Diagnosis Of Neuromuscular Disorders Research Articles

Classification of EMG Signals Using PCA and FFT

In this study, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from ulnar nerves of 59 patients to interpret data. The data of the patients were diagnosed by the neurologists as 19 patients were normal, 20 patients had neuropathy and 20 patients had myopathy. The amount of FFT coefficients had been reduced by using principal components analysis (PCA). This would facilitate calculation and storage of EMG data. PCA coefficients were applied to multilayer perceptron (MLP) and support vector machine (SVM) and both classified systems of performance values were computed. Consequently, the results show that SVM has high anticipation level in the diagnosis of neuromuscular disorders. It is proved that its test performance is high compared with MLP.

Can clinical signs identify newborns with neuromuscular disorders?

To evaluate retrospectively the prevalence of neuromuscular disorders in 83 newborns referred to a tertiary care center because of hypotonia and weakness and/or contractures, with a possible diagnosis of neuromuscular disorder. We also aimed to establish whether clinical signs could help to identify infants with neuromuscular disorders. Sixty-six of the 83 infants who fulfilled the inclusion criteria (79.5%) had an identifiable disorder, which was a neuromuscular disorder in 39 (46.9%). Absent or extremely reduced antigravity movements were mainly found in infants with neuromuscular disorders (sensitivity and specificity 97.4% and 75%), whereas partial range antigravity movements were more frequent in infants with other diagnosis. Contractures were mainly found in infants with peripheral nerve or muscle involvement but also were relatively frequent in infants with genetic or metabolic syndromes (sensitivity 69.2%, specificity 61.3%). Reduced fetal movements and abnormal liquor were frequent but not present consistently in infants with neuromuscular disorders (sensitivity 46.1% and 38.4%) and were found rarely in infants with other disorders (specificity 88.6% and 75.0%). Severe muscle weakness and contractures are the most reliable indicators of a neuromuscular disorder and should be carefully assessed in an infant with neonatal hypotonia.

Electron Microscopy in Neuromuscular Disorders

Electron microscopy has a strategic position in the diagnosis of neuromuscular disorders. In muscular fibers, the main abnormalities include vacuoles, inclusion bodies, and myofibrillar disorganization with or without abnormal inclusion material. Vacuolar changes include lipidic and glycogenic storage vacuoles, rimmed vacuoles, and lysosomal and autophagic vacuoles. Accumulation of abnormal inclusion material is found in nemaline myopathy, actinopathies, and hyaline body myopathy. Myofibrillar disorganization involves cores, multiminicores, and myosin chain depletion. Myofibrillar myopathies associate a pathologic pattern of myofibrillar dissolution and ectopic protein expression. They can be divided into two groups: myofibrillar myopathies with multiple expression proteins and myofibrillar myopathies with desmin and αB-crystallin expression only. In these two conditions, electron microscopy shows accumulation of a granulofilamentous material immunoreactive for desmin. At least three genes are implicated: desmin, αB-crystallin, and myotilin. Lastly, electron microscopy serves to identify changes, pathogenic or not, which are not shown up by light microscopy. Moreover, electron microscopy gives insight on pathophysiological mechanisms and can guide molecular genetics analysis.

Piège diagnostique et difficultés du conseil génétique dans une famille de patients porteurs de maladies neuromusculaires

Background. – Recent advances in the field of molecular genetics have provided useful tools for the diagnosis of neuromuscular disorders. Genetic counselling for many of these conditions may, however, be fraught with difficulties. Case report. – The patient, two paternal uncles and a paternal aunt presented with clinical and electromyographic evidence of type III spinal muscular atrophy despite an autosomal dominant-like pedigree. The diagnosis was confirmed by genetic testing for the SMN deletion. As the probandˈs mother was pregnant at the time of presentation of the affected child, a prenatal diagnostic test was performed. The deletion was not found in the DNA extracted from the trophoblast and the pregnancy proceeded to full term, and a normal child. At the same time, a first cousin of the proband was found to have a clinically similar condition. He had not the SMN deletion. He presented with electrophysiological and pathological features of limb-girdle muscular dystrophy. Genetic testing revealed a homozygote del T521 mutation of the gama-sarcoglycan gene. Conclusion. – To provide accurate genetic counselling, it is essential to get precise data on family background and diagnostic confirmation for each affected relative to avoid missing the possibility, albeit rare, of several neuromuscular disorders within a family.

A method for diagnosing multiple diseases in MUNIN.

A new method for diagnosing multiple diseases in large medical decision support systems based on causal probabilistic networks is proposed. The method is based on characteristics of the diagnostic process that we believe to be present in many diagnostic tasks, both inside and outside medicine. The diagnosis must often be made under uncertainty, choosing between diagnoses that each have small prior probabilities, but not so small that the possibility of two or more simultaneous diseases can be ignored. Often a symptom can be caused by several diseases and the presence of several diseases tend to aggravate the symptoms. For diagnostic problems that share these characteristic, we have proposed a method that operates in a number of phases: in the first phase only single diseases are considered and this helps to focus the attention on a smaller number of plausible diseases. In the second phase, pairs of diseases are considered, which make it possible to narrow down the field of plausible diagnoses further. In the following phases, larger subsets of diseases are considered. The method was applied to the diagnosis of neuromuscular disorders, using previous experience with the so-called MUNIN system as a starting point. The results showed that the method gave large reductions in computation time without compromising the computational accuracy in any substantial way. It is concluded that the method enables practical inference in large medical expert systems based on causal probabilistic networks.

ART2 Neural Network for Surface EMG Decomposition

Extraction of individual Motor Unit Action Potentials (MUAPs) from a surface ElectroMyoGram (EMG) is an essential but challenging task for clinical study and physiological investigation. This paper presents an automatic decomposition of surface EMGs using a self-organised ART2 neural network. In our approach, MUAP peaks are first detected using a Weighted Low-Pass Differential (WLPD) filter. A modified ART2 network is then utilised to classify MUAPs based on MUAP waveforms and firing time information. Individual MUAP trains are identified from real surface EMG signals recorded during weak contraction, and also from simulated surface EMGs. The firing statistics and the waveforms of individual MUAPs are then extracted. A number of computer tests on 50 simulated and real surface EMGs of limb muscles show that up to five MUAP trains can be effectively extracted, with their waveforms and firing parameters estimated. Being able to decompose real surface EMGs has essentially demonstrated the potential applications of our approach to the non-invasive diagnosis of neuromuscular disorders.

Non-invasive approach of motor unit recording during muscle contractions in humans.

Information about the structural and functional characteristics of the motor unit (MU) is highly relevant for the diagnosis of neuromuscular disorders. Electromyography (EMG) is a suitable method for obtaining the information needed. The problem is the separation of the activity of one MU from others which are simultaneously active. Such investigations of single MU activity have commonly used invasive methods, e.g. employing a needle or a wire. Conventional surface-EMG methods have limited resolution and detect, at high contraction levels, multiple MU superimposed one on the other. The separation of the activity of a single MU can be achieved in a non-invasive way when highly specialised acquisition techniques are used. One approach, called high spatial resolution EMG (HSR-EMG), is based on the use of multi-electrode arrays in combination with a two-dimensional Laplace filter. The HSR-EMG permits the completely non-invasive detection of single MU activity even during maximal voluntary contractions. First applications have shown that the method provides a deeper insight into the functional and structural characteristics of the MU. In this paper the application of HSR-EMG to the diagnosis of neuromuscular disorders will be presented, and the latest results will be given of its application in the evaluation of treatment of patients with plexus lesion.

Hypotonic infants and the Prader-Willi Syndrome

OBJECTIVE: To describe 6 patients with less than 3 years of age that were diagnosed with Prader-Willi syndrome (PWS) due to hypotonia, poor sucking, slight facial anomalies and minor abnormalities of hands and feet. PWS is a neurobehavioural disorder characterized by two distinct phases; in the first, the neonate presents variable degree of hypotonia, feeding problems with none or poor sucking; hypogonadism, characteristic facial features with almond shaped eyes, narrow bifrontal diameter and down-turned corners of the mouth. Neuropsichymotor development is delayed. Hypotonia is non progressive and tends to improve between 8 and 11 months of age. The second phase then starts and is characterized by increasing hyperphagia and obesity, among other features. Unfortunately, most PWS patients are diagnosed only after obesity is installed. METHODS: Methylation, microsatellites analysis and karyotypic studies by traditional and in situ hybridization techniques were done. RESULTS: A deletion of chromosome segment 15q11q13 was disclosed in 4 and maternal disomy in two patients. CONCLUSION: The diagnosis of PWS is generally established after the onset of obesity. So, we suggest that the genetic analysis must be carried out in children with severe hypotonia of unknown cause, poor sucking and some facial features of PWS (small hands and feet, hypogonadism, hypopigmentation, almond eyes and narrow bifrontal diameter). This can allow the early diagnosis and avoid invasive exams necessary for neuromuscular disorder diagnosis like muscle biopsy and electroneuromiography, wich frequently are associated with inconclusives results.

Noninvasive estimation of motor unit conduction velocity distribution using linear electrode arrays.

Determining the conduction velocity of motor unit action potentials is one of the most important problems in surface electromyography. The estimate of one average conduction velocity value depends on a variety of uncontrollable factors. More meaningful information is obtained from the estimation of the distribution of the different delays in the myoelectric signals. A solution to the problem is the separation and characterization of the individual components propagating at different velocities. A technique, based on surface electrode array recording, is proposed to estimate motor unit conduction velocity distribution. The method consists in the identification of the single action potentials in the time scale domain (with the continuous wavelet transform) and in the estimation of their conduction velocities based on the beamforming algorithm. The performances of the technique have been evaluated using simulated and real myoelectric signals. The results demonstrate that the technique is accurate and reliable. The method may be useful for the diagnosis of neuromuscular disorders, for the monitoring of muscle fatigue and for noninvasive investigation of individual motor units.

Chapter 19 Physiology and electromyography of swallowing and its disorders

The objective evaluation of the swallowing is clinically very important. For 80% of all individuals with dysphagia, neurological disorders are responsible. Swallowing is usually subdivided into four phases: an initial oral phase (including both oral preparatory and oral propulsive activity), a pharyngeal phase and an esophageal phase. The oral phase is mainly voluntary and highly variable in duration depending upon food consistency, taste, environment, hunger and motivation. The oral and pharyngeal phases are highly interrelated; the distinction between them is often unclear. When the pharyngeal phase begins, the following physiological events occur in rapid overlapping sequence so that they are nearly simultaneous. The approach to the diagnosis of impaired swallowing is two-fold, requiring both etiological and physiological considerations. The etiological approach depends on differential diagnosis while the physiological approach depends on analysis of the mechanism of swallowing. The EMG methods are derived from the known physiological events of oropharyngeal swallowing, which are amenable to clinical EMG techniques. The onset and duration of pharyngeal swallowing can be recorded from submental/suprahyoid muscles (SM-EMG). Standard needle EMG is sometimes performed for muscles innervated by the lower cranial muscles, such as tongue, larynx, and facial muscles. These studies are useful for differential diagnosis of neuromuscular disorders causing dysphagia and dysphonia but pharyngeal EMG is performed infrequently. Kinesiologic EMG also has a crucial role in the evaluation of the swallowing and its disorders.

New possibilities for prenatal diagnosis of muscular dystrophies: forced myogenesis with an adenoviral MyoD-vector

In some requests for prenatal diagnosis of neuromuscular disorders, such as Duchenne muscular dystrophy (DMD), the analysis yields an unsatisfactory risk estimate and in some cases a conclusive answer is not possible at all. Such results derive from the fact that the actual mutation is unknown and thus indirect tests have to be performed. These tests are problematic when no patient material is available or when recombinations hamper haplotype analysis.

Métodos automáticos de cuantificación de potenciales de unidad motora

The study of motor unit potentials (MUP) may be carried out using qualitative or quantitative methods. The considerable usefulness of quantitative methods in the diagnosis of neuromuscular disorders has led to development of new automatic techniques which permit greater speed and accuracy of clinical investigation. In this review we consider traditional electromyographic techniques and describe some automatic methods. Qualitative techniques are simpler but give limited information since they only permit recording of very marked alterations. The introduction of trigger and averaging permit quantitative analysis and thus the evaluation of less obvious changes. Although they do not identify all the potentials of an electromyographic signal, the new automatic techniques using partial decomposition avoid the loss of time occurring with earlier techniques, give reliable objective measurements of the MUP and also the possibility of measuring new parameters, such as the area or frequency of discharge. New methods of evaluation of the MUP have significant advantages over traditional methods, namely increased speed and objectivity. However, this makes it necessary to redefine normal values since different methods are used for obtaining and processing the signals, otherwise the results may not be strictly comparable.

Diagnóstico neurofisiológico de las miopatías

Electromyography is a technique widely used to assist diagnosis of neuromuscular disorders. The neuromuscular system as a whole (neuron, nerve, neuromuscular junction and muscle) have several electrochemical properties which may be detected by suitable apparatus and are the basis of electromyography. Healthy muscle has particular characteristics which are affected by structural/functional changes, whether they are of primary origin (myopathies) or secondary to alterations at other levels (secondary muscle involvement). In this paper we wish to summarize the electromyographic findings typical of myopathies, those which differentiate these from other neuromuscular disorders and to establish when possible the characteristic signs of each form of presentation. Finally we give a brief bibliography which we consider to be basic to understanding this diagnostic technique in this type of illness.

EMG-lnterferenzmusteranalyse

EMG interference pattern analysis is helpful in diagnosing neuromuscular disorders and thus an essential part of electromyography. Which of the various methods is the most sensitive is still a matter of debate. The aim of this review is to describe the different methods of EMG interference pattern analysis and to compare their sensitivity with regard to the diagnosis of neuromuscular disorders. Principally, subjective and objective methods can be distinguished. Objective methods, especially when carried out automatically, are favoured, because they provide quantitative data, allow comparing the results of different subjects and laboratories, allow Statistical analysis and minimise the observer bias. Automatic methods that do without force monitoring are preferred. Among these the turn/amplitude analysis has gained the widest acceptance, in particular the cloud method by Stalberg and Nandedkar and the peak-ratio method by Fuglsang-Frederiksen. Compared to the cloud method, the peak-ratio method is more sensitive, quicker and easier to perform and provides information about the whole pool of recruitable motor units.

Principles of high-spatial-resolution surface EMG (HSR-EMG): single motor unit detection and application in the diagnosis of neuromuscular disorders

The most detailed information about the structural and functional characteristics of the muscle can be gained from the single motor unit (MU) action potential. In addition, information about the activity of a single MU is essential for the diagnosis of neuromuscular disorders. Due to the low spatial resolution of conventional bipolar surface electromyography (EMG), the resulting signal is a superposition of a large number of simultaneous active MUs. The difficulty is in separating the activity of a single MU from simultaneous active adjacent MUs. In contrast to other non-invasive EMG procedures, the high-spatial-resolution-EMG (HSR-EMG), which is based on the use of a multi-electrode array in combination with a spatial filter procedure, allows the detection of single MU activity in a non-invasive way. It opens access to the excitation spread and enables the determination of the conduction velocity in single MUs, and the localization of the endplate region. In addition, HSR-EMG detects changes in the electrical activities of the MUs which are typical in neuromuscular disorders. Using HSR-EMG it was possible to identify 97% of all investigated volunteers and patients with muscular or neuronal disorders. Therefore, HSR-EMG is suitable as a tool for the non-invasive diagnosis of neuromuscular disorders.

Diagnostic biopsy of the motor nerve to the gracilis muscle

A biopsy specimen of the motor nerve to the gracilis muscle was obtained in 151 patients to aid in the diagnosis of neuromuscular disorders. The procedure is simple, safe, and has low morbidity and complication rates. The surgical technique is described. Indications for the use of this technique are motor neuropathy, motor neuron disease, Guillain-Barre syndrome, and mixed neuropathy with prominent motor symptoms.

Diagnostic biopsy of the motor nerve to the gracilis muscle

A biopsy specimen of the motor nerve to the gracilis muscle was obtained in 151 patients to aid in the diagnosis of neuromuscular disorders. The procedure is simple, safe, and has low morbidity and complication rates. The surgical technique is described. Indications for the use of this technique are motor neuropathy, motor neuron disease, Guillain-Barre syndrome, and mixed neuropathy with prominent motor symptoms.

Evaluation of the diagnostic performance of the expert EMG assistant MUNIN

The diagnostic performance of the medical expert system MUNIN for diagnosis of neuromuscular disorders was evaluated on a set of 30 test cases. The cases were provided by 7 experienced electromyographers who were subsequently invited to participate in the evaluation. To reasonably cover the range of disorders, the electromyographers were asked to provide cases from patients with different types of muscular dystrophy, with neuromuscular transmission disorders, with motor neurone disease, and with different types of polyneuropathies. In addition, patients with a range of local neuropathies were provided. Out of the 30 cases, 11 cases were evaluated by an “almost peer review” method and the remaining 19 cases were evaluated by a “silver standard” method. The number of cases evaluated by “almost peer review” was limited to 11 due to time constraints on the evaluation procedure. During the “almost peer review”, each electromyographer was asked to diagnose patients, using a vocabulary that closely resembled MUNIN's vocabulary. Subsequently, we attempted to provide a consensus diagnosis for the patients based on discussion among the participating electromyographers. The electromyographers were also asked to assess how well MUNIN had performed in each case. The remaining 19 cases were evaluated by a “silver standard” procedure, where MUNIN's diagnosis was compared to the diagnosis of the expert who provided the case. The results indicated that MUNIN performed well, and the electromyographers considered “that MUNIN performed at the same level as an experienced neurophysiologist.” In particular, it was noted that MUNIN handled cases with conflicting findings well, and that it was able to diagnose patients with multiple diseases.

Genetics-based machine learning for the assessment of certain neuromuscular disorders

Clinical electromyography (EMG) provides useful information for the diagnosis of neuromuscular disorders. The utility of artificial neural networks (ANN's) in classifying EMG data trained with backpropagation or Rohonen's self-organizing feature maps algorithm has recently been demonstrated. The objective of this study is to investigate how genetics-based machine learning (GBML) can be applied for diagnosing certain neuromuscular disorders based on EMG data. The effect of GBML control parameters on diagnostic performance is also examined. A hybrid diagnostic system is introduced that combines both neural network and GBML models. Such a hybrid system provides the end-user with a robust and reliable system, as its diagnostic performance relies on more than one learning principle. GBML models demonstrated similar performance to neural-network models, but with less computation. The diagnostic performance of neural network and GBML models is enhanced by the hybrid system.

Needle EMG of the human diaphragm: Power spectral analysis in normal subjects

Needle EMG of the diaphragm was performed in 43 diaphragms in 23 healthy volunteers. The mean +/- standard deviation for the median frequency (MF) of the power spectrum was 233.3 +/- 58.1 Hz. The MF increased with age and showed a negative correlation with the forced vital capacity (FVC), but there was no correlation with other anthropometric measures or the results of phrenic nerve conduction study. The higher MF in older subjects may be due to early recruitment of larger units. The negative correlation between MF and FVC is likely secondary to a lower level of contraction required for normal breathing in subjects with high FVC. The integrated EMG (iEMG) of each inspiration strongly correlated with the tidal volume and the duration of inspiration. Power spectral analysis of diaphragmatic EMG is feasible and reliable. It can be used to assess respiratory muscle fatigue and may help in the diagnosis of neuromuscular disorders affecting the diaphragm.