Influence of Baseline Fluctuation Cancellation on Automatic Measurement of Motor Unit Action Potential Duration

The aim of this work is to analyze the influence of a method for baseline fluctuation (BLF) cancellation for electromyographic (EMG) signals on automatic methods for measurement of the motor unit action potential (MUAP) duration. These methods include four conventional automatic methods (CAMs) and a recently published wavelet transform method (WTM). A set of 182 MUAPs from 170 EMG recordings were studied. The CAMs and the WTM were applied to the MUAPs before and after applying BLF cancellation to the recordings. A gold standard of duration marker positions (GSP) ws manually established. The accuracy of each algorithm was estimated as the dfference between its positions and the GSP. Accuracies were compared for the 5 methods and for each method before and after BLF cancellation. A significant difference between accuracy pre- and post-BLF removal was found in two CAMs; markers were closer to the GSP after BLF removal. For all MUAPs, the differences between WTM markers and the GSP were the smallest, and significant differences were not found for the WTM before and after BLF cancellation. The management of BLF is an important issue in EMG signal processing and BLF removal must be considered in extraction and analyse of MUAP waveforms. The BLF removal method improved the performance of two CAMs for MUAP duration measurement. The WTM was the most accurate and was not affected by BLF.

Establishment of the active region in scanning-MUP signals

The main purpose of this work is to define and test algorithms that are able to estimate the spatial limits of the active region of a motor unit potential and use it to distinguish among valid and invalid solutions. The dataset is provided by single-needle multiscannig-EMG simulations. Three different methods are proposed, described, and evaluated in this work. Results are analyzed for every method and compared between them.openEmbargo temporaneo per motivi di priorità  nella ricerca previo accordo con terze part

A denoising algorithm for surface EMG decomposition

The goal of the present thesis was to investigate a novel motor unit potential train (MUPT) editing routine, based on decreasing the variability in shape (variance ratio, VR) of the MUP ensemble. Decomposed sEMG data from 20 participants at 60% MVC of wrist flexion was used. There were two levels of denoising (relaxed and strict) criteria for removing discharge times associated with waveforms that did not decrease the VR and increase its signal-to-noise ratio (SNR) of the MUP ensemble. The peak-to-peak amplitude and the duration between the positive and negative peaks for the MUP template were dependent on the level of denoising (p’s 0.05). The same was true between denoising criteria (p>0.05). Editing the MUPT based on MUP shape resulted in significant differences in measures extracted from the MUP template, with trivial difference between the standard error of estimate for mean IDIs between the complete and denoised MUPTs

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications

Advances in Clinical Neurophysiology

Including some of the newest advances in the field of neurophysiology, this book can be considered as one of the treasures that interested scientists would like to collect. It discusses many disciplines of clinical neurophysiology that are, currently, crucial in the practice as they explain methods and findings of techniques that help to improve diagnosis and to ensure better treatment. While trying to rely on evidence-based facts, this book presents some new ideas to be applied and tested in the clinical practice. Advances in Clinical Neurophysiology is important not only for the neurophysiologists but also for clinicians interested or working in wide range of specialties such as neurology, neurosurgery, intensive care units, pediatrics and so on. Generally, this book is written and designed to all those involved in, interpreting or requesting neurophysiologic tests

Removal of electrocardiographic corruption from electromyographic signals using a localized wavelet based approach.

Introduction: Approximately 12,000 new cases of spinal cord injury (SCI) are reported each year in the US. Currently, the most widely used method of assessing the recovery of voluntary capability after spinal cord injury is the American Spinal Injury Association Impairment Scale (AIS). However, this test is not objective and is not sensitive enough to detect small activities. Recent studies have been using surface electromyography (EMG) to develop objective and sensitive spinal cord injury (SCI) characterization protocols. EMG recordings from the trunk muscles are contaminated with the electrical activity of the heart (ECG, electrocardiography). Depending on the level, spinal cord injury may disrupt the control of the trunk muscles, and EMG recordings from these muscles will be very weak compared to those in non-injured individuals. The elimination of ECG artifacts play critical role in precise evaluation of the trunk muscles in these individuals. While common global digital filters may generally remove some of the ECG corruption from the signal, these filters also remove or alter valuable EMG signal, which makes the physiological importance of these signals irrelevant. Methods: Local filtering approach was developed to remove this ECG noise, without significantly altering the EMG signal. The local filtering approach uses externally recorded ECG signals, in a separate lead configuration, as a mask to locate the area of ECG spikes within the noisy EMG signal. The areas of the signal containing the ECG noise are decomposed into 128 sub-wavelets using custom-scaled Morlet Wavelet Transform. Sub-wavelets pertaining to ECG within the signal at the ECG spike location are then removed, and the signal is reconstructed to create a clean EMG signal. This process is analytically tested for robustness and accuracy, using customized validation metrics, on simulated phantom signals. It is compared with a global Morlet Wavelet filter that does not localize its filtering process on the ECG spikes. Results: It was found that the localized filtering significantly reduced the Root-Mean-Squared (RMS) of the area of the signal containing ECG spikes. The Localized Filter also significantly reduced the error produced from removal of EMG signal in the areas outside of ECG spikes compared to global filter. The proposed local filter doesn’t degrade the signal, even at low ECG amplitudes (~60% improvement), compared to the global filter, which destroys the signal at this low amplitude ECG (-100% improvement). The proposed local filter is also far more efficient at removing larger amplitude ECG (more critical) than the global filter, which has a narrow range of signals that it can efficiently remove ECG. Hence, the proposed local filter is more robust and clinical-ready than the global filter. Conclusion: Proposed approach is far superior in terms of ECG removal accuracy, and introduction of artifact error from processing, compared to comparable global filter. It provides a mean to improve analysis of EMG signals as a tool to assess recovery from SCI

Electromyographic signal processing and analysis methods

La electromiografía clínica es una metodología de registro y análisis de la actividad bioeléctrica del músculo esquelético orientada al diagnóstico de las enfermedades neuromusculares. Las posibilidades de aplicación y el rendimiento diagnóstico de la electromiografía han evolucionado paralelamente al conocimiento de las propiedades de la energía eléctrica y al desarrollo de la tecnología eléctrica y electrónica. A mediados del siglo XX se introdujo el primer equipo comercial de electromiografía para uso médico basado en circuitos electrónicos analógicos. El desarrollo posterior de la tecnología digital ha permitido disponer de sistemas controlados por microprocesadores cada vez más fiables y potentes para captar, representar, almacenar, analizar y clasificar las señales mioeléctricas. Es esperable que el avance de las nuevas tecnologías de la información y la comunicación pueda conducir en un futuro próximo a la aplicación de desarrollos de inteligencia artificial que faciliten la clasificación automática de señales así como sistemas expertos de apoyo al diagnóstico electromiográfico.Clinical electromyography is a methodology for recording and analysing the bioelectrical activity of the skeletal muscle tissue in order to diagnose neuromuscular pathology. The possibilities of application and the diagnostic performance of electromyography have evolved parallel to a growing understanding of the properties of electricity and the development of electrical and electronic technology. The first commercially available electromyography equipment for medical use was introduced in the middle of the 20th century. It was based on analog electronic circuits. The subsequent development of digital technology made available more powerful and accurate systems, controlled by microprocessors, for recording, displaying, storing, analysing, and classifying the myoelectric signals. In the near future, it is likely that advances in the new information and communication technologies could result in the application of artificial intelligence systems to the automatic classification of signals as well as expert systems for electromyographic diagnosis support