Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron

Electromyography (EMG) signals are the measure of activity in the muscles. The aim of this study is to identify the neuromuscular disease based on EMG signals by means of classification. The neuromuscular diseases that have been identified are myopathy and neuropathy. The classification was carried out using Artificial Neural Network (ANN). There are five feature extraction techniques that were used to extract the signals such as Autoregressive (AR), Root Mean Square (RMS), Zero Crossing (ZC), Waveform length (WL) and Mean Absolute Value (MAV). A comparative analysis of these different techniques were carried out based on the results. The Multilayer Perceptron (MLP) was used for carrying out the classification

CLASSIFICATION OF T\TEUROMUSCIILAR DISORDERSI BASED ON ELECTROMYOGRAPTTY (EMG) SIGNALS

Electromyography (EMG) signals are the measune of activity in the muscles. The motion of the muscles will be generated and recorded using skin surface electrodes. EMG signals can be found from anywhere on the exterior of human's body such as biceps, triceps, shoulder, arm, hand, leg. The aim of this project is to identifr the neuromuscular diseases based on EMG signals by means of classification. The ncuromuscular diseases that have been identified are healthy, myopathy and neuropathy. The signals weIE taken and analyzed from EMG lab database to become datasets for classification system. The classification was carried out using Artilicial Neural Network. In this project, there are two techniques that used to classifr three different types of muscular disorders such as Multilayer Perceptron (MLP) and Wavelet Neural Network (WlIhI). And the input that applied to these systems using feature extraction from EMC signals. In time domain, five feature extraction techniques that used to exmct the sample of signal such as Autoregressive (AR), Root mean square (RMS), Zero crossing (zc), waveform length (wL) and Mean Absolute Value (MA$. The comparison between different techniques will be included based on the accuracy of the result. The input data has been used in Multilayer Perceptron (MtP) to train the classification system. Besides that, frequency domain was used for extracting the useful information from EMG signal for Wavelet neural network (wl[Nr) such as Power Spectrum Density (pSD), both systems were hained and the test performances were examined after training to provide the best result

Comparison of machine learning algorithms for EMG signal classification

The use of muscle activation signals in the control loop in biomechatronics systems is extremely important for effective and stable control. One of the methods used for this purpose is motion classification using electromyography (EMG) signals that reflect muscle activation. Classifying these signals with variable amplitude and frequency is a difficult process. On the other hand, EMG signal characteristics change over time depending on the person, task and duration. Various artificial intelligence-based methods are used for movement classification. One of these methods is machine learning. In this study, a total of 24 different models of 6 main machine learning algorithms were used for motion classification. With these models, 7 different wrist movements (rest, grip, flexion, extension, radial deviation, ulnar deviation, expanded palm) are classified. Test studies were carried out with 8 channels of EMG data taken from 4 subjects. Classification performances were compared in terms of classification accuracy and training time parameters. According to the simulation results, the Ensemble algorithm Bagged Trees model has been shown to have the highest classification performance with an average classification accuracy of 98.55%

CLASSIFICATION OF T\TEUROMUSCIILAR DISORDERSI BASED ON ELECTROMYOGRAPTTY (EMG) SIGNALS

Electromyography (EMG) signals are the measune of activity in the muscles. The motion of the muscles will be generated and recorded using skin surface electrodes. EMG signals can be found from anywhere on the exterior of human's body such as biceps, triceps, shoulder, arm, hand, leg. The aim of this project is to identifr the neuromuscular diseases based on EMG signals by means of classification. The ncuromuscular diseases that have been identified are healthy, myopathy and neuropathy. The signals weIE taken and analyzed from EMG lab database to become datasets for classification system. The classification was carried out using Artilicial Neural Network. In this project, there are two techniques that used to classifr three different types of muscular disorders such as Multilayer Perceptron (MLP) and Wavelet Neural Network (WlIhI). And the input that applied to these systems using feature extraction from EMC signals. In time domain, five feature extraction techniques that used to exmct the sample of signal such as Autoregressive (AR), Root mean square (RMS), Zero crossing (zc), waveform length (wL) and Mean Absolute Value (MA$. The comparison between different techniques will be included based on the accuracy of the result. The input data has been used in Multilayer Perceptron (MtP) to train the classification system. Besides that, frequency domain was used for extracting the useful information from EMG signal for Wavelet neural network (wl[Nr) such as Power Spectrum Density (pSD), both systems were hained and the test performances were examined after training to provide the best result

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

Multi-disciplinary Insights from the First European Forum on Visceral Myopathy 2022 Meeting

Visceral myopathy is a rare, life-threatening disease linked to identified genetic mutations in 60% of cases. Mostly due to the dearth of knowledge regarding its pathogenesis, effective treatments are lacking. The disease is most commonly diagnosed in children with recurrent or persistent disabling episodes of functional intestinal obstruction, which can be life threatening, often requiring long-term parenteral or specialized enteral nutritional support. Although these interventions are undisputedly life-saving as they allow affected individuals to avoid malnutrition and related complications, they also seriously compromise their quality of life and can carry the risk of sepsis and thrombosis. Animal models for visceral myopathy, which could be crucial for advancing the scientific knowledge of this condition, are scarce. Clearly, a collaborative network is needed to develop research plans to clarify genotype–phenotype correlations and unravel molecular mechanisms to provide targeted therapeutic strategies. This paper represents a summary report of the first ‘European Forum on Visceral Myopathy’. This forum was attended by an international interdisciplinary working group that met to better understand visceral myopathy and foster interaction among scientists actively involved in the field and clinicians who specialize in care of people with visceral myopathy. Graphical Abstract: [Figure not available: see fulltext.

Portable, Non-Invasive Fall Risk Assessment in End Stage Renal Disease Patients on Hemodialysis

Patients with end stage renal diseases (ESRD) on hemodialysis (HD) have high morbidity and mortality due to multiple causes, one of which is dramatically higher fall rates than the general population. The mobility mechanisms that contribute to falls in this population must be understood if adequate interventions for fall prevention are to be achieved. This study utilizes emerging noninvasive, portable gait, posture, strength, and stability assessment technologies to extract various mobility parameters that research has shown to be predictive of fall risk in the general population. As part of an ongoing human subjects study, mobility measures such as postural and locomotion profiles were obtained from five (5) ESRD patients undergoing HD treatments. To assess the effects of post-HD-fatigue on fall risk, both the pre- and post-HD measurements were obtained. Additionally, the effects of inter-HD periods (two days vs. three days) were investigated using the non-invasive, wireless, body-worn motion capture technology and novel signal processing algorithms. The results indicated that HD treatment influenced strength and mobility (i.e., weaker and slower after the dialysis, increasing the susceptibility to falls while returning home) and interdialysis period influenced pre-HD profiles (increasing the susceptibility to falls before they come in for a HD treatment). Methodology for early detection of increased fall risk – before a fall event occurs – using the portable mobility assessment technology for out-patient monitoring is further explored, including targeting interventions to identified individuals for fall prevention

A Multiple Instance Learning Approach to Electrophysiological Muscle Classification for Diagnosing Neuromuscular Disorders Using Quantitative EMG

Neuromuscular disorder is a broad term that refers to diseases that impair muscle functionality either by affecting any part of the nerve or muscle. Electrodiagnosis of most neuromuscular disorders is based on the electrophysiological classification of involved muscles which in turn, is performed by inferring the structure and function of the muscles by analyzing electromyographic (EMG) signals recorded during low to moderate levels of contraction. The functional unit of muscle contraction is called a motor unit (MU). The morphology and physiology of the MUs of an examined muscle are inferred by extracting motor unit potentials (MUPs) from the EMG signals detected from the muscle. As such, electrophysiological muscle classification is performed by first characterizing extracted MUPs and then aggregating these characterizations. The task of classifying muscles can be represented as an instance of a multiple instance learning (MIL) problem. In the MIL paradigm, a bag of instances shares a label and the instance labels are hidden, contrary to standard supervised learning, where each training instance is labeled. In MIL-based muscle classification, the instances are the MUPs extracted from the EMG signals of the analyzed muscle and the bag is the muscle. Detecting and counting the MUPs indicating a specific category of a neuromuscular disorder can result in accurately classifying the examined muscle. As such, three major issues usually arise: how to infer MUP labels without full supervision; how the cardinality relationships between MUP labels contribute to predict the muscle label; and how the muscle as a whole entity is classified. In this thesis, these three challenges are addressed. To this end, an MIL-based muscle classification system is proposed that has five major steps: 1) MUPs are represented using morphological, stability, and novel near fiber parameters as well as spectral features extracted from wavelet coefficients. This representation helps to analyze MUPs from a variety of aspects. 2) MUP feature selection using unsupervised similarity preserving Laplacian score which is independent of any learning algorithm. Hence, the features selected in this work can be used in other electrophysiological muscle classification systems. 3) MUP clustering using a novel clustering algorithm called Neighbourhood Distance Entropy Consistency (NDEC) which contributes to solve the traditional problem of finding representations of MUP normality and abnormality and provides a dynamic number of MUP characterization classes which will be used instead of the conventional three classes (i.e. normal, myopathic, and neurogenic). This clustering was performed to highlight the effects of disease on both fiber spatial distributions and fiber diameter distributions, which lead to a continuity of MUP characteristics. These clusters can potentially represent several concepts of MUP normality and abnormality. 4) Muscle representation by embedding its MUP cluster associations in a feature vector, and 5) Muscle classification using support vector machines or random forests. Quantitative results obtained by applying the proposed method to four electrophysiologically different groups of muscles including proximal arm, proximal leg, distal arm, and distal leg show the superior and stable performance of the proposed muscle classification system compared to previous works. Additionally, modelling electrophysiological muscle classification as an instance of the MIL can solve the traditional problem of characterizing MUPs without full supervision. The proposed clustering algorithm in this work, can be used as an effective technique in other pattern recognition and medical diagnostic systems in which discovering natural clusters within data is a necessity

Classification of Surface EMG Using Wavelet Packet Energy Analysis and a Genetic Algorithm-Based Support Vector Machine

The aim of our study was to recognize results of surface electromyography (sEMG) recorded under conditions of a maximum voluntary contraction (MVС) and fatigue states using wavelet packet transform and energy analysis. The sEMG signals were recorded in 10 young men from the right upper limb with a handgrip. sEMG signals were decomposed by wavelet packet transform, and the corresponding energies of certain frequencies were normalized as feature vectors. A back-propagation neural network, a support vector machine (SVM), and a genetic algorithm-based SVM (GA-SVM) worked as classifiers to distinguish muscle states. The results showed that muscle fatigue and MVC could be identified by level-4 wavelet packet transform and GA-SVM more accurately than when using other approaches. The classification correct rate reached 97.3% with sevenfold cross-validation. The proposed method can be used to adequately reflect the muscle activity.Ціллю нашого дослідження була розробка прийомів розпізнавання результатів електроміографічних відведень за допомогою поверхневих електродів (пЕМГ) в умовах розвитку максимального довільного скорочення та станів втоми; використовували пакетне вейвлет-перетворення та аналіз енергії. Сигнали пЕМГ піддавалися декомпозиції із застосуванням пакетного вейвлет-перетворення, і відповідні оцінки енергії певних частот нормувались як вектор ознак. Нейронна мережа із зворотним проведенням, машина опорних векторів (SVM) та SVM, базована на генетичному алгоритмі (GA-SVM), працювали як класифікатори, що розпізнавали стани м’язів. Отримані результати показали, що стани м’язової втоми та максимального довільного скорочення можуть бути ідентифіковані за допомогою пакетного вейвлет-перетворення 4-го рівня точніше, ніж у разі застосування інших підходів. Рівень коректності класифікації при семиразовій кросвалідизації сягав 97.3 %. Запропонований метод може бути використаний для адекватного відображення м’язової активності

Characterization of Neuromuscular Disorders Using Quantitative Electromyographic Techniques

This thesis presents a multifaceted effort to develop a system that allows electrodiagnostic clinicians to perform a quantitative analysis of needle detected electromyographic (EMG) signals for characterization of neuromuscular disorders. Currently, the most widely adopted practise for evaluation of patients with suspected neuromuscular disorders is based on qualitative visual and auditory assessment of EMG signals. The resulting characterizations from this qualitative assessment are criticized for being subjective and highly dependent on the skill and experience of the examiner. The proposed system can be decomposed functionally into three stages: (1) extracting relevant information from the EMG signals, (2) representing the extracted information in formats suitable for qualitative, semi-quantitative and quantitative assessment, and (3) supporting the clinical decision, i.e., characterizing the examined muscle by estimating the likelihood of it being affected by a specific category of neuromuscular disorders. The main contribution of the thesis to the extraction stage is the development of an automated decomposition algorithm specifically tailored for characterization of neuromuscular disorders. The algorithm focuses on identifying as many representative motor unit potential trains as possible in times comparable to the times needed to complete a qualitative assessment. The identified trains are shown to reliably capture important aspects of the motor unit potential morphology and morphological stability. With regards to the representation stage, the thesis proposes ten new quantitative EMG features that are shown to be discriminative among the different disease categories. Along with eight traditional features, the features can be grouped into subsets, where each subset reflects a different aspect of the underlying motor unit structure and/or function. A muscle characterization obtained using a feature set in which every relevant aspect is included using the most representative feature is more structured, simple, and generalizable. All the investigated features are clinically relevant. An examiner can easily validate their values by visual inspection; interpret them from an anatomical, physiological, and pathological basis; and is aware of their limitations and dependence on the acquisition setup. The second main contribution to the representation stage is the evaluation of the possibility of detecting neurogenic disorders using a newly proposed set of quantitative features describing the firing patterns of the identified motor units. The last contribution to the representation stage is the development of novel methods that allow an examiner to detect contributions from fibres close to the detection surface of a needle electrode and to track them across a motor unit potential train. The work in this thesis related to the decision support stage aims at improving existing methods for obtaining transparent muscle characterization. Transparent methods do not only estimate the likelihood of the muscle being affected by a specific disorder, but also induce a set of rules explaining the likelihood estimates. The results presented in this thesis show that remodelling the characterization problem using an appropriate binarization mapping can overcome the decrease in accuracy associated with quantizing features, which is used to induce transparency rules. To attain the above mentioned objectives, different signal processing and machine learning methods are utilized and extended. This includes spectral clustering, Savitzky-Golay filtering, dynamic time warping, support vector machines, classification based on event association rules and Gaussian mixture models. The performance of the proposed methods has been evaluated with four different sets of examined limb muscles (342 muscles in total). Also, it has been evaluated using simulated EMG signals calculated using physiologically and anatomically sound models. A system capable of achieving the aforementioned objectives is expected to promote further clinical adoption of quantitative electromyographic techniques. These techniques have potential advantages over existing qualitative assessments including resolving equivocal cases, formalizing communication and evaluating prognosis