Estimation of the muscle fiber density from the motor unit action potential

A space $X$ has a $mathbb{Q}$-diagonal if $X^2 setminus Delta$ has a $mathcal{K}( mathbb{Q})$-directed compact cover. We show that any compact space with a $mathbb{Q}$-diagonal is metrizable, hence any Tychonorff space with a $mathbb{Q}$-diagonal is cosmic. These give a positive answer to Problem 4.2 and Problem 4.8 in cite{COT11} raised by Cascales, Orihuela and Tkachuk

Feature Extraction and Classification of Neuromuscular Diseases Using Scanning EMG

Artuğ, 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

An algorithm for automatic detection of repeater F-waves and MUNE studies

Artuğ, Necdet Tuğrul (Arel Author)The present study aims to develop an algorithm and software that automatically detects repeater F-waves which are very difficult to analyze when elicited as high number of recordings in motor unit number estimation studies. The main strategy of the study was to take the repeater F waves discriminated by the neurologist, from limited number of recordings, as the gold standard and to test the conformity of the results of the new automated method. Ten patients with ALS and ten healthy controls were evaluated. 90 F-waves with supramaximal stimuli and 300 F-waves with submaximal stimuli were recorded. Supramaximal recordings were evaluated both manually by an expert neurologist and automatically by the developed software to test the performance of the algorithm. The results both acquired from the neurologist and from the software were found compatible. Therefore, the main expected impact of the present study is to make the analysis of repeater F waves easier primarily in motor unit number estimation studies, since there is currently a continuing need for such automated programs in clinical neurophysiology. Submaximal recordings were examined only by the developed software. The extracted features were: maximum M response amplitude, mean power of M response, mean of sMUP values, MUNE value, number of baskets, persistence of F-waves, persistence of repeater F-waves, mean of F-waves' powers, median of F-waves' powers. Feature selection methods were also applied to determine the most valuable features. Various classifiers such as multi-layer perceptron (MLP), radial basis function network (RBF), support vector machines (SVM) and k nearest neighbors (k-NN) were tested to differentiate two classes. Initially all features, then decreased numbers of features after feature selection process were applied to the aforementioned classifiers. The classification performance usually increased when decreased features were applied to intelligent systems. Ulnar recordings under submaximal stimulation showed better performance when compared with supramaximal equivalents or median nerve equivalents. The highest performance was obtained as 90% with k-NN algorithm which was a committee decision based classifier. This result was achieved with only two features, namely mean of sMUP amplitude and MUNE value

New features for scanned bioelectrical activity of motor unit in health and disease

Artuğ, 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