Classification Of Neuromuscular Diseases Research Articles

Prediction of biomedical signals using deep learning techniques

The electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) are all very useful diagnostic techniques. The widespread availability of mobile devices plus the declining cost of ECG, EEG, and EMG sensors provide a unique opportunity for making this kind of study widely available. The fundamental need for enhancing a country’s healthcare industry is the ability to foresee the plethora of ailments with which people are now being diagnosed. It’s no exaggeration to say that heart disease is one of the leading causes of mortality and disability in the world today. Diagnosing heart disease is a difficult process that calls for much training and expertise. Electrocardiogram (ECG) signal is an electrical signal produced by the human heart and used to detect the human heartbeat. Emotions are not simple phenomena, yet they do have a major impact on the standard of living. All of these mental processes including drive, perception, cognition, creativity, focus, attention, learning, and decision making are greatly influenced by emotional states. Electroencephalogram (EEG) signals react instantly and are more responsive to changes in emotional states than peripheral neurophysiological signals. As a result, EEG readings may disclose crucial aspects of a person’s emotional states. The signals generated by electromyography (EMG) are gaining prominence in both clinical and biological settings. Differentiating between neuromuscular illnesses requires a reliable method of detection, processing, and classification of EMG data. This study investigates potential deep learning applications by constructing a framework to improve the prediction of cardiac-related diseases using electrocardiogram (ECG) data, furnishing an algorithmic model for sentiment classification utilizing EEG data, and forecasting neuromuscular disease classification utilizing EMG signals.

Neuromuscular Disease Affecting the Larynx.

Neuromuscular diseases (NMDs) are diseases involving the motor nuclei of the cranial nerves and anterior horn cells of the spinal cord, the peripheral nerves, the neuromuscular junction, and/or muscle itself. The following groups of disorders from the WHO International Classification of Diseases are usually included in the classification of NMDs: (1) Motor neuron diseases or related disorders; (2) Disorders of nerve root, plexus or peripheral nerves; (3) Diseases of neuromuscular junction or muscle. These diseases include myasthenia gravis or certain specified neuromuscular junction disorders, primary disorders of muscle, and secondary myopathies. The following discussion selects NMDs from each group, which may have unique laryngeal features and more commonly involve the laryngologist and highlights different diagnostic and treatment considerations related to the larynx.

A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders

Electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. This paper is aim at introducing an effective multi-classifier approach to enhance classification accuracy .The proposed system employs both time domain and time-frequency domain features of motor unit action potentials (MUAPs) extracted from an EMG signal. Different classification strategies including single classifier and multiple classifiers with time domain and time frequency domain features were investigated. Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classifier used predict class label (Myopathic, Neuropathic, or Normal) for a given MUAP. Extensive analysis is performed on clinical EMG database for the classification of neuromuscular diseases and it is found that the proposed methods provide a very satisfactory performance in terms overall classification accuracy.

A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders

Electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. This paper is aim at introducing an effective multi-classifier approach to enhance classification accuracy .The proposed system employs both time domain and time-frequency domain features of motor unit action potentials (MUAPs) extracted from an EMG signal. Different classification strategies including single classifier and multiple classifiers with time domain and time frequency domain features were investigated. Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classifier used predict class label (Myopathic, Neuropathic, or Normal) for a given MUAP. Extensive analysis is performed on clinical EMG database for the classification of neuromuscular diseases and it is found that the proposed methods provide a very satisfactory performance in terms overall classification accuracy.

Classification of EMG based Diseases using Fuzzy Logic at Second Level

Intelligent diagnosis systems plays very important role in the medical field and particularly in diagnosis and classification of neuromuscular diseases (NMD). In such a field where uncertainty is always present, classical techniques of classification show a lack of efficiency thus leading to erroneous results. Fuzzy logic proved to be a suitable method for classification problems, particularly in NMDs. This paper aims to present a fuzzy logic based system for the classification of seven types of neuromuscular diseases at first level and classification of eight types of muscular dystrophy diseases at second level. The obtained results have shown good performance of the designed classifier.

Wavelet domain feature extraction scheme based on dominant motor unit action potential of EMG signal for neuromuscular disease classification.

In this paper, two schemes for neuromuscular disease classification from electromyography (EMG) signals are proposed based on discrete wavelet transform (DWT) features. In the first scheme, a few high energy DWT coefficients along with the maximum value are extracted in a frame by frame manner from the given EMG data. Instead of considering only such local information obtained from a single frame, we propose to utilize global statistics which is obtained based on information collected from some consecutive frames. In the second scheme, motor unit action potentials (MUAPs) are first extracted from the EMG data via template matching based decomposition technique. It is well known that not all MUAPs obtained via decomposition are capable of uniquely representing a class. Thus, a novel idea of selecting a dominant MUAP, based on energy criterion, is proposed and instead of all MUAPs, only the dominant MUAP is used for the classification. A feature extraction scheme based on some statistical properties of the DWT coefficients of dominant MUAPs is proposed. For the purpose of classification, the K-nearest neighborhood (KNN) classifier is employed. Extensive analysis is performed on clinical EMG database for the classification of neuromuscular diseases and it is found that the proposed methods provide a very satisfactory performance in terms of specificity, sensitivity, and overall classification accuracy.

DCT domain feature extraction scheme based on motor unit action potential of EMG signal for neuromuscular disease classification.

A feature extraction scheme based on discrete cosine transform (DCT) of electromyography (EMG) signals is proposed for the classification of normal event and a neuromuscular disease, namely the amyotrophic lateral sclerosis. Instead of employing DCT directly on EMG data, it is employed on the motor unit action potentials (MUAPs) extracted from the EMG signal via a template matching-based decomposition technique. Unlike conventional MUAP-based methods, only one MUAP with maximum dynamic range is selected for DCT-based feature extraction. Magnitude and frequency values of a few high-energy DCT coefficients corresponding to the selected MUAP are used as the desired feature which not only reduces computational burden, but also offers better feature quality with high within-class compactness and between-class separation. For the purpose of classification, the K-nearest neighbourhood classifier is employed. Extensive analysis is performed on clinical EMG database and it is found that the proposed method provides a very satisfactory performance in terms of specificity, sensitivity and overall classification accuracy.

Neuromuscular disease classification system

Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.

Hereditary neuromuscular diseases

This article presents the actual classification of neuromuscular diseases based on present expansion of our knowledge and understanding due to genetic developments. It summarizes the genetic and clinical presentations of each disorder together with CT findings, which we studied in a large group of patients with neuromuscular diseases. The muscular dystrophies as the largest and most common group of hereditary muscle diseases will be highlighted by giving detailed information about the role of CT and MRI in the differential diagnosis. The radiological features of neuromuscular diseases are atrophy, hypertrophy, pseudohypertrophy and fatty infiltration of muscles on a selective basis. Although the patterns and distribution of involvement are characteristic in some of the diseases, the definition of the type of disease based on CT scan only is not always possible.

Muscle relaxation in patients with neuromuscular diseases

The classification of neuromuscular diseases with regard to the use of muscle relaxants is based on the localisation of the particular abnormality. Three types of syndromes can be differentiated: (1) denervation states; (2) disturbances of neuromuscular transmission; and (3) intracellular disease. Succinylcholine should be avoided in all types of denervation syndrome due to the possibility of life-threatening hyperkalaemia. The time frame during which succinylcholine must be avoided following a traumatic denervation or burn begins 24 h after the event. The exact period of risk is unknown, but a duration of 6 months can be considered the absolute minimum. Patients may display increased sensitivity to non-depolarising muscle relaxants following damage to the second motoneuron (e.g., amyotrophic lateral sclerosis), whereas in diseases of the first motoneuron (e.g., cerebral apoplexy), increased resistance to muscle relaxants may be observed in the affected parts of the body. In diseases of the neuromuscular junction (myasthenia gravis) there is increased sensitivity to non-depolarising muscle relaxants. No complications have been described following the use of succinylcholine in these patients, however, the use of reversal agents may lead to prolongation of the effect of succinylcholine. Patients with a primary myopathy may display increased sensitivity to non-depolarising muscle relaxants. The use of drugs with acetylcholine-like actions (succinylcholine, reversal agents) should be avoided due to the danger of triggering muscle spasms in patients with myotonic disease and the risk of rhabdomyolysis in patients with dystrophic muscle disease. Irrespective of the type of muscle disease present, titration of the dose of muscle relaxant should always be done using a nerve stimulator.(ABSTRACT TRUNCATED AT 250 WORDS)

Pharmacologic Concerns of Corticosteroids in the Treatment of Patients with Immune-Related Neuromuscular Diseases

Traditionally, diseases affecting the motor unit had been subdivided based on anatomic criteria into diseases of the anterior horn cell, the peripheral nerve, the neuromuscular junction, and the muscle itself. For therapeutic considerations, however, a more precise cellular and clinicopathologic characterization of the diseases affecting the motor unit is needed. Based on the site and cell of the primary pathologic change, this article presents an immunotherapy-oriented broad classification of neuromuscular diseases affecting the motor unit.

An Atlas of Muscle Pathology in Neuromuscular Disease

The authors in their preface state that in developing this book they have tried to answer the questions: "What are the fundamental reactions of muscle to disease?" and "With what clinical entities are these reactions associated?" The first two-thirds of the book is devoted to the first question. With a series of superbly clear illustrations and a brief account in the text they have admirably and tersely fulfilled their intent. They divide the fundamental changes in muscle in response to disease into ten different categories, including structural changes in the muscle fiber, changes in muscle nuclei, changes in fiber size, ringed fibers, sarcoplasmic masses, changes in interstitial tissue, leukocyte infiltration, changes in muscle spindles and peripheral muscular nerves, the presence of basophilic fibers, and the pattern of the lesion. In the last third of the book the authors set forth their clinical classification of neuromuscular diseases. There have been many