Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders

Abstract

This article presents the design and validation of an accurate automatic diagnostic system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy categories to aid the diagnosis of neuromuscular diseases. Methods: First, an iEMG signal is decimated to produce a set of "disjoint" downsampled signals, which are decomposed by the lifting wavelet transform (LWT). The Higuchi's fractal dimensions (FDs) of LWT coefficients in the subbands are computed. The FDs of LWT subband coefficients are fused with one-dimensional local binary pattern derived from each downsampled signal. Next, a multilayer perceptron neural network (MLPNN) determines the class labels of downsampled signals. Finally, the sequence of class labels is fed to the Boyer-Moore majority vote (BMMV) algorithm, which assigns a class to every iEMG signal. Results: The MLPNN-BMMV classifier was experimented with 250 iEMG signals belonging to three categories. The performance of the classifier was validated in comparison with state-of-the-art approaches. The MLPNN-BMMV has resulted in impressive performance measures (%) using a 10-fold cross-validation-accuracy = 99.87 ± 0.25, sensitivity (normal) = 99.97 ± 0.13, sensitivity (myopathy) = 99.68 ± 0.95, sensitivity (neuropathy) = 99.76 ± 0.66, specificity (normal) = 99.72 ± 0.61, specificity (myopathy) = 99.98 ± 0.10, and specificity (neuropathy) = 99.96 ± 0.14-surpassing the existing approaches. Conclusions: A future research direction is to validate the classifier performance with diverse iEMG datasets, which would lead to the design of an affordable real-time expert system for neuromuscular disorder diagnosis. Index Terms-Fractal dimension, intramuscular electromyography, lifting wavelet transform, local binary pattern, majority vote, multilayer perceptron neural network, neuromuscular disorders. Impact Statement-An iEMG classifier is designed and validated with iEMG signals from healthy, myopathy, and neuropathy subjects. The classifier's outstanding performance is a step toward real-time diagnostic system for neuromuscular diseases.

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

“…Therefore, they are widely utilized in neuromuscular evaluations of clinical treatments. [ 35,36 ] However, this technique is highly invasive, with risks of harming the patients and affecting local detection property. Also, the implementation of iEMG is limited to certified professionals and is conducted under strict supervision, both of which limit its overall accessibility.…”

Frekans Alanı Öznitelik Çıkarımına Dayalı Makine Öğrenme Teknikleri ile Trapezius Yüzey EMG Sinyallerini Kullanarak Servikal Disk Fıtığı Hastalığının Tahmini

“…In the literature, some studies classify neuromuscular diseases [10][11][12], detect muscle activity [13][14], muscle fatigue [15][16], and classification of low back pain [17][18] and neck pain [19][20][21] using sEMG. To our knowledge, no study classifies CDH patients using surface EMG, except [22].…”

Intramuscular EMG feature extraction and evaluation at different arm positions and hand postures based on a statistical criterion method

“…8 On the contrary, iEMG signal acquisition is carried out by utilizing monopolar and concentric needle electrodes which are injected over the skin and into the muscle of interest. 9 Although, EMG signals tend to consist of useful information that can be used for controlling prosthesis such as prosthetic arm, they also comprise of noise and interferences as well. 10 Nonetheless, along with imparting noise, there is an additional issue relating to extracting the optimal features from the signals.…”