Recursive decomposition of electromyographic signals with a varying number of active sources: Bayesian modelling and filtering

International audienceThis paper describes a sequential decomposition algorithm for single channel intramuscular electromyography (iEMG) generated by a varying number of active motor neurons. As in previous work, we establish a Hidden Markov Model of iEMG, in which each motor neuron spike train is modeled as a renewal process with inter-spike intervals following a discrete Weibull law and motor unit action potentials are modeled as impulse responses of linear time-invariant systems with known prior. We then expand this model by introducing an activation vector associated to the state vector of the Hidden Markov Model. This activation vector represents recruitment/derecruitment of motor units and is estimated together with the state vector using Bayesian filtering. Non-stationarity of the model parameters is addressed by means of a sliding window approach, thus making the algorithm adaptive to variations in contraction force and motor unit action potential waveforms. The algorithm was validated using simulated and experimental iEMG signals with varying number of active motor units. The experimental signals were acquired from the tibialis anterior and abductor digiti minimi muscles by fine wire and needle electrodes. The decomposition accuracy in both simulated and experimental signals exceeded 90% and the recruitment/derecruitment was successfully tracked by the algorithm. Because of its parallel structure, this algorithm can be efficiently accelerated, which lays the basis for its future real-time applications in human-machine interfaces, e.g. for prosthetic control

Sistema para auxiliar a detecção da Doença de Parkinson interpretando sinais de tremor de repouso parkinsoniano utilizando aprendizado de máquinas

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, Engenharia de Software, 2018.A Doença de Parkinson é uma doença neurológica que atinge uma grande parcela da população mundial, sendo majoritariamente pessoas idosas. Apesar de não terem sido descobertos os motivos efetivos que causam a doença, seu diagnóstico é possível graças aos sinais evidentes causados pela presença da mesma, sendo os mesmos suficientes. Ainda existe uma necessidade de se obter mais informações sobre o estado do paciente dessa doença para possíveis estudos sobre a mesma. Para garantir o acesso à essas informações e auxiliar no diagnóstico da Doença de Parkinson é possível utilizar os conceitos de Aprendizado de Máquina, que tem sido muito requisitado em diversas áreas do conhecimento, incluindo a área da saúde, e para a solução de problemas de diversos contextos. Este trabalho de conclusão de curso implementa um sistema denominado DPDP (Detector Preliminar da Doença de Parkinson) no qual é aplicado um modelo de Aprendizado de Máquina para o auxílio no diagnóstico preliminar da doença de Parkinson. Isso foi feito utilizando amostras recolhidas por dispositivos de coletas de sinais de eletromiográfia de superfície (sEMG), afim de reconhecer padrões e obter novas informações sobre a doença em questão. O sistema utiliza o algoritimo Random Forest para detecção dos padrões da doença, pois o mesmo obteve maior exito em comparação a outros modelos estudados neste trabalho.Parkinson’s disease is a neurological disease that affects a large portion of the world’s population, most of whom are elderly. Although the actual reasons that cause the disease have not been discovered, its diagnosis is possible thanks to the evident signs caused by the presence of the disease, it being sufficient. There is still a need to obtain more information about the state of the patient of this disease for possible studies about it. In order to guarantee access to this information and to assist in the diagnosis of Parkinson’s Disease it is possible to use the concepts of Machine Learning, which has been widely requested in several areas of knowledge, including health, and to solve problems of several contexts. This final project implements a system called DPDP (Preliminary Detector of Parkinson’s Disease, Detector Preliminar da Doença de Parkinson) in which a Machine Learning model is applied to aid in the preliminary diagnosis of Parkinson’s disease. This was done using Surface Electromyography (sEMG) signals, in order to recognize patterns and to obtain new information about the disease in question. The system uses the algorithm Random Forest to detect the patterns of the disease, once it was more successful in comparison with other models studied in this work

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

Máquinas de vetores suporte para classificação do Onset em dados temporais de eletromiografia

Os estudos sobre parâmetros temporais em eletromiografia (EMG) focam a sua análise tendencialmente no onset, existindo uma escassez quanto à descrição e discussão dos fenómenos temporais. A dependência nos parâmetros dos algoritmos de deteção do onset e os diferentes métodos comprometem a reprodutibilidade de resultados. O objetivo deste trabalho é assim testar a performance de diferentes features no domínio do tempo na construção de modelos de Máquinas de Vetor Suporte (SVM) quanto à localização do onset. Sinal EMG de superfície foi recolhido durante o swing do golfe de 12 músculos (tronco e membro inferior) de 12 golfistas, 6 de handicap (Hc) baixo ( =1.4±2.5 18). O sinal foi segmentado com janelas de 200 ms de 5 em 5 ms e depois foram extraídas as seguintes features no domínio do tempo: Valor Médio Absoluto, Comprimento do Formato da Onda, Diferença Absoluta do Desvio Padrão, Variância do EMG, Integral EMG e Detetor Logarítmico. As features foram selecionadas e ordenadas quanto à importância sendo construídos três conjuntos de 2, 4 e 6 features (F2, F4 e F6) para cada modelo. Após a realização de pesquisa de rede (grid-search), os melhores parâmetros quanto à precisão da classificação pelo modelo radial basis function (RBF) – SVM foram selecionados por cross-validation. O teste de Friedman foi aplicado para comparar os parâmetros ( , ) nos três conjuntos de features e a ANOVA mista para comparar a classificação e vetores suporte entre os grupos de features e grupos de handicap (alto Hc, baixo Hc e total). Verificamos que os grupos alto, baixo e total Hc apresentaram uma precisão de classificação de 90.3±4% (média±desvio-padrão), 90.8±4.9% e 89.4±3.7% para F2, 94.9±2.5%, 95.0±3.3%, 93.5%±3.2% para F4 e 95.2±2.4%, 95.1±3.2% e 93.6±3.3% para F6. Os valores dos parâmetros RBF, a classificação e o número de vetores suporte tende a ser similar entre F4 e F6, variando no entanto em relação a F2. Concluímos assim que quatro features garantem uma precisão na classificação superior a 90% em relação aos instantes de tempo classificados como antes e depois do onset podendo servir de base de construção de modelos SVM.Studies on temporal parameters in electromyography (EMG) focus their analysis on onset. However, the description and discussion of temporal phenomena themselves is scares and the results reproducibility is hard due to different parameters and methods. Thus, the aim of this work is to test the performance of different time-domain features building Support Vector Machines (SVM) models for onset detection. Surface EMG was collected from 12 muscles (trunk and lower limb) during the golf swing. Twelve golfers of two handicap (Hc) groups were recruited (6 low Hc=1.4±2.5 18). The signal was segmented with 200 ms windows, with a lag between windows of 5 ms followed by time-domain features extraction: Mean Absolute Value, Waveform Length, Difference Absolute Standard Deviation Value, Variance, Integrated EMG, and Integral logarithmic detector. The features were selected and ranked by relevance on three sets of 2, 4 and 6 features (F2, F4 and F6). After conducting grid-search for radial basis function (RBF) - SVM, the best parameters were selected for each model using cross-validation. The Friedman test was used to compare the parameters (C,γ) of different models. A mixed ANOVA was performed to compare the support vector classification and interaction between features model and handicap groups (high Hc, Hc and low total). The high, low, and total Hc groups showed a classification accuracy of 90.3 ± 4% (mean±standard deviation), 90.8±4.9% and 89.4±3.7% for F2, 94.9±2.5%, 95.0±3.3%, 93.5%±3.2% for F4% and 95.2±2.4, 95.1±3.2% and 93.6±3.3% to F6. RBF values of the prameters, classification and number of support vectors tends to be similar between F4 and F6, though varying in relation to F2. We conclude therefore that four features ensure an accuracy rate exceeding 90% in relation to the time classification as before and after the onset. Timedomain features could be a basis for constructing SVM classification models

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

EMG Signal Decomposition Using Motor Unit Potential Train Validity

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its component motor unit potential trains (MUPTs). The extracted MUPTs can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid trains. Before using decomposition results and the motor unit potential (MUP) shape and motor unit (MU) firing pattern information related to each active MU for either clinical or research purposes the fact that the extracted MUPTs are valid needs to be confirmed. The existing MUPT validation methods are either time consuming or related to operator experience and skill. More importantly, they cannot be executed during automatic decomposition of EMG signals to assist with improving decomposition results. To overcome these issues, in this thesis the possibility of developing automatic MUPT validation algorithms has been explored. Several methods based on a combination of feature extraction techniques, cluster validation methods, supervised classification algorithms, and multiple classifier fusion techniques were developed. The developed methods, in general, use either the MU firing pattern or MUP-shape consistency of a MUPT, or both, to estimate its overall validity. The performance of the developed systems was evaluated using a variety of MUPTs obtained from the decomposition of several simulated and real intramuscular EMG signals. Based on the results achieved, the methods that use only shape or only firing pattern information had higher generalization error than the systems that use both types of information. For the classifiers that use MU firing pattern information of a MUPT to determine its validity, the accuracy for invalid trains decreases as the number of missed-classification errors in trains increases. Likewise, for the methods that use MUP-shape information of a MUPT to determine its validity, the classification accuracy for invalid trains decreases as the within-train similarity of the invalid trains increase. Of the systems that use both shape and firing pattern information, those that separately estimate MU firing pattern validity and MUP-shape validity and then estimate the overall validity of a train by fusing these two indices using trainable fusion methods performed better than the single classifier scheme that estimates MUPT validity using a single classifier, especially for the real data used. Overall, the multi-classifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance with overall accuracy of 99.4% and 98.8% for simulated and real data, respectively. The possibility of formulating an algorithm for automated editing MUPTs contaminated with a high number of false-classification errors (FCEs) during decomposition was also investigated. Ultimately, a robust method was developed for this purpose. Using a supervised classifier and MU firing pattern information provided by each MUPT, the developed algorithm first determines whether a given train is contaminated by a high number of FCEs and needs to be edited. For contaminated MUPTs, the method uses both MU firing pattern and MUP shape information to detect MUPs that were erroneously assigned to the train. Evaluation based on simulated and real MU firing patterns, shows that contaminated MUPTs could be detected with 84% and 81% accuracy for simulated and real data, respectively. For a given contaminated MUPT, the algorithm on average correctly classified around 92.1% of the MUPs of the MUPT. The effectiveness of using the developed MUPT validation systems and the MUPT editing methods during EMG signal decomposition was investigated by integrating these algorithms into a certainty-based EMG signal decomposition algorithm. Overall, the decomposition accuracy for 32 simulated and 30 real EMG signals was improved by 7.5% (from 86.7% to 94.2%) and 3.4% (from 95.7% to 99.1%), respectively. A significant improvement was also achieved in correctly estimating the number of MUPTs represented in a set of detected MUPs. The simulated and real EMG signals used were comprised of 3–11 and 3–15 MUPTs, respectively