59 research outputs found
Interface Design for Automation of the Scanning EMG Method
No full textScanning EMG is a method developed for examining the electro-physiological cross section and the size of the motor unit of a human muscle. Electrical specifications of the motor unit can be obtained as well as anatomical distribution of muscle fibers and pathological changes between different muscles can be examined by the help of this method. In this paper; an automation system which is designed for the execution of scanning EMG method, whether manually or automatically, and a user-interface are described. Parameters like step count and step size which are about the movement of an electrode. moved by a linear actuator, through muscle fibers can be defined as reference by user via designed interface. As a result, acquired signals are digitalized by data-acquisition card (DAQ) and saved as text file for the future signal process tasks
Feature Extraction and Classification of Neuromuscular Diseases Using Scanning EMG
No full textArtuğ, Necdet Tuğrul (Arel Author) Osman, Onur (Arel Author) Göker, İmran(Arel Author) --- Conference: IEEE International Symposium on Innovations in Intelligent Systems and Applications, 2014.In this study a new dataset are prepared for neuromuscular diseases using scanning EMG method and four new features are extracted. These features are maximum amplitude, phase duration at the maximum amplitude, maximum amplitude times phase duration, and number of peaks. By using statistical values such as mean and variance, number of features has increased up to eight. This dataset was classified by using multi layer perceptron (MLP), support vector machines (SVM), k-nearest neighbours algorithm (k-NN), and radial basis function networks (RBF). The best accuracy is obtained as 97.78% with SVM algorithm and 3-NN algorithm
New features for scanned bioelectrical activity of motor unit in health and disease
No full textArtuğ, Necdet Tuğrul (Arel Author), Göker, İmran (Arel Author), Osman, Onur (Arel Author)The present study aims to find new features that support the differential diagnosis of neuromuscular diseases. Scanning EMG is an experimental method developed for understanding the motor unit organization and for observing temporal and spatial characteristics of motor unit's electrical activity. A motor unit consists of a motor neuron and muscle fibers that are innervated by its motor neuron.Both simulation and biological data on neuromuscular diseases are considered in this study. Biological data were acquired from 3 patients with neurogenic involvement (2 with poliomyelitis sequela and 1 with spinal muscular atrophy), 2 patients with myopathy (1 with inflammatory myopathy and 1 with muscular dystrophy) and 4 healthy participants. Seven features are extracted by specifications of neuromuscular diseases and characteristics of EMG signals. These features are maximum amplitude, spike duration, the number of peaks, maximum amplitude x spike duration, number of peaks x spike duration, the ratio of the power outside the activity corridor to the power inside the activity corridor and the number of peaks outside of the activity corridor. The autocorrelation function of the sum of scanning EMG signals is effective in determining the activity corridor of these signals and the spike duration can be determined more easily by using the activity corridor. Wavelet transform based noise reduction and the windowing method are proposed for calculating the features correctly. By this method, spike duration and the number of peaks should be able to be calculated more precisely. It is confirmed that if the signals are filtered by a high pass filter with a cut off frequency of 2 KHz, the calculation of the number of peaks should be easier.While maximum amplitude and maximum amplitude times spike duration are found to be significant for diagnosing neurogenic diseases, other features are found to be significant for all groups by ANOVA test. It is determined that which features are more effective for differential diagnosis and the dataset that contains normal people and patients is classified using multi-layer perceptron (MLP), radial basis function network (RBF), support vector machines (SVM) and k nearest neighbor algorithm (k-NN). The best accuracy is obtained as 85% with MLP network
Determining MUAP Activity Corridor In Scanning EMG Recordings
No full textInternational Symposium on Innovations in Intelligent SysTems and Applications (INISTA 2015) --SEP 02-04, 2015 -- Madrid, SPAINWOS: 000380428200042In scanning EMG recordings there are more than one motor unit activity which is recorded by concentric needle electrode. Especially for determining features of a motor unit action potential (MUAP) such as phase duration and number of peaks easily, first the other motor unit activities must be discarded. In this study a new method is proposed to determine the activity corridor that related with the motor unit to be examined in scanning EMG recordings. This method is comprised of wavelet transform based noise reduction and autocorrelation function based location detection. Number of 34 scanning EMG recordings was tested by using this method and the activity corridors were determined correctly.Univ Autonoma Madrid, Ai+d
The effect of recording site on extracted features of motor unit action potential
No full textArtuğ, N. Tuğrul (Arel Author), Göker, İmran (Arel Author), Osman, Onur (Arel Author)Motor unit action potential (MUAP), which consists of individual muscle fiber action potentials (MFAPs), represents the electrical activity of the motor unit. The values of the MUAP features are changed by denervation and reinnervation in neurogenic involvement as well as muscle fiber loss with increased diameter variability in myopathic diseases. The present study is designed to investigate how increased muscle fiber diameter variability affects MUAP parameters in simulated motor units. In order to detect this variation, simulated MUAPs were calculated both at the innervation zone where the MFAPs are more synchronized, and near the tendon, where they show increased temporal dispersion. Reinnervation in neurogenic state increases MUAP amplitude for the recordings at both the innervation zone and near the tendon. However, MUAP duration and the number of peaks significantly increased in a case of myopathy for recordings near the tendon. Furthermore, of the new features, “number of peaks × spike duration” was found as the strongest indicator of MFAP dispersion in myopathy. MUAPs were also recorded from healthy participants in order to investigate the biological counterpart of the simulation data. MUAPs which were recorded near to tendon revealed significantly prolonged duration and decreased amplitude. Although the number of peaks was increased by moving the needle near to tendon, this was not significant
Investigation of the effect of fiber diameter variability via recording from tendon via of single fiber electromyography
No full textIn this preliminary study,two muscle fibers which one of them had a constant fiber diameter and the other had a variable fiber diameter were created by using an EMG simulator. Single Muscle Fiber Action Potentials (SMFAPs) were recorded either from the vicinity of the neuromuscular junction or near the tendon. It was intended to reveal the relationship between the time dispersions of these electrical activities and the differences of muscle fiber diameters. Hence it is considered that the the difference between the diameters of muscle fibers contributing to these electrical activities can be estimated
Classification of juvenile myoclonic epilepsy data acquired through scanning electromyography with machine learning algorithms
No full textOsman, Onur (Arel Author), Özekes, Serhat (Arel Author)In this paper, classification of Juvenile Myoclonic Epilepsy (JME) patients and healthy volunteers included into Normal Control (NC) groups was established using Feed-Forward Neural Networks (NN), Support Vector Machines (SVM), Decision Trees (DT), and Na < ve Bayes (NB) methods by utilizing the data obtained through the scanning EMG method used in a clinical study. An experimental setup was built for this purpose. 105 motor units were measured. 44 of them belonged to JME group consisting of 9 patients and 61 of them belonged to NC group comprising ten healthy volunteers. k-fold cross validation was applied to train and test the models. ROC curves were drawn for k values of 4, 6, 8 and 10. 100% of detection sensitivity was obtained for DT, NN, and NB classification methods. The lowest FP number, which was obtained by NN, was 5
Development of a Method to Analyze Compound Action Potential (CMAP) Scan Used in the Diagnosis and Monitoring of Neuromuscular Diseases
No full textGöker, İmran (Arel Author)Motor Unit Number Estimate (MUNE) is a quantitative method which has been developed to determine the axon number as close as possible to the real axon number. It is used to assess and to monitor neuromuscular diseases such as anterior horn diseases. The Electrophysiological muscle scan is based on recording Compound Muscle Action Potentials (CMAPs) as response of the muscles innervated by the nerves being stimulated by gradually increased electrical currents. The purpose of this study is to develop a method which will ensure the MUNE by the analysis of the data obtained via electrohysiological muscle scan. CMAPs which were generetaed as a result of gradual stimulation in simulator software were recorded. The absolute values of the differences of CMAPs and the ratio of the mean of these values to the maximum CMAP value were computed through a software created in MATLAB to calculate MUNE values. Hence, it was intended to determine the relationship between the real axon count and MUN
- …
