Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders

EMG signal decomposition: how can it be accomplished and used?

The motor unit action potentials (MUPs) in an electromyographic (EMG) signal provide a significant source of information for the assessment of neuromuscular disorders. Since recently there were different types of developments in computer-aided EMG equipment, different methodologies in the time domain and frequency domain has been followed for quantitative analysis of EMG signals. In this study, the usefulness of the different feature extraction methods for describing MUP morphology is investigated. Besides, soft computing techniques were presented for the classification of intramuscular EMG signals. The proposed method automatically classifies the EMG signals into normal, neurogenic or myopathic. Also, multilayer perceptron neural networks (MLPNN), dynamic fuzzy neural network (DFNN) and adaptive neuro-fuzzy inference system (ANFIS) based classifiers were compared in relation to their accuracy in the classification of EMG signals. Concerning the impacts of features on the EMG signal classification, different results were obtained through analysis of the soft computing techniques. The comparative analysis suggests that the ANFIS modelling is superior to the DFNN and MLPNN in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability.

Classification of EMG signals using combined features and soft computing techniques

Decomposition and quantitative analysis of clinical electromyographic signals.

EMG signal filtering based on Empirical Mode Decomposition

Artificial neural network (ANN) based signal processing methods have been shown to have significant robustness in processing complex, degraded, noisy, and unstable signals. A novel approach to automated electromyogram (EMG) signal decomposition, using an ANN processing architecture, is presented here. Due to the lack of a priori knowledge of motor unit action potential (MUAP) morphology, the EMG decomposition must be performed in an unsupervised manner. An ANN classifier, consisting of a multilayer perceptron neural network and employing a novel unsupervised training strategy, is proposed. The ANN learns repetitive appearances of MUAP waveforms from their suspected occurrences in a filtered EMG signal in an autoassociative learning task. The same training waveforms are fed into the trained ANN and the output of the ANN is fed back to its input, giving rise to a dynamic retrieval net classifier. For each waveform in the data, the network discovers a feature vector associated with that waveform. For each waveform, classification is achieved by comparing its feature vector with those of the other waveforms. Firing information of each MUAP is further used to refine the classification results of the ANN classifier. Then, individual MUAP waveform shapes are derived and their firing tables are created. >

NNERVE: Neural Network Extraction of Repetitive Vectors for Electromyography. I. Algorithm

For pt. I see ibid., vol. 41, no. 11, p. 1039-53 (1994). In pt. I the authors presented a new method for the decomposition of clinical electromyographic signals, NNERVE, which utilizes a novel "pseudo-unsupervised" neural network approach to signal decomposition. Here the authors present a detailed performance analysis. They present definitions for quantitative performance criteria. NNERVE is shown to be highly reliable over a wide range of neural network architectures. It is also minimally sensitive to learning parameters. The degradations of performance over a wide range of signals and parameters are shown to be gradual, slight and graceful. These characteristics are shown to translate directly into a high degree of robustness over widely varying signals. Real signals obtained from the entire range of patients encountered in clinical situations are shown to be correctly handled without any modifications or adjustments of any parameters. This neural network method is then directly compared to a prior traditional signal processing method and is shown quantitatively to have consistently superior performance on both simulated and real signals. Clinically acceptable performance over a wide range of signals, recorded using standard clinical methodology, and the lack of a need for user interaction, will facilitate the use of motor unit quantitation in routine clinical electromyography. >

NNERVE: Neural Network Extraction of Repetitive Vectors for Electromyography. II. Performance analysis

Following predictions by first-principles theory of a huge tunnel magnetoresistance (TMR) effect in epitaxial Fe/MgO/Fe magnetic tunnel junctions (MTJs), measured magnetoresistance (MR) ratios of about 200% at room temperature (RT) have been reported in MgO-based epitaxial MTJs. Recently, a MR ratio of about 600% has been reported at RT in MgO-based MTJs prepared by magnetron sputtering, using amorphous CoFeB as the ferromagnetic electrode. These MTJs show great potential for application in spintronic devices. Fully epitaxial MTJs are excellent model systems that enhance our understanding of the spin-dependent tunneling process as the interface is well defined and can be fully characterized. Both theoretical calculations and experimental results clearly indicate that the interfacial structure plays a crucial role in the coherent tunneling across a single crystal MgO barrier, especially in epitaxial MgO-based MTJs grown by molecular beam epitaxy (MBE). Surface X-ray diffraction, Auger electron spectroscopy, X-ray absorption spectra, and X-ray magnetic circular dichroism techniques have been reported previously for interface characterization. However, no consistent viewpoint has been reached on the interfacial structures (such as FeO layer formation at the bottom Fe/MgO interface), and it is still an open issue. In this article, our recent studies on the interface characterization of MgO-based epitaxial MTJs by X-ray photoelectron spectroscopy, high resolution transmission electron microscopy, and spin-dependent tunneling spectroscopy, will be presented.

Interface characterization of epitaxial Fe/MgO/Fe magnetic tunnel junctions.

To develop quantitative measures of motor unit action potential (MUAP) sounds, we correlated Fourier domain features of isolated, individual MUAPs with classic time domain measurements. There were moderate correlations between amplitude or duration measurements, and absolute low-frequency (39 to 234 Hz) energy and amplitude of the peak frequency, and high correlations between the composite time domain feature of amplitude x duration, and total energy, amplitude of the peak frequency, and absolute low-frequency energy. Polyphasic potentials have multiple peaks in the magnitude component of the Fourier transform. Phase information appears to convey the “crisp” sound of MUAPs close to the recording electrode. The clinical description of “large” or “small” MUAPs by sound is likely based on absolute low-frequency energy, and incorporates both amplitude and duration information. We conclude that features of isolated MUAPs may be analyzed in the Fourier domain, and that they correlate closely with traditionally used measures of known diagnostic significance. The sound of the EMG used by clinical electromyographers is amenable to quantitative analysis.

Quantitative computer analysis of the sounds of isolated motor unit action potentials

A signal decomposition method which utilizes a multi-layer dynamic network to automatically decompose a clinical electromyogram (EMG), without supervision, is proposed. Due to the lack of a priori knowledge of motor unit potential (MUP) morphology, the EMG decomposition must be performed in an unsupervised manner. A neural network classifier, consisting of a multi-layer neural net of perceptrons and using an unsupervised training strategy, is proposed. The neural network learns repetitive appearances of MUP waveforms from their suspected occurrence in a given filtered EMLG signal by using an unsupervised clustering strategy. Upon training, the network creates stable attractors which correspond to nominal representations of MUP clusters hidden in the data. The decomposition/clustering capabilities of the proposed method are validated on a real EMG signal and on an unlabeled signal set. >

Electromyogram decomposition via unsupervised dynamic multi-layer neural network

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