Filter design for cancellation of baseline-fluctuation in needle EMG recordings

Comparing Different Filter-Parameter for Pre-Processing of Brain-Stimulation Evoked Motor Potentials

Background: Brain stimulation motor-evoked potentials (MEPs) are transient signals and not periodic signals, and thus, they differ significantly in their properties from classical surface electromyograms. Unsuitable pre-processing of MEPs due to inappropriate filter settings leads to distortions. Filtering of extensor carpi radialis MEPs with transient signal characteristics of 20 subjects was examined. The effects of a 1st-order Butterworth high-pass filter (HPF) with different cut-off frequencies 1 Hz, 20 Hz, 40 Hz, and 80 Hz and a 5 Hz Butterworth high-pass filter with degrees 1st, 2nd, 4th, 8th-order are investigated for the filter output. Results: The filtering of the MEPs with an inappropriate filter setting led to distortions on the parameters peak-to-peak amplitudes of the MEP (MEP_pp) and the absolute integral of the MEP (MEP_area). The lowest distortions of all of the examined filter parameters were revealed after filtering with the lowest filter order and the lowest cut-off frequency. The 1st-order 1 Hz HPF calculation results in a difference of −0.53% (p < 0.001) for the MEPpp and −1.94% (p < 0.001) for the MEParea. Significance: Reproducibility is a major concern in science, including brain stimulation research. Only the filtering of the MEPs with appropriate filter settings led to mostly undistorted MEPs.

Masked least-squares averaging in processing of scanning-EMG recordings with multiple discharges

Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked least-squares smoothing (MLSS) can be used to eliminate artifacts in recordings with just one discharge of the motor unit potential (MUP) at each location. However, more effective artifact removal can be achieved if several discharges per position are recorded. In this case, processing usually involves averaging the discharges available at each position and then applying a median filter in the spatial dimension. The main drawback of this approach is that the median filter tends to distort the signal waveform. In this paper, we present a new algorithm that operates on multiple discharges simultaneously and in the spatial dimension. We refer to this algorithm as the multi-masked least-squares smoothing (MMLSS) algorithm: an extension of the MLSS algorithm for the case of multiple discharges. The algorithm is tested using simulated scanning-EMG signals in different recording conditions, i.e., at different levels of muscle contraction and for different numbers of discharges per position. The results demonstrate that the algorithm eliminates artifacts more effectively than any previously available method and does so without distorting the waveform of the signal. Graphical abstract The raw scanning-EMG signal, which can be composed by several discharges of the MU, is processed by the MMLSS algorithm so as to eliminate the artifact interference. Firstly, artifacts are detected for each discharge from the raw signal, obtaining a multi-discharge validity mask that indicates the samples that have been corrupted by artifacts. Secondly, a least-squares smoothing procedure simultaneously operating in the spatial dimension and among the discharges is applied to the raw signal. This second step is performed using only the not contaminated samples according to the validity mask. The resulting MMLSS-processed scanning-EMG signal is clean of artifact interference.

A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings

Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components.

Graphical Abstract

Features for muscle fatigue computed from electromyogram and mechanomyogram: A new one

Muscle fatigue produces negative effects in the performance and it may lead to a muscle failure. This problem makes the quantitative grading of muscle fatigue a necessity in ergonomic and physiological research. Moreover, the quantitative grading of muscle fatigue is needed to increase work and sport productivity and prevent several accidents that result from muscle fatigue. Even though there are many studies for this aim, there is no quantitative criterion for the evaluation of muscle fatigue. The main reason is that muscle fatigue is a complex physiological situation that is dependent on several parameters. Our aim in this study is to present a new feature to evaluate muscle fatigue and prove the reliability of the new feature by making correlation analyses between this with other features. For this aim, electromyography and mechanomyography signals were simultaneously recorded from the biceps brachii and triceps brachii muscles during the isometric and isotonic contractions of 60 healthy volunteers (30 females, 30 males). The mean power frequency and median frequency, which are used in the literature, were compared to the frequency ratio change, the new measure; correlations between the frequency ratio change and the mean power frequency and median frequency were analysed. There was a high correlation between the features, and frequency ratio change can be used to quantitatively evaluate muscle fatigue.

A modified algorithm of the combined ensemble empirical mode decomposition and independent component analysis for the removal of cardiac artifacts from neuromuscular electrical signals

Neuronal and muscular electrical signals contain useful information about the neuromuscular system, with which researchers have been investigating the relationship of various neurological disorders and the neuromuscular system. However, neuromuscular signals can be critically contaminated by cardiac electrical activity (CEA) such as the electrocardiogram (ECG) which confounds data analysis. The purpose of our study is to provide a method for removing cardiac electrical artifacts from the neuromuscular signals recorded. We propose a new method for cardiac artifact removal which modifies the algorithm combining ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA). We compare our approach with a cubic smoothing spline method and the previous combined EEMD and ICA for various signal-to-noise ratio measures in simulated noisy physiological signals using a surface electromyogram (sEMG). Finally, we apply the proposed method to two real-life sets of data such as sEMG with ECG artifacts and ambulatory dog cardiac autonomic nervous signals measured from the ganglia near the heart, which are also contaminated with CEA. Our method can not only extract and remove artifacts, but can also preserve the spectral content of the neuromuscular signals.

Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury

Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals.

Eliminating cardiac electrical artifacts from cardiac autonomic nervous signals using a combination of empirical mode decomposition and independent component analysis

Cardiac autonomic nervous (CAN) signals in ambulatory dogs can nowadays be measured by an implantable radio transmitter system. CAN signals are known to be related to heart failure. However, they are critically contaminated by cardiac electrical activities (CEA) which confound data analysis. We propose a method of analysis which combines empirical mode decomposition (EMD) and independent component analysis (ICA). This method composed of two steps: First, the EMD method decomposed a single channel recording into multichannel data, then we applied the ICA to these multichannel data. Using an ambulatory dog's CAN signal data from Seoul National University Hospital, we compared our approach with a commonly used high pass filter (HPF) method for various amplitudes of simulated CAN signals. Root-mean-squared errors between simulated CAN signals and CAN signals with CEA artifact were calculated for assessing the noise cancellation effect. Moreover, we observed changes in spectral content via power spectral density. Finally, we applied the proposed method to real data. Our method could not only extract and remove CEA artifact in CAN signals, but also preserved the spectral content of CAN signals.

Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders

This paper describes a new method for the classification of neuromuscular disorders based on the analysis of scalograms determined by the Symlet 4 wavelet technique. The approach involves isolating single motor unit action potentials (MUAPs), computing their scalograms, taking the maximum values of the scalograms in five selected scales, and averaging across MUAPs to give a single 5-dimensional feature vector per subject. After SVM analysis, the vector is reduced to a single decision parameter, called the Wavelet Index, allowing the subject to be assigned to one of three groups: myogenic, neurogenic or normal. The software implementation of the method described above created a tool supporting electromyographic (EMG) examinations. The method is characterized by a high probability for the accurate diagnosis of muscle state. The method produced 5 misclassifications out of 800 examined cases (total error of 0.6%).

Application of a novel automatic duration method measurement based on the wavelet transform on pathological motor unit action potentials

OBJECTIVE

To evaluate a recently published automatic duration method based on the wavelet transform applied on normal and pathological motor unit action potentials (MUAPs).

METHODS

We analyzed 313 EMG recordings from normal and pathological muscles during slight contractions. After the extraction procedure, 339 potentials were accepted for analysis: 68 from normal muscles, 124 from myopathic muscles, 20 from chronic neurogenic muscles, 83 from subacute neurogenic muscles and also 44 fibrillation potentials, as an example of very low duration muscular potentials. A "gold standard" of the duration positions (GSP) was obtained for each MUAP from the manual measurements of two senior electromyographists. The results of the novel method were compared to five well-known conventional automatic methods (CAMs). To compare the six methods, the differences between the automatic marker positions and the GSP for the start and end markers were calculated. Then, for the different groups of normal and pathological MUAPs, we applied: a one-factor ANOVA to compare their relative mean differences, the estimated mean square error (EMSE) and a Chi-square test about the rate of automatic marker placements with differences to the GSP greater than 5 ms, taken as gross errors.

RESULTS

The mean and the standard deviation of the differences, the EMSE and the gross errors for the novel method were smaller than those observed with the CAMs in the five different MUAP groups and significantly different in most of the cases.

CONCLUSIONS

The novel automatic duration method is more accurate than other available algorithms in normal and pathological MUAPs.

SIGNIFICANCE

Accurate MUAP duration automatic measurement is an important issue in daily clinical practice.

Wavelet analysis for Support Vector Machine classification of motor unit action potentials

The paper presents a new method for neuromuscular disorders diagnosis based on analysis of scalograms determined by the Symlet 4 wavelets technique. Obtained results served for extraction of five features, which, after SVM analysis, were reduced to a single decision parameter allowing assigning the investigated cases to one of three groups: myogenic, neurogenic or normal. Software implementation of the method permitted to create a diagnostic tool for EMG investigation aid. The method characterizes high probability of accurate diagnosis of a muscle state with total error of 0.5% - 4 misclassifications out of 780 examined cases.

Analysis of the peak-to-peak ratio of extracellular potentials in the proximity of excitable fibres

In a previous work we studied the ratio between the amplitudes of the second and first phases (which we call PPR, after peak-to-peak ratio) of the single fibre action potential (SFAP) for a collection of fibrillation potentials (FPs) extracted from two pathological muscles. These FPs showed a wider PPR range than the Dimitrov-Dimitrova (D-D) convolutional model could provide. We proposed a modification of the D-D intracellular action potential (IAP) in order to obtain a range of PPRs comparable to that observed in our FPs. This paper extends that study to a large number of SFAPs extracted from the tibialis anterior muscle of normal subjects. The estimation of the average PPR range of non-diseased muscles in non-fatigued conditions is important since it can be used as a reference to establish a comparison with PPR ranges from muscles suffering some disorder or from fibres that are fatigued. Other aspects of the PPR, as its sensitivity with volume conductor parameters or to what extent changes in the SFAP PPR reflects changes in IAP spatial profile are also examined. We found that the PPR of experimental SFAPs ranges from 0.3 to 2.5 in all subjects and that all PPR histograms contain a well-defined single peak around the PPR value 1.0.

Removing ECG noise from surface EMG signals using adaptive filtering

Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment for dystonia. These recordings are critically often contaminated by cardiac artefact. Our objective of this study was to evaluate the performance of an adaptive noise cancellation filter in removing electrocardiogram (ECG) interference from surface EMGs recorded from the trapezius muscles of patients with cervical dystonia. Performance of the proposed recursive-least-square adaptive filter was first quantified by coherence and signal-to-noise ratio measures in simulated noisy EMG signals. The influence of parameters such as the signal-to-noise ratio, forgetting factor, filter order and regularization factor were assessed. Fast convergence of the recursive-least-square algorithm enabled the filter to track complex dystonic EMGs and effectively remove ECG noise. This adaptive filter procedure proved a reliable and efficient tool to remove ECG artefact from surface EMGs with mixed and varied patterns of transient, short and long lasting dystonic contractions.

Linear discriminant analysis of MUAP scalograms

The paper presents a new approach to the computer aided diagnostic systems for the needs of quantitative electromyography. The approach is based on the analysis of wavelet scalograms of the motor unit action potentials calculated on the basis of 4th order Symlet wavelet. The scalograms provide the vector consisting of six features describing the state of a muscle that can be reduced to the two features with use of Linear Discriminant Analysis. Consequently, the healthy, myogenic and neurogenic cases can be successfully classified using the linear methods.

Spectral analysis of motor unit action potentials

The statistical processing of electromyographic signal examination performed in the time domain ensures mostly correct classification of pathology; however, because of an ambiguity of most temporal parameter definitions, a diagnosis can include a significant error that strongly depends on the neurologist's experience. Then, selected temporal parameters are determined for each run, and their mean values are calculated. In the final stage, these mean values are compared with a standard and, including additional clinical information, a diagnosis is given. An inconvenience of this procedure is high time consumption that arises from the necessity of determination of many parameters. Additionally, an ambiguity in determination of basic temporal parameters can cause doubts when parameters found by the physician are compared with standard parameters determined in other research centers. In this paper, we present a definition for spectral discriminant that directly enables a unique diagnosis to be made. An essential advantage of the suggested discriminant is a precise and algorithmically realized definition that enables an objective comparison of examination results obtained by physicians with different experiences or working in different research centers. A suggestion of the standard for selected muscle based on a population of 70 healthy cases is presented in the Results section.

Motor Unit Action Potential Duration, II: A New Automatic Measurement Method Based on the Wavelet Transform

The aim of this work is to present and evaluate a new algorithm, based on the wavelet transform, for the automatic measurement of motor unit action potential (MUAP) duration. A total of 240 MUAPs were studied. The waveform of each MUAP was wavelet-transformed, and the start and end points were estimated by regarding the maxima and minima points in a particular scale of the wavelet transform. The results of the new method were compared to the gold standard of duration marker positions obtained by manual measurement. The new method was also compared to a conventional algorithm, which we had found to be best in a previous comparative study. To evaluate the new method against manual measurements, the dispersion of automatic and manual duration markers were analyzed in a set of 19 repeatedly recorded MUAPs. The differences between the new algorithm's marker positions and the gold standard of duration marker positions were smaller than those observed with the conventional method. The dispersion of the new algorithm's marker positions was slightly less than that of the manual one. Our new method for automatic measurement of MUAP duration is more accurate than other available algorithms and more consistent than manual measurements.