Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue

Rate of change in longitudinal EMG indicates time course of an individual's neuromuscular adaptation in resistance-based muscle training

An individual's long-term neuromuscular adaptation can be measured through time-domain analyses of surface electromyograms (EMG) in regular resistance-based training. The perceived changes in recruitment, such as those measured during muscle fatigue, can subsequently prolong the recovery time in rehabilitation applications. Thus, by developing quantifiable methods for measuring neuromuscular adaptation, adjuvant treatments applied during neurorehabilitation can be improved to reduce recovery times and to increase patient quality of care. This study demonstrates a novel time-domain analysis of long-term changes in EMG captured neuromuscular activity that we aim to use to develop a quantified performance metric for muscle-based intervention training and optimization of an individual. We measure EMG of endurance and hypertrophy-based resistance exercises of healthy participants over 100 days to identify trends in long-term neuromuscular adaptation. Particularly, we show that the rate of EMG amplitude increase (motor recruitment) is dependent on the training modality of an individual. Particularly, EMG decreases over time with repetitive training – but the rate of decrease is different in hypertrophy, endurance, and control exercises. We found that the EMG peak contraction decreases across all subjects, on average, by 8.23 dB during hypertrophy exercise and 10.09 dB for endurance exercises over 100 days of training, while control participants showed negligible change. This represents approximately 2 dB difference EMG activity when comparing endurance and hypertrophy exercises, and >8 dB change when comparing to our control cases. As such, we show that the slope of the long-term EMG activity is related to the resistance-based exercise. We believe this can be used to identify person-specific performance metrics, and to create optimized interventions using a measured performance baseline of an individual

Quantifying Spasticity: A Review

A precise method to measure spasticity is fundamental in improving the quality of life of spastic patients. The measurement methods that exist for spasticity have long been considered scarce and inadequate, which can partly be explained by a lack of consensus in the definition of spasticity. Spasticity quantification methods can be roughly classified according to whether they are based on neurophysiological or biomechanical mechanisms, clinical scales, or imaging techniques. This article reviews methods from all classes and further discusses instrumentation, dimensionality, and EMG onset detection methods. The objective of this article is to provide a review on spasticity measurement methods used to this day in an effort to contribute to the advancement of both the quantification and treatment of spasticity

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Comprehensive assessment of respiratory function, a step towards early weaning from the ventilator

Methods for assessing diaphragmatic function can be useful in determining the functional status of the respiratory system and can contribute to determining an individual’s prognosis, depending on their pathology. They can also be a useful tool for making objective decisions regarding mechanical ventilation weaning and extubation. Esophageal and transdiaphragmatic pressure measurement, diaphragm ultrasound, diaphragmatic excursion, surface electromyography (sEMG) and some serum biomarkers are of increasing interest and use in clinical and intensive care settings to offer a more objective process for withdrawing mechanical ventilation; especially in the situation that we are experiencing with the increased demand for mechanical ventilation to treat patients with Covid-19-associated viral pneumonia. In this literature review, we updated the clinical and physiological indicators with more evidence to improve ventilator withdrawal techniques. We concluded that, to ensure successful extubation in a way that is useful, cost-effective, practical for health personnel and non-invasive for the patient, further studies of novel techniques such as surface electromyography should be implemented

EEG-based investigation of cortical activity during Postural Control

The postural control system regulates the ability to maintain a stable upright stance and to react to changes in the external environment. Although once believed to be dominated by low-level reflexive mechanisms, mounting evidence has highlighted a prominent role of the cortex in this process. Nevertheless, the high-level cortical mechanisms involved in postural control are still largely unexplored. The aim of this thesis is to use electroencephalography, a widely used and non-invasive neuroimaging tool, to shed light on the cortical mechanisms which regulate postural control and allow balance to be preserved in the wake of external disruptions to one’s quiet stance. EEG activity has been initially analysed during a well-established postural task - a sequence of proprioceptive stimulations applied to the calf muscles to induce postural instability – traditionally used to examine the posturographic response. Preliminary results, obtained through a spectral power analysis of the data, highlighted an increased activation in several cortical areas, as well as different activation patterns in the two tested experimental conditions: open and closed eyes. An improved experimental protocol has then been developed, allowing a more advanced data analysis based on source reconstruction and brain network analysis techniques. Using this new approach, it was possible to characterise with greater detail the topological structure of cortical functional connections during the postural task, as well as to draw a connection between quantitative network metrics and measures of postural performance. Finally, with the integration of electromyography in the experimental protocol, we were able to gain new insights into the cortico-muscular interactions which direct the muscular response to a postural challenge. Overall, the findings presented in this thesis provide further evidence of the prominent role played by the cortex in postural control. They also prove how novel EEG-based brain network analysis techniques can be a valid tool in postural research and offer promising perspectives for the integration of quantitative cortical network metrics into clinical evaluation of postural impairment.Kerfi stöðustjórnunar er afturvirkt stýrikerfi sem vinnur stöðugt að því að viðhalda uppréttri stöðu líkamans og bregðast við ójafnvægi. Vaxandi þekking á undanförnum árum hefur lýst því að úrvinnsla þessara upplýsinga á sér stað á öllum stigum miðtaugakerfisins, þá sérstaklega barkarsvæði heilahvela. Engu að síður, er nákvæmu hlutverk heilabarkar við stöðustjórnun enn óljóst að mörgu leyti. Tilgangur þessa verkefnis var að rannsaka nánar hlutverk heilabarkar við truflun og áreiti á kerfi stöðustjórnarinnar, með notkun hágæða heilarafrits (EEG). Við byrjuðum á því að mæla heilarit einstaklinga meðan á þekktri líkamsstöðu-æfingu stóð, til þess að skoða svörun líkamans við röð titringsáreita sem beitt var á kálfavöðvana til að framkalla óstöðugleika. Bráðabirgðaniðurstöður fengnar með PSD-aðferð (power spectral analysis) leiddu í ljós aukna virkni á ákveðnum svæðum í heilaberki og sérstakt viðbragðsmynstur við að framkvæma æfinguna, annars vegar með lokuð augu og hins vegar opin augu. Rannsókn okkar hélt áfram með nýrri og þróaðari tækni sem gerði okkur kleift að framkvæma fullkomnari greiningaraðferðir til að túlka, greina og skilja merki frá heilaritnu. Með fullkomnari greiningaraðferðum var hægt að lýsa með nákvæmari hætti staðfræðilega uppbyggingu starfrænna tenginga í heilaberki meðan á líkamsstöðu æfingunni stóð, sem og að draga tengsl á milli megindlegra netmælinga og mælinga á líkamsstöðu. Að lokum bætist við vöðvarafritsmæling við aðferðafræðina, sem gaf okkur innsýn inn í samskipti heilabarka og vöðvana sem stýra vöðvaviðbrögðum og viðhalda líkamsstöðu við utanaðkomandi áreiti. Á heildina litið gefa niðurstöðurnar sem settar eru fram í þessari ritgerð enn sterkari vísbendingar um það áberandi hlutverk sem heilabörkurinn gegnir við stjórnun líkamsstöðu. Niðurstöðurnar sanna einnig hvernig ný aðferð á greiningu á tengslaneti heilans sem byggir á heilariti getur verið gilt tæki í líkamsstöðu rannsóknum og er nytsamlegt tól fyrir mælingar á heilakerfisneti í klínískt mat á skerðingu líkamsstöðu

Deep Learning Based Upper-limb Motion Estimation Using Surface Electromyography

To advance human-machine interfaces (HMI) that can help disabled people reconstruct lost functions of upper-limbs, machine learning (ML) techniques, particularly classification-based pattern recognition (PR), have been extensively implemented to decode human movement intentions from surface electromyography (sEMG) signals. However, performances of ML can be substantially affected, or even limited, by feature engineering that requires expertise in both domain knowledge and experimental experience. To overcome this limitation, researchers are now focusing on deep learning (DL) techniques to derive informative, representative, and transferable features from raw data automatically. Despite some progress reported in recent literature, it is still very challenging to achieve reliable and robust interpretation of user intentions in practical scenarios. This is mainly because of the high complexity of upper-limb motions and the non-stable characteristics of sEMG signals. Besides, the PR scheme only identifies discrete states of motion. To complete coordinated tasks such as grasping, users have to rely on a sequential on/off control of each individual function, which is inherently different from the simultaneous and proportional control (SPC) strategy adopted by the natural motions of upper-limbs. The aim of this thesis is to develop and advance several DL techniques for the estimation of upper-limb motions from sEMG, and the work is centred on three themes: 1) to improve the reliability of gesture recognition by rejecting uncertain classification outcomes; 2) to build regression frameworks for joint kinematics estimation that enables SPC; and 3) to reduce the degradation of estimation performances when DL model is applied to a new individual. In order to achieve these objectives, the following efforts were made: 1) a confidence model was designed to predict the possibility of correctness with regard to each classification of convolutional neural networks (CNN), such that the uncertain recognition can be identified and rejected; 2) a hybrid framework using CNN for deep feature extraction and long short-term memory neural network (LSTM) was constructed to conduct sequence regression, which could simultaneously exploit the temporal and spatial information in sEMG data; 3) the hybrid framework was further extended by integrating Kalman filter with LSTM units in the recursive learning process, obtaining a deep Kalman filter network (DKFN) to perform kinematics estimation more effectively; and 4) a novel regression scheme was proposed for supervised domain adaptation (SDA), based on which the model generalisation among subjects can be substantially enhanced

EEG-based investigation of cortical activity during Postural Control

The postural control system regulates the ability to maintain a stable upright stance and to react to changes in the external environment. Although once believed to be dominated by low-level reflexive mechanisms, mounting evidence has highlighted a prominent role of the cortex in this process. Nevertheless, the high-level cortical mechanisms involved in postural control are still largely unexplored. The aim of this thesis is to use electroencephalography, a widely used and non-invasive neuroimaging tool, to shed light on the cortical mechanisms which regulate postural control and allow balance to be preserved in the wake of external disruptions to one’s quiet stance. EEG activity has been initially analysed during a well-established postural task - a sequence of proprioceptive stimulations applied to the calf muscles to induce postural instability – traditionally used to examine the posturographic response. Preliminary results, obtained through a spectral power analysis of the data, highlighted an increased activation in several cortical areas, as well as different activation patterns in the two tested experimental conditions: open and closed eyes. An improved experimental protocol has then been developed, allowing a more advanced data analysis based on source reconstruction and brain network analysis techniques. Using this new approach, it was possible to characterise with greater detail the topological structure of cortical functional connections during the postural task, as well as to draw a connection between quantitative network metrics and measures of postural performance. Finally, with the integration of electromyography in the experimental protocol, we were able to gain new insights into the cortico-muscular interactions which direct the muscular response to a postural challenge. Overall, the findings presented in this thesis provide further evidence of the prominent role played by the cortex in postural control. They also prove how novel EEG-based brain network analysis techniques can be a valid tool in postural research and offer promising perspectives for the integration of quantitative cortical network metrics into clinical evaluation of postural impairment

Investigation into the control of an upper-limb myoelectric prosthesis

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