Research on AR-AKF Model Denoising of the EMG Signal.

Electromyography (EMG) signals can be used for clinical diagnosis and biomedical applications. It is very important to reduce noise and to acquire accurate signals for the usage of the EMG signals in biomedical engineering. Since EMG signal noise has the time-varying and random characteristics, the present study proposes an adaptive Kalman filter (AKF) denoising method based on an autoregressive (AR) model. The AR model is built by applying the EMG signal, and the relevant parameters are integrated to find the state space model required to optimally estimate AKF, eliminate the noise in the EMG signal, and restore the damaged EMG signal. To be specific, AR autoregressive dynamic modeling and repair for distorted signals are affected by noise, and AKF adaptively can filter time-varying noise. The denoising method based on the self-learning mechanism of AKF exhibits certain capabilities to achieve signal tracking and adaptive filtering. It is capable of adaptively regulating the model parameters in the absence of any prior statistical knowledge regarding the signal and noise, which is aimed at achieving a stable denoising effect. By comparatively analyzing the denoising effects exerted by different methods, the EMG signal denoising method based on the AR-AKF model is demonstrated to exhibit obvious advantages.

tDCS application for postural control in Parkinson's disease: Effects are associated with baseline characteristics.

INTRODUCTION: Transcranial direct current stimulation (tDCS) improves postural response to perturbation in patients with Parkinson's disease (PwPD). However, the influence of baseline characteristics such as clinical/cognitive and postural performance on the response to tDCS remains unclear. OBJECTIVE: To investigate whether baseline level of postural control (performance during sham condition) and clinical/cognitive characteristics are associated with tDCS-related changes in postural responses to external perturbations in PwPD. METHODS: Twenty-four PwPD participated in this study. Clinical assessment included disease severity, disease duration, levodopa equivalent dose and global cognition. Anodal tDCS protocols targeting the primary motor cortex were applied in two separate sessions (at least 2 weeks apart): active (2 mA for 20 min) and sham stimulation. Seven trials with the backward translation of the support base (20 cm/s and 5 cm) were performed after tDCS. Postural outcomes included the recovery time to stable position and onset latency of the medial gastrocnemius (MG). Pearson and Spearman correlation tests were performed. RESULTS: No significant correlations were observed between clinical/cognitive characteristics and tDCS-related changes in postural responses. Negative associations were observed between the baseline level of postural control and tDCS-related changes in postural responses for the recovery time (r = -0.657; p < 0.001) and the MG onset latency (rs = -0.539; p = 0.007). PwPD with worse baseline postural control demonstrated greater improvement after active stimulation. CONCLUSIONS: Findings suggest that tDCS-related effects on postural response to perturbation are related to the baseline level of postural control, but not to clinical characteristics in PwPD. Those with worse baseline postural control responded better to tDCS.

Evolution of inspiratory muscle function in children during mechanical ventilation.

BACKGROUND: There is no universally accepted method to assess the pressure-generating capacity of inspiratory muscles in children on mechanical ventilation (MV), and no study describing its evolution over time in this population. METHODS: In this prospective observational study, we have assessed the function of the inspiratory muscles in children on various modes of MV. During brief airway occlusion maneuvers, we simultaneously recorded airway pressure depression at the endotracheal tube (ΔPaw, force generation) and electrical activity of the diaphragm (EAdi, central respiratory drive) over five consecutive inspiratory efforts. The neuro-mechanical efficiency ratio (NME, ΔPaw/EAdimax) was also computed. The evolution over time of these indices in a group of children in the pediatric intensive care unit (PICU) was primarily described. As a secondary objective, we compared these values to those measured in a group of children in the operating room (OR). RESULTS: In the PICU group, although median NMEoccl decreased over time during MV (regression coefficient - 0.016, p = 0.03), maximum ΔPawmax remained unchanged (regression coefficient 0.109, p = 0.50). Median NMEoccl at the first measurement in the PICU group (after 21 h of MV) was significantly lower than at the only measurement in the OR group (1.8 cmH2O/µV, Q1-Q3 1.3-2.4 vs. 3.7 cmH2O/µV, Q1-Q3 3.5-4.2; p = 0.015). Maximum ΔPawmax in the PICU group was, however, not significantly different from the OR group (35.1 cmH2O, Q1-Q3 21-58 vs. 31.3 cmH2O, Q1-Q3 28.5-35.5; p = 0.982). CONCLUSIONS: The function of inspiratory muscles can be monitored at the bedside of children on MV using brief airway occlusions. Inspiratory muscle efficiency was significantly lower in critically ill children than in children undergoing elective surgery, and it decreased over time during MV in critically ill children. This suggests that both critical illness and MV may have an impact on inspiratory muscle efficiency.

Abnormal Laryngeal Electromyography Findings in Asymptomatic Adults Across the Age Spectrum.

OBJECTIVES/HYPOTHESIS: Laryngeal electromyography (LEMG) is a diagnostic tool for patients with suspected neurogenic abnormalities of the larynx. LEMG is often used with the assumption that any abnormality is symptom-/disease-related. We sought to determine the prevalence of abnormal LEMG findings in a group of healthy asymptomatic adults across a large age spectrum. STUDY DESIGN: Open, prospective study, gender-match and age balanced by decade. METHODS: Forty-six healthy participants (age 20-78) underwent LEMG, including 178 muscles. Participants had no history of voice problems, normal VHI-10, and normal flexible laryngoscopy. Qualitative and quantitative LEMG (bilateral) were performed involving the thyroarytenoid-lateral cricoarytenoid muscle complex (TA-LCA) and cricothyroid (CT) muscles. LEMG parameters included evaluation for fibrillation potentials, sharp waves, reduced recruitment, polyphasic potentials, electrical synkinesis, and measurement of turns per second. RESULTS: Of participants, 4% had at least one abnormal qualitative finding (slightly reduced recruitment or two to three discrete polyphasic potentials). There were no findings of fibrillation potentials or sharp waves. There were no abnormal qualitative findings in the CT muscles tested. Of participants, 16% had at least one abnormal synkinesis finding. LEMG qualitative abnormalities and quantitative abnormalities do not appear to correlate with gender or age. CONCLUSION: Abnormal qualitative and quantitative LEMG findings were uncommon and minor in severity in our group of asymptomatic healthy adults. The likelihood of abnormal LEMG results in asymptomatic adults was 2.2% for qualitative findings, 9.3% for synkinesis, and 5.4% for turns/s. LEVEL OF EVIDENCE: 3 Laryngoscope, 131:2065-2069, 2021.

What is the impact of continuous neuromonitoring on the incidence of injury to the recurrent laryngeal nerve during total thyroidectomy?

BACKGROUND: Continuous intraoperative neuromonitoring may facilitate reversal of intraoperative injurious operative maneuvers in comparison with intermittent intraoperative neuromonitoring. The aim of this study was to evaluate the impact of the routine use of continuous intraoperative neuromonitoring on intraoperative injuries to the recurrent laryngeal nerve. METHOD: This study was a prospective case series with retrospective analysis of consecutive patients undergoing total thyroidectomy from August 2013 to August 2019. During this period, intermittent intraoperative neuromonitoring (before Mar 2016) and continuous intraoperative neuromonitoring (after Mar 2016) were used in all patients. RESULTS: We reviewed the outcomes of 603 patients (466 female patients) comprising 236 who underwent intermittent intraoperative neuromonitoring and 367 who underwent continuous intraoperative neuromonitoring. Intraoperative adverse electromyography events (>50% decrease in amplitude between VN1 and VN2) were observed in 87 patients (14.5%) and were less frequent in the continuous intraoperative neuromonitoring group (10.6 vs 20.3%, P = .001). Intraoperative loss of signal (electromyography events with VN2 ≤100µV) were observed in 35 patients (5.8%) without any difference between the 2 groups of patients (5.2 vs 6.8%, P = .415). Postoperative recurrent laryngeal nerve palsies were observed in 36 patients (5.9%) without any difference between the 2 groups of patients (4.9 vs 7.6%, P = .168). CONCLUSION: The routine use of continuous intraoperative neuromonitoring improves the rate of intraoperative adverse electromyography events but does not impact significantly the rates of loss of signal and recurrent laryngeal nerve palsy.

An integrated EMD adaptive threshold denoising method for reduction of noise in ECG.

Electrocardiogram (ECG) denoising is a biomedical research area of great importance. In this paper, an integrated empirical mode decomposition adaptive threshold denoising method (IEMD-ATD) is proposed for processing ECGs. Three methods are included in the IEMD-ATD. First, an integrated EMD method based on a framework of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed to improve the decomposition quality and stability of raw ECGs. Second, a new grouping method for intrinsic mode functions (IMFs) is developed based on the energy and eigenperiod of IMFs. The grouping method is able to determine the boundaries among high-frequency noise predominant IMFs, useful information predominant IMFs and IMFs with low-frequency noises. Finally, an adaptive threshold denoising method is derived and used for denoising high-frequency noise predominant IMFs. There are two main contributions: 1) an adaptive threshold determination method based on the 3σ criterion and 2) a peak filtering denoising method for retaining useful information contained in the values smaller than the threshold. Synthetic and real ECG data in the MIT-BIH database are utilised in experiments to illustrate the effectiveness of IEMD-ATD for ECG denoising. The results indicate that IEMD-ATD offers better performance in improving the signal-to-noise ratio (SNR) and correlation coefficient compared with the existing EMD denoising methods. Our method offers obvious advantages, especially in retaining detailed information on the QRS complex of the ECG, which is significant for the feature extraction of ECG signals and for pathological diagnosis.

Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction.

Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems.

Quantitative Evaluation of Interference Patterns on Electromyography in Neuropathy.

OBJECTIVE: The aim of this study was to develop a quantitative evaluation method of interference patterns on needle electromyography that is easy to apply in clinical use and to examine its usefulness. Diagnostic electrophysiological assessments are important for physiatrists, and correct diagnosis and assessment are essential for proper rehabilitation. DESIGN: A total of 112 maximum interference patterns of upper extremity muscles suspected of being affected by neuropathy were quantitatively evaluated based on the parameters of integration values, mean amplitudes, the number of peaks, and activity. "Activity" was defined as the sum of the time during which myoelectric signals were recorded during 1 sec with maximum voluntary contraction, and it was expressed as a percentage. The relationships of the previous parameters with spontaneous pathological potentials and polyphasic motor unit potentials were examined. RESULTS: The area under the curve of the receiver operating characteristic curve for the diagnosis of neuropathy was the highest using activity (0.917). The integral value and mean amplitude were useful for the diagnosis of cases with chronic neuropathy showing slightly decreased interference patterns. CONCLUSIONS: The quantitative evaluation of the maximal contraction interference pattern in this study was useful for the diagnosis of neuropathy.

Methodology Proposal of EMG Hand Movement Classification Based on Cross Recurrence Plots.

Dealing with electromyography (EMG) signals is often not simple. The nature of these signals is nonstationary, noisy, and high dimensional. These EMG characteristics make their predictability even more challenging. Cross recurrence plots (CRPs) have demonstrated in many works their capability of detecting very subtle patterns in signals often buried in a noisy environment. In this contribution, fifty subjects performed ten different hand movements with each hand with the aid of electrodes placed in each arm. Furthermore, the nonlinear features of each subject's signals using cross recurrence quantification analysis (CRQA) have been performed. Also, a novel methodology is proposed using CRQA as the mainstream technique to detect and classify each of the movements presented in this study. Additional tools were presented to determine to which extent this proposed methodology is able to avoid false classifications, thus demonstrating that this methodology is feasible to classify surface EMG (SEMG) signals with good accuracy, sensitivity, and specificity. Lastly, the results were compared with traditional machine learning methods, and the advantages of using the proposed methodology above such methods are highlighted.

Optimal Estimation of EMG Standard Deviation (EMG σ ) in Additive Measurement Noise: Model-Based Derivations and Their Implications.

Typical electromyogram (EMG) processors estimate EMG signal standard deviation (EMG σ ) via moving average root mean square (RMS) or mean absolute value (MAV) filters, whose outputs are used in force estimation, prosthesis/orthosis control, etc. In the inevitable presence of additive measurement noise, some processors subtract the noise standard deviation from EMG RMS (or MAV). Others compute a root difference of squares (RDS)-subtract the noise variance from the square of EMG RMS (or MAV), all followed by taking the square root. Herein, we model EMG as an amplitude-modulated random process in additive measurement noise. Assuming a Gaussian (or, separately, Laplacian) distribution, we derive analytically that the maximum likelihood estimate of EMG σ requires RDS processing. Whenever that subtraction would provide a negative-valued result, we show that EMG σ should be set to zero. Our theoretical models further show that during rest, approximately 50% of EMG σ estimates are non-zero. This result is problematic when EMG σ is used for real-time control, explaining the common use of additional thresholding. We tested our model results experimentally using biceps and triceps EMG from 64 subjects. Experimental results closely followed the Gaussian model. We conclude that EMG processors should use RDS processing and not noise standard deviation subtraction.

Patient characteristics and hospitalisation costs of spinal muscular atrophy in Spain: a retrospective multicentre database analysis.

OBJECTIVES: To analyse the characteristics of patients diagnosed with spinal muscular atrophy in Spain, and to revise data on disease management and use of resources in both public and private healthcare centres. DESIGN: A retrospective multicentre database analysis. SETTING: 870 admission records registered between 1997 and 2015 with a diagnosis of spinal muscular atrophy were extracted from a Spanish claims database that includes hospital inpatient and outpatient admissions from 313 public and 192 private hospitals in Spain. RESULTS: Admission files corresponded to 705 patients; 61.99% were males and 38.01% females. Average patient age was 37 years. Disease comorbidities registered during the admission consistently included hypertension, scoliosis and respiratory failures, all associated with the standard disease course. Regarding disease management at the hospital level, patients were mostly admitted through scheduled appointments (58.16%), followed by emergency admissions (41.72%), and into neurology services in 17% of the cases. Mean hospitalisation time was 10.45 days and in-hospital mortality reached 5.29%. The overall direct medical costs of spinal muscular atrophy were €291 525, excluding medication. The average annual cost per admission was €6274, with large variations likely to reflect disease complexity and that increases with length of stay. CONCLUSIONS: The rarity of the disease difficulties the study of demographics and management; yet, an analysis of patient characteristics provides necessary information that can be used by governments to establish more efficient healthcare protocols. This study reflects the impact that individual needs and disease severity can have in disease burden calculations. Forthcoming decision-making policies should take into account medical costs and its variability, as well as pharmaceutical expenses and indirect costs. To our knowledge, this is the first study evaluating the use of healthcare resources of patients with spinal muscular atrophy in Spain.

Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury.

The appropriate selection of individual-specific spinal cord epidural stimulation (scES) parameters is crucial to re-enable independent standing with self-assistance for balance in individuals with chronic, motor complete spinal cord injury, which is a key achievement toward the recovery of functional mobility. To date, there are no available algorithms that contribute to the selection of scES parameters for facilitating standing in this population. Here, we introduce a novel framework for EMG data processing that implements spectral analysis by continuous wavelet transform and machine learning methods for characterizing epidural stimulation-promoted EMG activity resulting in independent standing. Analysis of standing data collected from eleven motor complete research participants revealed that independent standing was promoted by EMG activity characterized by lower median frequency, lower variability of median frequency, lower variability of activation pattern, lower variability of instantaneous maximum power, and higher total power. Additionally, the high classification accuracy of assisted and independent standing allowed the development of a prediction algorithm that can provide feedback on the effectiveness of muscle-specific activation for standing promoted by the tested scES parameters. This framework can support researchers and clinicians during the process of selection of epidural stimulation parameters for standing motor rehabilitation.

Effect of handrail height and age on the timing and speed of reach-to-grasp balance reactions during slope descent.

We investigated the effect of handrail height on the timing and speed of reach-to-grasp balance reactions during slope descent, in fourteen younger and thirteen older adults. Participants walked along an 8° slope mounted to a robotic platform. Platform perturbations evoked reach-to-grasp reactions. Handrail height did not significantly affect handrail contact time (i.e., time from perturbation onset to handrail contact) or movement time (i.e., time from EMG latency to handrail contact). Participants appeared to compensate for the increased hand-handrail distance with higher rails via increased peak upward hand speed, and decreased vertical handrail overshoot. Aging was associated with slower EMG latency, reduced hand acceleration time, and increased hand deceleration time. Our findings suggest that participants were not disadvantaged by higher handrails from reach-to-grasp timing or speed perspectives, and that other metrics (e.g., center-of-mass control after grasping) may be more important when evaluating handrail designs for balance recovery.

Detection of pronator muscle overactivity in children with unilateral spastic cerebral palsy: Development of a semi-automatic method using EMG data.

BACKGROUND: The pronator teres and pronator quadratus muscles are frequently injected with neuromuscular blocking agents to improve supination in children with spastic cerebral palsy and limited active elbow supination. However, determining by simple clinical examination whether these muscles are overactive during active movement is difficult. OBJECTIVE: This study aimed to develop a semi-automatic method to detect pronator muscle overactivity by using surface electromyography (EMG) during active supination movements in children with cerebral palsy. METHODS: In total, 25 children with unilateral spastic cerebral palsy (10 males; mean [SD] age 10.6 [3.0] years) and 12 typically developing children (7 males; mean age 11.0 [3.0] years) performed pronation-supination movements at 0.50Hz. Kinematic parameters and surface EMG signals were recorded for both pronator muscles. Three experts visually assessed muscle overactivity in the EMG signals of the children with cerebral palsy, in comparison with the reference group. The reliability and discrimination ability of the visual assessments were analysed. Overactivity detection thresholds for the semi-automatic method were adjusted by using the visual assessment by the EMG experts. The positive and negative predictive values of the semi-automatic detection method were calculated. RESULTS: Intra-rater reliability of visual assessment by EMG experts was excellent and inter-rater reliability was moderate. For the 25 children with unilateral spastic cerebral palsy, EMG experts could discriminate different profiles of pronator overactivity during active supination: no pronator overactivity, one overactive pronator, or overactivity of both pronators. The positive and negative predictive values were 96% and 91%, respectively, for this semi-automatic detection method. CONCLUSIONS: Detection of pronator overactivity by using surface EMG provides an important complement to the clinical examination. This method can be used clinically, with the condition that clinicians be aware of surface EMG limitations. We believe use of this method can increase the accuracy of treatment for muscle overactivity, resulting in improved motor function and no worsening of paresis.

Anatomically accurate model of EMG during index finger flexion and abduction derived from diffusion tensor imaging.

This study presents a modelling framework in which information on muscle fiber direction and orientation during contraction is derived from diffusion tensor imaging (DTI) and incorporated in a computational model of the surface electromyographic (EMG) signal. The proposed model makes use of the principle of reciprocity to simultaneously calculate the electric potentials produced at the recording electrode by charges distributed along an arbitrary number of muscle fibers within the muscle, allowing for a computationally efficient evaluation of extracellular motor unit action potentials. The approach is applied to the complex architecture of the first dorsal interosseous (FDI) muscle of the hand to simulate EMG during index finger flexion and abduction. Using diffusion tensor imaging methods, the results show how muscle fiber orientation and curvature in this intrinsic hand muscle change during flexion and abduction. Incorporation of anatomically accurate muscle architecture and other hand tissue morphologies enables the model to capture variations in extracellular action potential waveform shape across the motor unit population and to predict experimentally observed differences in EMG signal features when switching from index finger abduction to flexion. The simulation results illustrate how structural and electrical properties of the tissues comprising the volume conductor, in combination with fiber direction and curvature, shape the detected action potentials. Using the model, the relative contribution of motor units of different sizes located throughout the muscle under both conditions is examined, yielding a prediction of the detection profile of the surface EMG electrode array over the muscle cross-section.

SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis.

OBJECTIVES: Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated analytical tool. METHODS: Six ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model's predictive ability. RESULTS: By applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/- 0.2 SEM), specificity of 91.6% (+/- 0.1 SEM) and classification accuracy of 87.9% (+/- 0.1 SEM). An amplitude exclusion threshold (100 µV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p = 0.394). CONCLUSION: We have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE). SIGNIFICANCE: Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.

sEMG-angle estimation using feature engineering techniques for least square support vector machine.

In the practical implementation of control of electromyography (sEMG) driven devices, algorithms should recognize the human's motion from sEMG with fast speed and high accuracy. This study proposes two feature engineering (FE) techniques, namely, feature-vector resampling and time-lag techniques, to improve the accuracy and speed of least square support vector machine (LSSVM) for wrist palmar angle estimation from sEMG feature. The root mean square error and correlation coefficients of LSSVM with FE are 9.50 ± 2.32 degree and 0.971 ± 0.018 respectively. The average training time and average execution time of LSSVM with FE in processing 12600 sEMG points are 0.016 s and 0.053 s respectively. To evaluate the proposed algorithm, its estimation results are compared with those of three other methods, namely, LSSVM, radial basis function (RBF) neural network, and RBF with FE. Experimental results verify that introduction of time-lag into feature vector can greatly improve the estimation accuracy of both RBF and LSSVM; meanwhile the application of feature-vector resampling technique can significantly increase the training and execution speed of RBF neural network and LSSVM. Among different algorithms applied in this study, LSSVM with FE techniques performed best in terms of training and execution speed, as well as estimation accuracy.

Reliability and validity of pelvic floor muscle displacement measurements during voluntary contractions.

INTRODUCTION AND HYPOTHESIS: Understanding the functioning of pelvic floor muscles (PFM) is crucial in female PFM rehabilitation. The aim of this study was to determine the intra-session retest reliability and validity to evaluate the quantity of PFM displacement. METHODS: This cross-sectional observational study examined the PFM displacement of 17 young healthy nulliparous women in the midsagittal plane. Three maximal voluntary contractions (MVCs) and five fast voluntary contractions (FVCs) were simultaneously examined with an electromagnetic tracking system (ETS) and transabdominal ultrasound (TAUS) and expressed in millimeters (mean, SD). To evaluate reliability and validity, the analysis of variance, intraclass coefficient (2,1), standard error of measurement (SEM), and minimal detectable difference (MDD) were calculated. RESULTS: Maximal voluntary contractions and FVCs in supine position measured by an ETS (TAUS) showed a displacement of MVC: 3.5 ± 1.9 mm (7.8 ± 4.5 mm), FVC: 3.5 ± 2.4 mm (7.6 ± 5.3 mm), and during standing of MVC: 5.2 ± 1.6 mm (9.4 ± 3.8 mm) and FVC: 4.8 ± 2.5 mm (9.7 ± 4.1 mm). Intraclass correlation for the ETS (TAUS) measurement varied between 0.79 and 0.89 (0.61 and 0.74), SEM 0.52 and 1.03 mm (1.54 and 3.2 mm), and MDD 1.54 and 3.2 mm (6.64 and 7.53 mm). The correlation between an ETS and TAUS varied between 0.53 and 0.67. CONCLUSIONS: For MVC and FVC, ETS measurements are highly reliable and TAUS measurements are moderately reliable for both contraction types. The correlation between the TAUS and ETS measurements is moderate. An ETS seems to be a reliable and valid measurement tool for evaluating PFM displacement during voluntary contractions. In future studies, the reproducibility and validity of ETS measurements need to be investigated in impact activities.

Interference Removal From Electromyography Based on Independent Component Analysis.

High-density surface electromyography (HD-EMG) provides detailed information about muscle activation. However, HD-EMG recordings can be interfered by motion artifacts and power line noise. In this paper, an interference detection and removal method with minimal distortion of the EMG was developed based on the independent component analysis (ICA). After the source separation, the independent components with power line noise were detected based on the spectra and were processed with notch filters. Components with motion artifacts were identified by analyzing the peak frequency of the spectrum, and motion artifacts were filtered with a high-pass filter and an amplitude thresholding method. The EMG signals were then reconstructed based on the processed source signals. The denoising performance was evaluated on both simulated and experimental EMG signals. The results showed that our method was significantly better than the digital filter method and the conventional ICA-based method where components with interferences were set to zero. Namely, our method showed a minimal distortion of the denoised EMG amplitude and frequency and a higher yield of decomposed motor units. Our interference detection and removal algorithm can be used as an effective preprocessing procedure and can benefit macro level EMG analysis and micro level motor unit analysis.

Adaptive Hybrid Classifier for Myoelectric Pattern Recognition Against the Interferences of Outlier Motion, Muscle Fatigue, and Electrode Doffing.

Traditional myoelectric prostheses that employ a static pattern recognition model to identify human movement intention from surface electromyography (sEMG) signals hardly adapt to the changes in the sEMG characteristics caused by interferences from daily activities, which hinders the clinical applications of such prostheses. In this paper, we focus on methods to reduce or eliminate the impacts of three types of daily interferences on myoelectric pattern recognition (MPR), i.e., outlier motion, muscle fatigue, and electrode doffing/donning. We constructed an adaptive incremental hybrid classifier (AIHC) by combining one-class support vector data description and multi-class linear discriminant analysis in conjunction with two specific update schemes. We developed an AIHC-based MPR strategy to improve the robustness of MPR against the three interferences. Extensive experiments on hand-motion recognition were conducted to demonstrate the performance of the proposed method. Experimental results show that the AIHC has significant advantages over non-adaptive classifiers under various interferences, with improvements in the classification accuracy ranging from 7.1% to 39% ( ). The additional evaluations on data deviations demonstrate that the AIHC can accommodate large-scale changes in the sEMG characteristics, revealing the potential of the AIHC-based MPR strategy in the development of clinical myoelectric prostheses.