Convolution Kernel Compensation Research Articles

A fast gradient convolution kernel compensation method for surface electromyogram decomposition

Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm. Specifically, the new fgCKC algorithm considers the past gradient in addition to the current gradient. Furthermore, the EMG signals are divided by sliding windows to simulate real-time decomposition, and the proposed algorithm was validated on simulated and experimental signals. In the offline decomposition, fgCKC has the same robustness as gCKC, with sensitivity differences of 2.6 ± 1.3 % averaged across all trials and subjects. Nevertheless, depending on the number of MUs and the signal-to-noise ratio of signals, fgCKC is approximately 3 times faster than gCKC. In the real-time part, the processing only needed 240 ms average per window of EMG signals on a regular personal computer (IIntel(R) Core(TM) i5-12490F 3 GHz, 16 GB memory). These results indicate that fgCKC achieves real-time decomposition by significantly reducing processing time, providing more possibilities for non-invasive neuronal behavior research.

Acute effects of caffeine or quercetin ingestion on motor unit firing pattern before and after resistance exercise.

The aim of the present study was to investigate the acute effect of caffeine or quercetin ingestion on motor unit firing patterns and muscle contractile properties before and after resistance exercise. High-density surface electromyography (HDs-EMG) during submaximal contractions and electrically elicited torque in knee extensor muscles were measured before (PRE) and 60min after (POST1) ingestion of caffeine, quercetin glycosides, or placebo, and after resistance exercise (POST2) in ten young males. The Convolution Kernel Compensation technique was used to identify individual motor units of the vastus lateralis muscle for the recorded HDs-EMG. Ingestion of caffeine or quercetin induced significantly greater decreases in recruitment thresholds (RTs) from PRE to POST1 compared with placebo (placebo: 94.8 ± 9.7%, caffeine: 84.5 ± 16.2%, quercetin: 91.9 ± 36.7%), and there were significant negative correlations between the change in RTs (POST1-PRE) and RT at PRE for caffeine (rs = - 0.448, p < 0.001) and quercetin (rs = - 0.415, p = 0.003), but not placebo (rs = - 0.109, p = 0.440). Significant positive correlations between the change in firing rates (POST2-POST1) and RT at PRE were noted with placebo (rs = 0.380, p = 0.005) and quercetin (rs = 0.382, p = 0.007), but not caffeine (rs = 0.069, p = 0.606). No significant differences were observed in electrically elicited torque among the three conditions. These results suggest that caffeine or quercetin ingestion alters motor unit firing patterns after resistance exercise in different threshold-dependent manners in males.

Integration of Motor Unit Filters for Enhanced Surface Electromyogram Decomposition During Varying Force Isometric Contraction.

Muscles generate varying levels of force by recruiting different numbers of motor units (MUs), and as the force increases, the number of recruited MUs gradually rises. However, current decoding methods encounter difficulties in maintaining a stable and consistent growth trend in MU numbers with increasing force. In some instances, an unexpected reduction in the number of MUs can even be observed as force intensifies. To address this issue, in this study, we propose an enhanced decoding method that adaptively reutilizes MU filters. Specifically, in addition to the normal decoding process, we introduced an additional procedure where MU filters are reused to initialize the algorithm. The MU filters are iterated and adapted to the new signals, aiming to decode motor units that were actually activated but cannot be identified due to heavy superimposition. We tested our method on both simulated and experimental surface electromyogram (sEMG) signals. We simulated isometric signals (10%-70%) with known MU firing patterns using experimentally recorded MU action potentials from forearm muscles and compared the decomposition results to two baseline approaches: convolution kernel compensation (CKC) and fast independent component analysis (fastICA). Our method increased the decoded MU number by a rate of 135.4% ± 62.5 % and 63.6% ± 20.2 % for CKC and fastICA, respectively, across different signal-to-noise ratios. The sensitivity and precision for MUs decomposed using the enhanced method remained at the same accuracy level (p <0.001) as those of normally decoded MUs. For the experimental signals, eight healthy subjects performed hand movements at five different force levels (10%-90%), during which sEMG signals were recorded and decomposed. The results indicate that the enhanced process increased the number of decoded MUs by 21.8% ± 10.9 % across all subjects. We discussed the possibility of fully capturing all activated motor units by appropriately reusing previously decoded MU filters and improving the balance of activated motor unit numbers across varying excitation levels.

Real-Time Hand Gesture Recognition by Decoding Motor Unit Discharges Across Multiple Motor Tasks From Surface Electromyography.

Surface electromyography (EMG) decomposition techniques have been developed to decode motor neuron activities non-invasively in the past decades, showing superior performance in human-machine interfaces such as gesture recognition and proportional control. However, neural decoding across multiple motor tasks and in real-time remains challenging, which limits its wide application. In this work, we proposed a real-time hand gesture recognition method by decoding motor unit (MU) discharges across multiple motor tasks ( 10) in a motion-wise way. The EMG signals were first divided into numerous segments related to motions. The convolution kernel compensation algorithm was applied for each segment individually. The local MU filters, which indicate the MU-EMG correlation for each motion, were calculated iteratively in each segment and reused for global EMG decomposition to trace the MU discharges across motor tasks in real-time. The motion-wise decomposition method was applied on the high-density EMG signals recorded during twelve hand gesture tasks from eleven non-disabled participants. The neural feature of discharge count was extracted for gesture recognition based on five common classifiers. On average, 164 ±34 MUs were identified for twelve motions from each subject, with a pulse-to-noise ratio of 32.1 ±5.6 dB. The average time cost of EMG decomposition in a sliding window of 50 ms was less than 5 ms. The average classification accuracy using a linear discriminant analysis classifier was 94.6 ±8.1%, which was significantly higher than that of a time-domain feature called root mean square. The superiority of the proposed method was also validated with a previously published EMG database comprising 65 gestures. These results indicate the feasibility and superiority of the proposed method for MU identification and hand gesture recognition across multiple motor tasks, extending the potential applications of neural decoding in human-machine interfaces.

Decoding firings of a large population of human motor units from high-density surface electromyogram in response to transcranial magnetic stimulation.

We describe a novel application of methodology for high-density surface electromyography (HDsEMG) decomposition to identify motor unit (MU) firings in response to transcranial magnetic stimulation (TMS). The method is based on the MU filter estimation from HDsEMG decomposition with convolution kernel compensation during voluntary isometric contractions and its application to contractions elicited by TMS. First, we simulated synthetic HDsEMG signals during voluntary contractions followed by simulated motor evoked potentials (MEPs) recruiting an increasing proportion of the motor pool. The estimation of MU filters from voluntary contractions and their application to elicited contractions resulted in high (>90%) precision and sensitivity of MU firings during MEPs. Subsequently, we conducted three experiments in humans. From HDsEMG recordings in first dorsal interosseous and tibialis anterior muscles, we demonstrated an increase in the number of identified MUs during MEPs evoked with increasing stimulation intensity, low variability in the MU firing latency and a proportion of MEP energy accounted for by decomposition similar to voluntary contractions. A negative relationship between the MU recruitment threshold and the number of identified MU firings was exhibited during the MEP recruitment curve, suggesting orderly MU recruitment. During isometric dorsiflexion we also showed a negative association between voluntary MU firing rate and the number of firings of the identified MUs during MEPs, suggesting a decrease in the probability of MU firing during MEPs with increased background MU firing rate. We demonstrate accurate identification of a large population of MU firings in a broad recruitment range in response to TMS via non-invasive HDsEMG recordings. KEY POINTS: Transcranial magnetic stimulation (TMS) of the scalp produces multiple descending volleys, exciting motor pools in a diffuse manner. The characteristics of a motor pool response to TMS have been previously investigated with intramuscular electromyography (EMG), but this is limited in its capacity to detect many motor units (MUs) that constitute a motor evoked potential (MEP) in response to TMS. By simulating synthetic signals with known MU firing patterns, and recording high-density EMG signals from two human muscles, we show the feasibility of identifying firings of many MUs that comprise a MEP. We demonstrate the identification of firings of a large population of MUs in the broad recruitment range, up to maximal MEP amplitude, with fewer required stimuli compared to intramuscular EMG recordings. The methodology demonstrates an emerging possibility to study responses to TMS on a level of individual MUs in a non-invasive manner.

A peel-off convolution kernel compensation method for surface electromyography decomposition

Objective:Estimating discharge patterns of motor units by electromyography (EMG) decomposition has shown promising perspectives in neurophysiologic investigations and human–machine interfaces. However, the number of decoded motor units is still limited, especially under high excitation and noisy conditions, which is a major barrier to the broad application of EMG decomposition. In this work, we applied a peel-off decomposition strategy to the convolution kernel compensation (CKC) algorithm to extract more motor units non-invasively. Methods:EMG signals were firstly decomposed into motor unit spike trains (MUSTs) using the CKC. After each decomposition, the motor unit action potentials (MUAPs) were reconstructed and subtracted from the EMG signals. Then the same CKC decomposition was applied to the residual signals. This peel-off procedure was repeated until no valid motor units were identified. The proposed decomposition strategy was validated on synthetic EMG signals by convolving simulated MUSTs and experimentally extracted MUAPs under multiple excitations and noise conditions. Then the decomposition performance was evaluated on experimental data. Main results:Compared with the classic CKC method, the number of the identified motor units from synthetic signals was significantly increased by 7 to 43 in each decomposition. Moreover, the average agreement between identified MUST and ground truth was 0.80 ± 0.20, indicating a high decomposition accuracy. From experimental EMG signals, the peel-off method could identify more motor units (>20) with high confidence than the classic method (<5) across three excitation levels. Conclusion and Significance:These results demonstrate the efficiency of the proposed method in identifying more motor units from EMG signals, extending the potential applications of surface EMG decomposition for neural decoding.

High-density Surface EMG Decomposition by Combining Iterative Convolution Kernel Compensation with an Energy-specific peel-off Strategy.

Objective- This study aims to develop a novel framework for high-density surface electromyography (HD-sEMG) signal decomposition with superior decomposition yield and accuracy, especially for low-energy MUs. Methods- An iterative convolution kernel compensation-peel off (ICKC-P) framework is proposed, which consists of three steps: decomposition of the motor units (MUs) with relatively large energy by using the iterative convolution kernel compensation (ICKC) method and extraction of low-energy MUs with a Post-Processor and novel 'peel-off' strategy. Results- The performance of the proposed framework was evaluated by both simulated and experimental HD-sEMG signals. Our simulation results demonstrated that, with 120 simulated MUs, the proposed framework extracts more MUs compared to K-means convolutional kernel compensation (KmCKC) approach across six noise levels. And the proposed 'peel-off' strategy estimates more accurate MUAP waveforms at six noise levels than the 'peel-off' strategy proposed in the progressive FastICA peel-off (PFP) framework. For the experimental sEMG signals recorded from biceps brachii, an average of 16.1 ± 3.4 MUs were identified from each contraction, while only 10.0 ± 2.8 MUs were acquired by the KmCKC method. Conclusion- The high yield and accuracy of MUs decomposed from simulated and experimental HD-sEMG signals demonstrate the superiority of the proposed framework in decomposing low-energy MUs compared to existing methods for HD-sEMG signal decomposition. Significance- The proposed framework enables us to construct a more representative motor unit pool, consequently enhancing our understanding pertaining to various neuropathological conditions and providing invaluable information for the diagnosis and treatment of neuromuscular disorders and motor neuron diseases.

Identification of Motor Unit Firings in H-Reflex of Soleus Muscle Recorded by High-Density Surface Electromyography.

We developed and tested the methodology that supports the identification of individual motor unit (MU) firings from the Hoffman (or H) reflex recorded by surface high-density EMG (HD-EMG). Synthetic HD-EMG signals were constructed from simulated 10% to 90% of maximum voluntary contraction (MVC), followed by 100 simulated H-reflexes. In each H-reflex the MU firings were normally distributed with mean latency of 20 ms and standard deviations (SDLAT) ranging from 0.1 to 1.3 ms. Experimental H-reflexes were recorded from the soleus muscle of 12 men (33.6 ± 5.8 years) using HD-EMG array of 5×13 surface electrodes. Participants performed 15 to 20 s long voluntary plantarflexions with contraction levels ranging from 10% to 70% MVC. Afterwards, at least 60 H-reflexes were electrically elicited at three levels of background muscle activity: rest, 10% and 20% MVC. HD-EMGs of voluntary contractions were decomposed using the Convolution Kernel Compensation method to estimate the MU filters. When applied to HD-EMG signals with synthetic H reflexes, MU filters demonstrated high MU identification accuracy, especially for [Formula: see text] ms. When applied to experimental H-reflex recordings, the MU filters identified 14.1 ± 12.1, 18.2 ± 12.1 and 20.8 ± 8.7 firings per H-reflex, with individual MU firing latencies of 35.9 ± 3.3, 35.1 ± 3.0 and 34.6 ± 3.3 ms for rest, 10% and 20% MVC background muscle activity, respectively. Standard deviation of MU latencies across experimental H-reflexes were 1.0 ± 0.8, 1.3 ± 1.1 and 1.5 ± 1.2 ms, in agreement with intramuscular EMG studies.

Adaptive Real-Time Decomposition of Electromyogram During Sustained Muscle Activation: A Simulation Study.

Real-time decomposition of electromyogram (EMG) into constituent motor unit (MU) activity has shown promising applications in neurophysiology and human-machine interactions. Existing decomposition methods could not accommodate stochastic variations in EMG signals such as drifts of action potential amplitudes and MU recruitment-derecruitment (rotation) patterns during long-term recordings. The objective of this study was to develop an adaptive real-time decomposition approach suitable for prolonged muscle activation. We developed a parallel-double-thread computation algorithm. The backend thread initiated and periodically refined and updated the MU information (separation matrix) using independent component analysis and convolution kernel compensation. The frontend thread performed the real-time decomposition. We evaluated our algorithm on synthesized high-density EMG signals, in which MUs were recruited-derecruited sporadically and MU action potentials amplitude drifted over time. Different signal-to-noise levels were also simulated. Compared with the decomposition without the adaptive processes, periodically fine-tuned and updated separation matrix increased identifiable MU number by 3-4 fold over 30-minute of signals. The increased MU number was more prominent at higher signal-to-noise ratios. The decomposition accuracy also increased by up to 10% with greater improvement observed at higher muscle contraction levels. The adaptive algorithm can maintain the decomposition performance over time, allows us to continuously track the same MUs during sustained activation, and, at the same time, can add newly recruited MU information to existing separation matrix. Our approach showed robust performance over time, which has the potential to longitudinally evaluate MU firing and recruitment properties and improve neural decoding performance for neural-machine interactions.

Segment-Wise Decomposition of Surface Electromyography to Identify Discharges Across Motor Neuron Populations.

The surface electromyography (EMG) decomposition techniques have shown promising results in neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, current decomposition methods could only decode a limited number of motor units (MUs) because of the local convergence. The number of identified MUs remains similar even though more muscles or movements are involved, where multiple motor neuron populations are activated. The objective of this study was to develop a segment-wise decomposition strategy to increase the number of MU decoded from multiple motor neuron populations. The EMG signals were divided into several segments depending on the number of involved movements. The motor neurons, activated during each movement, were regarded as a population. The convolution kernel compensation (CKC) method was applied individually for each segment to decode the motor unit discharges from each motor neuron population. The MU filters were obtained in each segment and filtrated to estimate the MU spike trains (MUSTs) from the global EMG signals. The decomposition performance was validated on synthetic and experimental EMG signals. From synthetic EMG signals generated by two motor neuron populations, the proposed segment-wise CKC (swCKC) decoded significantly more MUs during low and medium excitation levels, with an increased rate of 16.3% to 75.4% compared with the conventional CKC. From experimental signals recorded during ten motor tasks, 133±24 MUs with the pulse-to-noise ratio of 36.6±6.5 dB were identified for each subject by swCKC, whereas the conventional CKC identified only 43±12 MUs. These results indicate the feasibility and superiority of the proposed swCKC to decode MU activities across motor neuron populations, extending the potential applications of EMG decomposition for neural decoding during multiple motor tasks.

Online Tracking of the Phase Difference Between Neural Drives to Antagonist Muscle Pairs in Essential Tremor Patients.

Transcutaneous electrical stimulation has been applied in tremor suppression applications. Out-of-phase stimulation strategies applied above or below motor threshold result in a significant attenuation of pathological tremor. For stimulation to be properly timed, the varying phase relationship between agonist-antagonist muscle activity during tremor needs to be accurately estimated in real-time. Here we propose an online tremor phase and frequency tracking technique for the customized control of electrical stimulation, based on a phase-locked loop (PLL) system applied to the estimated neural drive to muscles. Surface electromyography signals were recorded from the wrist extensor and flexor muscle groups of 13 essential tremor patients during postural tremor. The EMG signals were pre-processed and decomposed online and offline via the convolution kernel compensation algorithm to discriminate motor unit spike trains. The summation of motor unit spike trains detected for each muscle was bandpass filtered between 3 to 10 Hz to isolate the tremor related components of the neural drive to muscles. The estimated tremorogenic neural drive was used as input to a PLL that tracked the phase differences between the two muscle groups. The online estimated phase difference was compared with the phase calculated offline using a Hilbert Transform as a ground truth. The results showed a rate of agreement of 0.88 ± 0.22 between offline and online EMG decomposition. The PLL tracked the phase difference of tremor signals in real-time with an average correlation of 0.86 ± 0.16 with the ground truth (average error of 6.40° ± 3.49°). Finally, the online decomposition and phase estimation components were integrated with an electrical stimulator and applied in closed-loop on one patient, to representatively demonstrate the working principle of the full tremor suppression system. The results of this study support the feasibility of real-time estimation of the phase of tremorogenic neural drive to muscles, providing a methodology for future tremor-suppression neuroprostheses.

Association between the Degree of Pre-Synaptic Dopaminergic Pathway Degeneration and Motor Unit Firing Behavior in Parkinson's Disease Patients.

The relationship between motor unit (MU) firing behavior and the severity of neurodegeneration in Parkinson’s disease (PD) is not clear. This study aimed to elucidate the association between degeneration with dopaminergic pathways and MU firing behavior in people with PD. Fourteen females with PD (age, 72.6 ± 7.2 years, disease duration, 3.5 ± 2.1 years) were enrolled in this study. All participants performed a submaximal, isometric knee extension ramp-up contraction from 0% to 80% of their maximal voluntary contraction strength. We used high-density surface electromyography with 64 electrodes to record the muscle activity of the vastus lateralis muscle and decomposed the signals with the convolution kernel compensation technique to extract the signals of individual MUs. We calculated the degree of degeneration of the central lesion-specific binding ratio by dopamine transporter single-photon emission computed tomography. The primary, novel results were as follows: (1) moderate-to-strong correlations were observed between the degree of degeneration of the central lesion and MU firing behavior; (2) a moderate correlation was observed between clinical measures of disease severity and MU firing behavior; and (3) the methods of predicting central nervous system degeneration from MU firing behavior abnormalities had a high detection accuracy with an area under the curve >0.83. These findings suggest that abnormalities in MU activity can be used to predict central nervous system degeneration following PD.

A convolutional neural network to identify motor units from high-density surface electromyography signals in real time

Objectives. This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spike trains and estimate the neural drive to muscles from high-density electromyography (HD-EMG) signals in real time. Two distinct deep CNNs are compared with the convolution kernel compensation (CKC) algorithm using simulated and experimentally recorded signals. The effects of window size and step size of the input HD-EMG signals are also investigated. Approach. The MU spike trains were first identified with the CKC algorithm. The HD-EMG signals and spike trains were used to train the deep CNN. Then, the deep CNN decomposed the HD-EMG signals into MU discharge times in real time. Two CNN approaches are compared with the CKC: (a) multiple single-output deep CNN (SO-DCNN) with one MU decomposed per network, and (b) one multiple-output deep CNN (MO-DCNN) to decompose all MUs (up to 23) with one network. Main results. The MO-DCNN outperformed the SO-DCNN in terms of training time (3.2–21.4 s epoch−1 vs 6.5–47.8 s epoch−1, respectively) and prediction time (0.04 vs 0.27 s sample−1, respectively). The optimal window size and step size for MO-DCNN were 120 and 20 data points, respectively. It results in sensitivity of 98% and 85% with simulated and experimentally recorded HD-EMG signals, respectively. There is a high cross-correlation coefficient between the neural drive estimated with CKC and that estimated with MO-DCNN (range of r-value across conditions: 0.88–0.95). Significance. We demonstrate the feasibility and the validity of using deep CNN to accurately identify MU activity from HD-EMG with a latency lower than 80 ms, which falls within the lower bound of the human electromechanical delay. This method opens many opportunities for using the neural drive to interface humans with assistive devices.

Quercetin ingestion modifies human motor unit firing patterns and muscle contractile properties

Quercetin is a polyphenolic flavonoid that has reported to block the binding of adenosine to A1 receptors at central nervous system and increase calcium release from the sarcoplasmic reticulum at skeletal muscle. The aim of the present study was to investigate the acute effect of quercetin ingestion on motor unit activation and muscle contractile properties. High-density surface electromyography during submaximal contractions and electrically elicited contraction torque in knee extensor muscles were measured before (PRE) and 60 min after (POST) quercetin glycosides or placebo ingestions in 13 young males. Individual motor units of the vastus lateralis muscle were identified from high-density surface electromyography by the Convolution Kernel Compensation technique. Firing rates of motor units recruited at 30–50% of the maximal voluntary contraction torque (MVC) were increased from PRE to POST only with quercetin (9.0 ± 2.3 to 10.5 ± 2.0 pps, p = 0.034). Twitch torque during doublet stimulation was decreased from PRE to POST with placebo (77.1 ± 17.1 to 73.9 ± 17.6 Nm, p = 0.005), but not with quercetin (p > 0.05). For motor units recruited at < 10% of MVC, normalized firing rate were decreased with quercetin (1.52 ± 0.33 to 1.58 ± 0.35%MVC/pps, p = 0.002) but increased with placebo (1.61 ± 0.32 to 1.57 ± 0.31%MVC/pps, p = 0.005). These results suggest that ingested quercetin has the functional roles to: mitigate reduction in the muscle contractile properties, enhance activations of relatively higher recruitment threshold motor units, and inhibit activation of relatively lower recruitment threshold motor units.

Deep Learning for Robust Decomposition of High-Density Surface EMG Signals.

Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trains. Once the separation matrix is blindly estimated on a signal interval, it is also possible to apply the same matrix to subsequent signal segments. Nonetheless, the trained separation matrices are sub-optimal in noisy conditions and require that incoming data undergo computationally expensive whitening. One unexplored alternative is to instead use the paired HD-sEMG signal and BSS output to train a model to predict MU activations within a supervised learning framework. A gated recurrent unit (GRU) network was trained to decompose both simulated and experimental unwhitened HD-sEMG signal using the output of the gCKC algorithm. The results on the experimental data were validated by comparison with the decomposition of concurrently recorded intramuscular EMG signals. The GRU network outperformed gCKC at low signal-to-noise ratios, proving superior performance in generalising to new data. Using 12 seconds of experimental data per recording, the GRU performed similarly to gCKC, at rates of agreement of 92.5% (84.5%-97.5%) and 94.9% (88.8%-100.0%) respectively for GRU and gCKC against matched intramuscular sources.

Identification of the laterality of motor unit behavior in female patients with parkinson's disease using high-density surface electromyography.

Patients with Parkinson's disease (PD) have greater laterality of muscle contraction properties than other people with parkinsonism diseases. However, few studies have reported the laterality of MU activation properties of the lower extremity muscles in patients with PD. The aim of the present study was to identify the laterality of MU behavior in PD patients using high-density surface electromyography (HD-SEMG). Eleven female patients with PD (age, 69.2±6.2years, disease duration, 2.7±0.9years, Unified Parkinson's disease Rating Scale score, 13 (9-16)), and 9 control female subjects (age, 66.8±3.5years) were enrolled in the present study. All subjects performed a sustained isometric knee extension in a 30% maximal voluntary contraction (MVC) task for 20s. HD-SEMG signals were used to record and extract single MU firing behavior in the vastus lateralis muscle during submaximal isometric knee extensor contractions with 64 electrodes and decomposed with the convolution kernel compensation technique to extract individuals MUs. Compared to the control subjects, the patients with PD exhibited laterality of the MU firing rate and an absence of a relationship between the mean MU firing rate and MU threshold. Patients with PD exhibit laterality of MU behavior and experience MU behavioral abnormalities even with mild symptoms such as Hoehn & Yahr stage≤3 and disease duration=2.7±0.9. These findings suggest the importance of considering the detection of abnormal muscle properties in PD patients beginning in the early phase of the disease.

On the Prediction of Motor Unit Filter Changes in Blind Source Separation of High-Density Surface Electromyograms During Dynamic Muscle Contractions

We study the changes of Motor Unit (MU) filters in MU identification from high-density surface electromyograms recorded during isokinetic dynamic contractions of biceps brachii muscle. We demonstrate that these changes can be predicted for limited changes of the joint angle by linearly extrapolating previously recorded changes, allowing for the linear prediction-correction paradigm of MU filter updating. We then demonstrate the efficiency of this paradigm by implementing MU filter updating by the Kalman filter and integrating it into the previously published Convolution Kernel Compensation (CKC) MU identification method. When compared to the original CKC method and the previously published cyclostationary CKC method devoted to MU identification in repeated dynamic contractions, the Kalman based MU filter prediction yielded a superior precision of MU firing tracking in dynamic contractions. In the case of relatively fast biceps brachii contractions with full elbow flexion and extension in 2s, the Kalman based MU filter prediction tracked 21.3 ± 1.8 MUs with an average sensitivity of 95.6 ± 7.0% and precision of 96.5 ± 3.5%. In the same conditions, the original CKC method identified 7.1 ± 2.0 MUs with an average sensitivity of 62.7 ± 20.1% and precision of 98.1 ± 3.9%, whereas cyclostationary CKC tracked 18.9 ± 2.0 MUs with an average sensitivity of 91.8 ± 12.2% and precision of 94.7 ± 5.1%.

On the Reuse of Motor Unit Filters in High Density Surface Electromyograms Recorded at Different Contraction Levels

We analyzed the efficiency of Motor Unit (MU) tracking across different experimentally recorded contraction levels of Biceps Brachii (BB), Tibialis Anterior (TA), First Dorsal Interosseous (FDI) and Abductor Digiti Minimi (ADM) muscles and in simulated conditions. We used the Convolution Kernel Compensation (CKC) algorithm to estimate the MU filters from high-density electromyograms (HDEMGs) at contraction levels ranging from 5 % to 70 % of Maximum Voluntary Contraction (MVC), and applied these MU filters to all the recorded contractions levels of the same muscle. For each MU filter estimation-application pair we assessed the number of identified MUs, Pulse-to-Noise Ratio (PNR), Mean Discharge Rate (MDR) and MUAP peak-to-peak (P2P) amplitude, normalized by HDEMG P2P amplitude. In simulated conditions, we also calculated the Precision ( $Pr$ ) and Sensitivity ( $Se$ ) of MU firing identification. We also reported the number of jointly identified MUs for all possible contraction level pairs. The results in simulated conditions confirmed the efficiency of MU filter transfer from low to high contraction levels. The large majority of MUs identified at lower contraction levels were tracked successfully at higher contraction levels, though many of them were not directly identified at higher contraction levels. Sensitivities of identified MU firings were above 97 %, decreasing only by about 1 % when transferring the MU filters from low to high contraction levels. In the experimental conditions the efficiency of the MU tracking depended significantly on the investigated muscle. The highest efficiency was demonstrated in the FDI muscle, where about 90 % and 70 % of MUs identified at the contraction level of MU filter estimation were also tracked at 10 % and 20 % higher contraction level, respectively. These figures decreased to 47 % and 20 % of MUs in TA, and to 21 % and 18 % of MUs in the BB muscle. The observed differences between the MU filter transfer efficiencies are likely caused by the differences in the size and anatomy of the investigated muscles.

Estimation of Muscle Co-Activations in Wrist Rehabilitation After Stroke is Sensitive to Motor Unit Distribution and Action Potential Shapes.

We evaluated different muscle excitation estimation techniques, and their sensitivity to Motor Unit (MU) distribution in muscle tissue. For this purpose, the Convolution Kernel Compensation (CKC) method was used to identify the MU spike trains from High-Density ElectroMyoGrams (HDEMG). Afterwards, Cumulative MU Spike Train (CST) was calculated by summing up the identified MU spike trains. Muscle excitation estimation from CST was compared to the recently introduced Cumulative Motor Unit Activity Index (CAI) and classically used Root-Mean-Square (RMS) amplitude envelop of EMG. To emphasize their dependence on the MU distribution further, all three muscle excitation estimates were used to calculate the agonist-antagonist co-activation index. We showed on synthetic HDEMG that RMS envelopes are the most sensitive to MU distribution (10 % dispersion around the real value), followed by the CST (7 % dispersion) and CAI (5 % dispersion). In experimental HDEMG from wrist extensors and flexors of post-stroke subjects, RMS envelopes yielded significantly smaller excitations of antagonistic muscles than CST and CAI. As a result, RMS-based co-activation estimates differed significantly from the ones produced by CST and CAI, illuminating the problem of large diversity of muscle excitation estimates when multiple muscles are studied in pathological conditions. Similar results were also observed in experimental HDEMG of six intact young males.

Single channel high noise level ECG deconvolution using optimized blind adaptive filtering and fixed-point convolution kernel compensation

An electrocardiogram (ECG) is used to record the electrical activity of the heart. However, ECG signals are susceptible to the noise from various sources which increases the probability of misinterpretation and can affect the diagnostic process. Traditional noise cancellation techniques, which uses finite and deterministic coefficient, are not efficient, since the ECG signals are non-stationary. Thus, adaptive filters are commonly utilized on such signal as they can adjust their coefficient according to the changing nature of non-stationary signal. Adaptive algorithms still have a disadvantage that they require the model of noise or desired signal. In this paper, a novel algorithm is introduced based on fixed-point convolution kernel compensation for finding a model for using an adaptive filter; then a recursive least square method is used for completing steps of deconvolution of the ECG signal. The deconvolution method can be used for denoising ECG signals in very low signal to noise ratio circumstances and also can be used in blind source separation applications such as separation of fetal ECG from maternal ECG. ECG signals were utilized in this study are taken from the MIT-BIH Arrhythmia database for showing the performance of the algorithm on denoising applications. The results demonstrate that the proposed algorithm renders a much-improved performance in removing the noise from ECG signals, especially in a scenario where signal to noise ratio is negative. Moreover, the noninvasive fetal ECG dataset (NI-FECG) provided by Physionet is also used for fetal ECG extraction by a single thoracic channel. By comparing fetal ECG extraction methods in the literature and the proposed method, it reveals that the proposed method can extract the QRS complex of fetal ECG by a single thoracic channel as accurate as other methods which use abdominal channels.