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In this thesis, several algorithmic procedures to estimate the MU fiber parameters from the waveform of a scanning-EMG signal (i.e., from the recording of a MUP in multiple positions along a linear corridor) have been developed. The different procedures correspond to different modifications of the scanning-EMG technique, which differ in the number of scanning needles and recording ports used to record the signal. The three proposed variants are: the 1-port recording, based on a single scanning needle with a single recording port placed at one side of the needle (i.e., a single fiber EMG needle); the 2-port recording, based on a single scanning needle, with two recording ports, placed at opposite sides of the needle; and the 4-port recording, based on two scanning needles, each one with two recording ports. The estimation system also uses a linear array of surface-EMG recordings to obtain complementary information about the MU. In this way, the recording setup to achieve the MU parameter estimation consists on a simultaneous surface- and scanning-EMG signal recording of the MUP. The estimation system has been evaluated for the three scanning-EMG recording configurations (1-port, 2-port, and 4-port), and compared to the case in which the estimation is performed from a MUP recorded at a single position. The evaluation has been done in a simulation framework, using state of the art models of the muscle, MUs, recruitment, and needles, and developing specific models for the simultaneous surface- and scanning-EMG recording process. This provides a controlled environment in which the performance of the system can be objectively quantified and evaluated.The results evidence that MU parameters are estimated much more accurately when using the scanning-EMG technique than when using a MUP recorded at a single position, corroborating the hypothesis that the use of signals recorded at multiple positions enhances the parameter estimation. Among the three proposed recording configurations, the poorest estimation results have been obtained for the 1-port configuration which, moreover, is only capable of estimating the MU fibers at one side of the needle. The 2-port configuration gives better results, and allows to estimate the MU fibers at both sides of the needle. The 4-port configuration is the one that provides the best performance, but it has the disadvantage of being the most difficult configuration to be physically implemented. In the view of these results, a deeper evaluation of the 2-port recording configuration is done. This is because it combines a good estimation performance with a relative ease to be physically implemented. An additional effort is done to calculate several global MU parameters, such as the MU fiber density, the average potential propagation velocity of the fibers, and the width of the innervation zone, from the resulting set of estimated fibers. These global MU parameters provide relevant physiological information from a clinical point of view. Hence global parameters connect the estimation system developed in this thesis with a future application in the diagnosis and follow-up of neuromuscular pathologies. Programa de Doctorado en Tecnologías de las Comunicaciones, Bioingeniería y de las Energías Renovables (RD 99/2011) Bioingeniaritzako eta Komunikazioen eta Energia Berriztagarrien Teknologietako Doktoretza Programa (ED 99/2011)

In this chapter, the monitoring of the electrical activity of skeletal muscles is depicted. The main components of the detection and conditioning of the EMG signals is explained in the sense of the biomedical instrumentation. But, first, a brief description of EMG generation is introduced. The hardware components of the general instrumentation system used in the acquisition of EMG signal such as amplifier, filters, analog-to-digital converter are discussed in detail. Subsequently, different types of electrodes used in different EMG techniques are mentioned. Then, various EMG signals that can be detected and monitored via EMG systems are described and their clinical importance is discussed with detail. Finally, different EMG techniques used in clinical studies and their purposes are explained with detail. © 2018 IGI Global. All rights reserved.

The study of the anatomy and physiology of the motor unit has important implications in the diagnosis and follow-up of neuromuscular pathologies. Muscle action potentials allow the use of electrophysiological techniques based on electromyography (EMG) to make inferences about muscle structure, state and behaviour. Scanning EMG is one such technique that can record the temporal and spatial distribution of electrical activity of a single motor unit, allowing for deep insight into the structure and function of motor units. In this chapter, we describe the scanning EMG technique in detail, both from a technical and clinical point of view. A brief review of the motor unit anatomy and physiology is provided in Section 2. The technique, the apparatus setup, the recording procedure and the signal processing required are described in Section 3. Key results of studies using scanning EMG are reviewed in Section 4, including findings related to motor unit organisation in normal muscle and how changes due to pathology are reflected using this electrophysiological technique. Finally, Section 5 provides some hints regarding the use of scanning EMG in research. This work was supported by the Regional Health Ministry of the Government of Navarre under the project 1312/2010.

Osman, 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.