• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.55-64
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• 2004

Conventional bipolar surface electromyography(EMG) technique detects only the superimposed electromyographic activity of a large number of motor units due to its low spatial resolution. For the diagnosis of neuromuscular disorder, the information of single MU is required. In this paper, 9 channel array surface electrode system was as designed and MLoG filter was proposed. Also the MCPT(modified convolution processing technique)method was proposed for the improvement of MUAP resolution. For performance evaluation, power spectrum analysis of random data and raw EMG signal comparison of MUAP shape and quantitative estimation of SNR were executed. As a result, the MUAP resolution improvement of 32％ was obtained from the standpoint of the signal-to-noise ratio(SNR).

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.55-58
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• 1997

In this paper, we study a signal processing method which extracts each MUAP(motor unit action potential) from EMG(Electromyogram) interference pattern or clinical diagnostic purposes. First of all, differential digital filtering is selected or eliminating the spike components of the MUAP's from the background noise. And, the algorithm identifies the spikes over the certanin threshold by template matching in frequency domain. After missing or false firing actor is cut off at the IPI(inter pulse interval) histogram, we averages the MUAP waveforms from the raw signal using the identified spikes as triggers, and Finally, measures their amplitudes, durations, and numbers of phases. Specially, We introduce algorithm performed by template matching in the frequency domain. A typical 3-s signal recorded from the biceps brachii muscle using a conventional needle electrode during a isometric contraction is used. Finally, the method decomposed five simultaneous active MUAP's from original EMG signal.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.319-326
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• 1996

Clinical myography(EMG) is a technique for diagnosing neuromuscular disorders by analyzing the electrical signal that can be records by needle electrode during a muscular contraction. The EMG signal arises from electrical discharges that accompany the generation of force by groups of muscular fiber, and the analysis of EMG signal provides symptoms that can distinguish disorder of mLecle from disor- ders of nerve. One of the methods for analysis of EMG signal is to separate the individual discharge-the motor unit action potentials(MVAPS) - from EMG signal. But we can only observe the EMG signal that is a superimposed version of time delayed MUAPS. To obtain the information about MUAP(, i.e., position, firing number, magnitude etc), first of all, a method that can separate each MUAP from the EMG signal must be developed Although the methods for MUAP separation have been proposed by many researcherl they have required heavy computational burden. In this paper, we proposed a new method that has less computational burden and performs more reliable separation of superimposed EMG signal using wavelet filter which has multiresolution analysis as major property. As a result, we develope the separation algorithm of superimposed EMG signal which has less computational burden than any other researchers and exacutes exact separation process. The performance of this method has been discussed in the automatic resolving procedure which is neccessary to identify every firing of every motor unit from the EMG pattern.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1951-1958
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• 2011

The objective of this study is to analyze the performance of digital low-pass differentiators(LPD) and then to provide a method to select effective LPD filter, for detecting spikes of surface motor unit action potentials(MUAP). The successful spike detection of MUAPs is a first important step for EMG signal decomposition. The performances of simple and weighted LPD(SLPD and WLPD) filters are analyzed based on different filter lengths and varying MUAPs from simulated surface EMG signals. The SNR improving coefficient and effective MUAP duration range from the analysis results can be used to select proper LPD filters under the varying conditions of surface EMG.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.30 no.1
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• pp.28-33
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• 2019

Background and Objectives : Laryngeal electromyography (LEMG) is valuable to evaluate the innervation status of the laryngeal muscles and the prognosis of vocal fold paralysis (VFP). However, there is a lack of agreement on quantitative interpretation of LEMG. The aim of this study is to measure the motor unit action potentials (MUAP) quantitatively in order to find cut-off values of amplitude, duration, phase for unilateral vocal fold paralysis patients. Materials and Method : Retrospective chart review was performed for the unilateral VFP patients who underwent LEMG from March 2016 to May 2018. Patient's demography, cause of VFP, vocal cord mobility, and LEMG finding were analyzed. The difference between normal and paralyzed vocal folds and cut-off values of duration, amplitude, and phase in MUAP were evaluated. Results : Thirty-six patients were enrolled in this study. Paralyzed vocal fold had significantly longer duration (p=0.021), lower amplitude (p=0.000), and smaller phase (p=0.012) than the normal. The cut-off values of duration, amplitude, and phase in MUAP for unilateral VFP were 5.15 ms, $68.35{\mu}V$, and 1.85 respectively. Conclusion : An analysis of MUAP successfully provided quantitative differences between normal and paralyzed vocal folds. But, additional research is needed to get more available cut-off value which is helpful to evaluate the status of laryngeal innervations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.1014-1019
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• 2013

Signal processing techniques based on fractional order calculus have been successfully applied in analyzing heavy-tailed non-Gaussian signals. It was found that the surface EMG signals from the muscles having nuero-muscular disease are best modeled by using the heavy-tailed non-gaussian random processes. In this regard, this paper describes an application of digital fractional order lowpass differentiators(FOLPD, weighted FOLPD) based on the fractional order calculus in detecting peaks of surface EMG signal. The performances of the FOLPD and WFOLPD are analyzed based on different filter length and varying MUAP wave shape from recorded and simulated surface EMG signals. As a results, the WFOLPD showed better SNR improving factors than the existing WLPD and to be more robust under the various surface EMG signals.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.632-640
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• 2007

The low force estimation method of skeletal muscle was proposed by using ICA(independent component analysis) and neuro-transmission model. An EMG decomposition is the procedure by which the signal is classified into its constituent MUAP(motor unit action potential). The force index of electromyography was due to the generation of MUAP. To estimate low force, current analysis technique, such as RMS(root mean square) and MAV(mean absolute value), have not been shown to provide direct measures of the number and timing of motoneurons firing or their firing frequencies, but are used due to lack of other options. In this paper, the method based on ICA and chemical signal transmission mechanism from neuron to muscle was proposed. The force generation model consists of two linear, first-order low pass filters separated by a static non-linearity. The model takes a modulated IPI(inter pulse interval) as input and produces isometric force as output. Both the step and random train were applied to the neuro-transmission model. As a results, the ICA has shown remarkable enhancement by finding a hidden MAUP from the original superimposed EMG signal and estimating accurate IPI. And the proposed estimation technique shows good agreements with the low force measured comparing with RMS and MAV method to the input patterns.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.7 no.1
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• pp.79-86
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• 2003

A myoelectric signal, under sustained isometric contraction of muscle the modelled as the output of a linear time-varying system whose input is constant number of pulse train. The proposed model considered localized muscle fatigue by metabolic by-products during sustained fatiguing contraction. To characterize muscle fatiguing model of myoelectric signal, We calculated median frequency of generated signal as fatiguing index of muscle during sustained isometric contraction.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.3
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• pp.28-39
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• 2010

In this paper, a new method to estimate MU (motor unit) location in the short head of BIC (biceps brachii) muscle using surface EMG (electromyogram) is proposed. The SMUAP (single motor unit action potential) is generated from a MU located at certain depth from the skin surface. The depth is referred as MU location. For estimating muscle force precisely, the information of the MU location is required. The reference SMUAPs are simulated based on anatomical structure of human muscle, and compared with acquired real EMG signals using 3-channel surface EMG electrode. The proposed method was compared with the results of previous researchers and verified its accuracy by computer simulation. From the simulation result in case of the MU located in 8[mm], the average estimation error of proposed method was 0.01[mm]. But the average estimation error of Roeleveld's method was 2.33[mm] and Akazawa's method was 1.70[mm]. Therefore the proposed method was more accurate than the methods of previous researchers.

• Annals of Clinical Neurophysiology
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• v.6 no.2
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• pp.85-91
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• 2004

Background: Needle electromyography (EMG) and motor evoked potential (MEP) of the genioglossus (tongue) are difficult to perform in evaluations of the craniobulbar region in amyotrophic lateral sclerosis (ALS). Therefore, we investigated the yields of needle EMG and MEP recorded from the upper trapezius, since it receives innervation from the lower medulla and upper cervical cord. Methods: Needle EMG and MEP of the upper trapezius were obtained in 17 consecutive ALS patients. The needle EMG parameters recorded included abnormal spontaneous activity and motor unit action potential (MUAP) morphology. An upper motor neuron (UMN) lesion was presumed when either response to cortical stimulation was absent, or the central conduction time was delayed (>mean+2SD). Results: Of the five patients with bulbar-onset ALS, four had abnormalities in the upper trapezius and four in the tongue by needle EMG. In contrast, of the 12 patients with limb-onset ALS, 11 had abnormalities in the upper trapezius, and only five in the tongue. When MEP was performed, it was found that three of the five patients with bulbar symptoms and three of the six patients with isolated limb involvement had abnormal MEP findings. Conclusions: Electrophysiological studies of the upper trapezius are more sensitive those of the tongue in patients without bulbar symptoms. Thus, needle EMG and MEP of the upper trapezius are alternative tools for assessing bulbar and rostral neuraxial involvement in the diagnosis of ALS.