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The motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide a significant source of information for the assessment of neuromuscular disorders. In this work, different types of machine learning methods were used to classify EMG signals and compared in relation to their accuracy in classification of EMG signals. The models automatically classify the EMG signals into normal, neurogenic or myopathic. The best averaged performance over 10 runs of randomized cross-validation is also obtained by different classification models. Some conclusions concerning the impacts of features on the EMG signal classification were obtained through analysis of the classification techniques. The comparative analysis suggests that the fuzzy support vector machines (FSVM) modelling is superior to the other machine learning methods in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. The combined model with discrete wavelet transform (DWT) and FSVM achieves the better performance for internal cross validation (External cross validation) with the area under the reciever operating characteristic (ROC) curve (AUC) and accuracy equal to 0.996 (0.970) and 97.67% (93.5%), respectively. These results show that the proposed model have the potential to obtain a reliable classification of EMG signals, and to assist the clinicians for making a correct diagnosis of neuromuscular disorders.