Hossein Parsaei

EMG Signal Decomposition Using Motor Unit Potential Train Validity

A system to resolve an intramuscular electromyographic (EMG) signal into its component motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters of motor units (MUs), such as their motor unit potential (MUP) templates and mean firing rates, are of interest. The system filters an EMG signal, detects MUPs, and clusters and classifies the detected MUPs into MUPTs. Clustering is partially based on the K-means algorithm, and the supervised classification is implemented using a certainty-based algorithm. Both clustering and supervised classification algorithms use MUP shape and MU firing pattern information along with signal dependent assignment criteria to obtain robust performance across a variety of EMG signals. During classification, the validity of extracted MUPTs are determined using several supervised classifiers; invalid trains are corrected and the assignment threshold for each train is adjusted based on the estimated validity (i.e., adaptive classification). Performance of the developed system in terms of accuracy (Ae), assignment rate (Ar), correct classification rate (CCr), and the error in estimating the number of MUPTs represented in the set of detected MUPs (ENMUPTs) was evaluated using 32 simulated and 30 real EMG signals comprised of 3-11 and 3-15 MUPTs, respectively. The developed system, with average CCr of 86.4% for simulated and 96.4% for real data, outperformed a previously developed EMG decomposition system, with average CCr of 71.6% and 89.7% for simulated and real data, by 14.7% and 6.7%, respectively. In terms of ENMUPTs, the new system, with average ENMUPTs of 0.3 and 0.2 for simulated and real data respectively, was better able to estimate the number of MUPTs represented in a set of detected MUPs than the previous system, with average ENMUPTs of 2.2 and 0.8 for simulated and real data respectively. For both the simulated and real data used, variations in Ac, Ar, and ENMUPTs for the newly developed system were lower than for the previous system, which demonstrates that the new system can successfully adjust the assignment criteria based on the characteristics of a given signal to achieve robust performance across a wide variety of EMG signals, which is of paramount importance for successfully promoting the clinical application of EMG signal decomposition techniques.

Validating motor unit firing patterns extracted by EMG signal decomposition

Motor unit (MU) firing pattern information can be used clinically or for physiological investigation. It can also be used to enhance and validate electromyographic (EMG) signal decomposition. However, in all instances the validity of the extracted MU firing patterns must first be determined. Two supervised classifiers that can be used to validate extracted MU firing patterns are proposed. The first classifier, the single/merged classifier (SMC), determines whether a motor unit potential train (MUPT) represents the firings of a single MU or the merged activity of more than one MU. The second classifier, the single/contaminated classifier (SCC), determines whether the estimated number of false-classification errors in a MUPT is acceptable or not. Each classifier was trained using simulated data and tested using simulated and real data. The accuracy of the SMC in categorizing a train correctly is 99% and 96% for simulated and real data, respectively. The accuracy of the SCC is 84% and 81% for simulated and real data, respectively. The composition of these classifiers, their objectives, how they were trained, and the evaluation of their performances using both simulated and real data are presented in detail.

SVM-Based Validation of Motor Unit Potential Trains Extracted by EMG Signal Decomposition

Motor unit potential trains (MUPTs) extracted via electromyographic (EMG) signal decomposition can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid. In this paper, support vector machine (SVM)-based supervised classifiers are proposed to estimate the validity of extracted MUPTs. The classifiers use either the MU firing pattern or the MUP shape consistency of an MUPT, or both, to estimate its validity. The developed classifiers estimate the class label of an MUPT (i.e., valid/invalid) and a degree of support for the decision being made. A single SVM that estimates the validity of a given MUPT using extracted MU firing pattern and MUP shape features was investigated. In addition, the effectiveness of multiclassifier techniques which estimate the overall validity of a train by fusing the MU firing pattern and MUP shape validity of a given MUPT, determined separately by two distinct SVMs, was also investigated. Training based only on simulated data showed robust classification performance of the several multiclassifier methods when tested using both simulated and real test data. Of the methods studied, the multiclassifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance. Assuming 12.7% of extracted MUPTs are on average invalid, the estimated accuracy for this method in correctly categorizing MUPTs extracted during decomposition was 99.4% and 98.8% for simulated and real data, respectively.

Adaptive motor unit potential train validation using MUP shape information

A decomposed electromyographic (EMG) signal provides information that can be used clinically or for physiological investigation. However, in all instances the validity of the extracted motor unit potential trains (MUPTs) must first be determined because, as with all pattern recognition applications, errors will occur during decomposition. Moreover, detecting invalid MUPTs during EMG signal decomposition can enhance decompositions results. Eight methods to validate an extracted MUPT using its motor unit potential (MUP) shape information were studied. These MUPT validation methods are based on existing cluster analysis algorithms, four were newly developed adaptive methods and four were classical cluster validation methods. The methods evaluate the shapes of the MUPs of a MUPT to determine whether the MUPT represents the activity of a single motor unit (i.e. it is a valid MUPT) or not. Evaluation results using both simulated and real data show that the newly developed adaptive methods are sufficiently fast and accurate to be used during or after the decomposition of EMG signals. The adaptive gap-based Duda and Hart (AGDH) method had significantly better accuracies in correctly categorizing the MUPTs extracted during decomposition (91.3% and 94.7% for simulated and real data, respectively; assuming 12.7% of the extracted MUPTs are on average invalid). The accuracy with which invalid MUPTs can be detected is dependent on the similarity of the MUP templates of the MUPTs merged to create the invalid train and suggests the need, in some cases, for the combined use of motor unit firing pattern and MUP shape information.

A method for detecting and editing MUPTs contaminated by false classification errors during EMG signal decomposition

A robust method for detecting motor unit potential trains (MUPTs) contaminated with false classification errors (FCEs) during EMG signal decomposition and then removing the FCEs from a contaminated train is presented. Using motor unit (MU) firing pattern information provided by each MUPT, the developed algorithm first determines whether a given train is contaminated by high number of FCEs and needs to be edited. For contaminated MUPTs, the method uses both MU firing pattern and motor unit potential (MUP) shape information to detect MUPs that were erroneously assigned to the train (i.e., represent FCEs). For the simulated data used in this study contaminated MUPTs could be detected with 88.7% accuracy. For a given contaminated MUPT, the algorithm on average correctly detected 83.4% of the FCEs and left 93.4% of the correctly assigned MUPs. The accuracy of the MUPs classified to a MUPT was estimated to be 92.1% on average.

Validation of motor unit potential trains using motor unit firing pattern information

A robust and fast method to assess the validity of a motor unit potential train (MUPT) obtained by decomposing a needle-detected EMG signal is proposed. This method determines whether a MUPT represents the firings of a single motor unit (MU) or the merged activity of more than one MU, and if is a single train it identifies whether the estimated levels of missed and false classification errors in the MUPT are acceptable. Two supervised classifiers, the Single/Merged classifier (SMC) and the Error Rate classifier (ERC), and a linear model for estimating the level of missed classification error have been developed for this objective. Experimental results using simulated data show that the accuracy of the SMC and the ERC in correctly categorizing a train is 99% and %84 respectively.

A new feature selection method for classification of EMG signals

Discrimination of neuromuscular diseases based on electromyogram (EMG) is still a hot topic among the rehabilitation society. Although many attempts have been made to elicit informative features from the discretized EMG signals, traditional visual inspection is still their gold-standard method. Therefore, this paper is aimed at introducing an effective combinational feature to enhance the classification rate among the control group and subjects with neuropathy and myopathy diseases. All EMG signals were artificially simulated, by incorporating statistical and morphological properties of each group into their signal models, in the EMG laboratory of Waterloo University. To classify the subjects by the proposed method, first, EMG signals are decomposed by empirical mode decomposition (EMD) to its natural subspaces, then number of subspaces is aligned through all windowed signals, and Kolmogorov Complexity (KC) and other informative feature are determined to reveal the amount of irregularity within each subspace. Finally, these features are applied to support vector machine (SVM). Experimental results show our method can differentiate these three groups efficiently.

Augmenting the decomposition of EMG signals using supervised feature extraction techniques

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its constituent motor unit potential trains (MUPTs). In this work, the possibility of improving the decomposing results using two supervised feature extraction methods, i.e., Fisher discriminant analysis (FDA) and supervised principal component analysis (SPCA), is explored. Using the MUP labels provided by a decomposition-based quantitative EMG system as a training data for FDA and SPCA, the MUPs are transformed into a new feature space such that the MUPs of a single MU become as close as possible to each other while those created by different MUs become as far as possible. The MUPs are then reclassified using a certainty-based classification algorithm. Evaluation results using 10 simulated EMG signals comprised of 3-11 MUPTs demonstrate that FDA and SPCA on average improve the decomposition accuracy by 6%. The improvement for the most difficult-to-decompose signal is about 12%, which shows the proposed approach is most beneficial in the decomposition of more complex signals.

MUP shape-based validation of a motor unit potential train

A method using the gap statistic is proposed to evaluate the validity of a motor unit potential train (MUPT) in terms of motor unit potential (MUP) shape consistency. This algorithm determines whether the MUPs of a given MUPT are homogeneous in terms of their shapes or not. It also checks if there are gaps in the inter-discharge interval (IDI) train of the given MUPT. If the MUPs are not homogeneous or if there is a temporal gap in the MUPT, the given MUPT is split into valid trains. To overcome MUP shape variability caused by jitter or needle movement during signal detection, similar MUPTs are merged if the resulting merged train is a valid train. Experimental results using simulated EMG signals show that the accuracy of the developed method in determining valid MUPTs and invalid MUPTs correctly is 97.58% and 99.33% on average, respectively. This performance encourages the use of this method for automated validation of MUPTs.

Motor Unit Potential Train Validation and Its Application in EMG Signal Decomposition

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its constituent motor unit potential trains (MUPTs). The purpose of EMG signal decomposition is to provide an estimate of the firing pattern and motor unit potential (MUP) template of each active motor unit (MU) that contributed significant MUPs to the EMG signal. The extracted MU firing patterns, MUP templates, and their estimated feature values can assist with the diagnosis of neuromuscular disorders (Stalberg & Falck, 1997; Troger & Dengler, 2000; Fuglsang-Frederiksen, 2006; Pino et al., 2008; Farkas et al., 2010), the understanding of motor control ( De Luca et al. 1982a, 1982b; Contessa et al.,2009), and the characterization of MU architecture (Lateva & McGill, 2001), but only if they are valid trains. Depending on the complexity of the signal being decomposed, the variability of MUP shapes and MU firing patterns, and the criteria and parameters used by the decomposition algorithm to merge or split the obtained MUPTs, several invalid MUPTs may be created.