Neuromuscular disease detection by neural networks and fuzzy entropy on time‐frequency analysis of electromyography signals

Vallejo, Marcela; Gallego, Carlos J.; Duque-Muñoz, Leonardo; Delgado-Trejos, Edilson

doi:10.1111/exsy.12274

Cited by 27 publications

(20 citation statements)

References 22 publications

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“…Since not only the quantity of attributes may play an important role, but also their quality, a relevance analysis to the ordinal patterns for

(6 patterns) obtained when processing the PAF database was applied. Relevance analysis aims to reduce the complexity in a representation space, removing redundant and/or irrelevant information according to an objective function, in order to improve classification performance and discover the intrinsic information for decision support purposes [ 59 ]. In this paper, a relevance analysis routine based on the RELIEF-F algorithm was used to highlight the most discriminant patterns [ 60 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Cuesta–Frau

Murillo-Escobar

Orrego

et al. 2019

Entropy

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Permutation Entropy (PE) is a time series complexity measure commonly used in a variety of contexts, with medicine being the prime example. In its general form, it requires three input parameters for its calculation: time series length N, embedded dimension m, and embedded delay τ . Inappropriate choices of these parameters may potentially lead to incorrect interpretations. However, there are no specific guidelines for an optimal selection of N, m, or τ , only general recommendations such as N > > m ! , τ = 1 , or m = 3 , … , 7 . This paper deals specifically with the study of the practical implications of N > > m ! , since long time series are often not available, or non-stationary, and other preliminary results suggest that low N values do not necessarily invalidate PE usefulness. Our study analyses the PE variation as a function of the series length N and embedded dimension m in the context of a diverse experimental set, both synthetic (random, spikes, or logistic model time series) and real–world (climatology, seismic, financial, or biomedical time series), and the classification performance achieved with varying N and m. The results seem to indicate that shorter lengths than those suggested by N > > m ! are sufficient for a stable PE calculation, and even very short time series can be robustly classified based on PE measurements before the stability point is reached. This may be due to the fact that there are forbidden patterns in chaotic time series, not all the patterns are equally informative, and differences among classes are already apparent at very short lengths.

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“…Since not only the quantity of attributes may play an important role, but also their quality, a relevance analysis to the ordinal patterns for

Section: Experiments and Resultsmentioning

confidence: 99%

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Cuesta–Frau

Murillo-Escobar

Orrego

et al. 2019

Entropy

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“…The results demonstrate that the Endo‐GCS method has important clinical value in decision support for gastric cancer detection, especially in the absence of pathological experimental conditions. In addition, when comprehensively considering the specificity and sensitivity of gastric cancer screening, the Endo‐GCS method can most effectively mitigate the negative impact of incorrect diagnoses of gastric cancer …”

Section: Experiments and Results Analysismentioning

confidence: 99%

A homogeneous ensemble method for predicting gastric cancer based on gastroscopy reports

Ding

Pan

et al. 2019

Expert Systems

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Gastroscopy is important for finding suspicious stomach lesions, screening for gastric cancer, and providing early diagnoses. Due to the differences in the levels of diagnosis and treatment among gastroscope doctors, clinical diagnosis based on gastroscopy is limited by low diagnostic sensitivity and specificity to gastric cancer. An assistive system for gastroscopy report analysis can be helpful to improve the success rate of gastric cancer detection. In this study, a homogeneous ensemble decision support system for gastric cancer screening (Endo‐GCS) that performs word segmentation, feature extraction, and gastric cancer screening on text‐based gastroscopy reports is proposed. The proposed Endo‐GCS method establishes a progressive local weighting algorithm that improves the overall prediction performance of the homogeneous ensemble model in gastric cancer screening. An optimal threshold estimation algorithm is developed to minimize the negative impact of misdiagnosis and missed diagnoses. Through a comparative experimental study using real gastroscopy report data, the pathological examination conclusion is the gold standard. The sensitivity of the proposed Endo‐GCS method is 88.27%, the specificity is 77.84%, and the accuracy is 82.11%, which significantly improved the sensitivity 65.49% and the accuracy 80.5% of the gastroscopic diagnosis results, respectively.

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“…In like manner, several classifiers were attempted to enhance the iEMG classification performance. Typical examples include artificial neural networks (ANN) [12], [14], [20], [27]- [30]; deep learning algorithm [17]; neuro-fuzzy system [8], [13];…”

Section: Introductionmentioning

confidence: 99%

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

Jose

George

Subathra

et al. 2020

IEEE Open J. Eng. Med. Biol.

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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.

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Neuromuscular disease detection by neural networks and fuzzy entropy on time‐frequency analysis of electromyography signals

Cited by 27 publications

References 22 publications

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

A homogeneous ensemble method for predicting gastric cancer based on gastroscopy reports

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

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