Assessment of the non-linear response of the fSampEn on simulated EMG signals

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksFixed sample entropy (fSampEn) is a promising technique for the analysis of respiratory electromyographic (EMG) signals. Its use has shown outperformance of amplitude-based estimators such as the root mean square (RMS) in the evaluation of respiratory EMG signals with cardiac noise and a high correlation with respiratory signals, allowing changes in respiratory muscle activity to be tracked. However, the relationship between the fSampEn response to a given muscle activation has not been investigated. The aim of this study was to analyze the nature of the fSampEn measurements that are produced as the EMG activity increases linearly. Simulated EMG signals were generated and increased linearly. The effect of the parameters r and the size of the moving window N of the fSampEn were evaluated and compared with those obtained using the RMS. The RMS showed a linear trend throughout the study. A non-linear, sigmoidal-like behavior was found when analyzing the EMG signals using the fSampEn. The lower the values of r, the higher the non-linearity observed in the fSampEn results. Greater moving windows reduced the variation produced by too small values of r.Peer ReviewedPostprint (author's final draft

Deep Learning-Enabled Swallowing Monitoring and Postoperative Recovery Biosensing System

This study introduces an innovative 3D printed dry electrode tailored for biosensing in postoperative recovery scenarios. Fabricated through a drop coating process, the electrode incorporates a novel 2D material.Comment: the abstract can't uploaded full

Deep learning for surface electromyography artifact contamination type detection

The quality of surface Electromyography (sEMG) signals could be an issue if highly contaminated by Power Line Interference (PLI), Electrocardiogram signal (ECG), Movement Artifact (MOA) or White Gaussian Noise (WGN), that could lead to unsafe operation of devices that is controlled by sEMG data, such as electro-mechanical prothesis. There are some mitigation methods proposed for some specifics sEMG contaminants and to use these methods in an efficient way is important to identify the contaminant in the sEMG signal. In this work we propose the use of a Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) units in the hidden layer with no need of features extraction with the objective to classify the signal directly from sequences of the band-pass filtered data. The method proposed use the NinaPro database with amputee and non-amputee subjects. Only non-amputee subjects are used for parameters selection and then tested on both databases. The results show that 98% of the non-contaminated sEMG data was corrected classified and more than 95% of the contaminants were identified inside the training SNR range. Also, in this work is presented a SNR sensibility control and the contamination analysis in the range from −40 dB to 40 dB in 10 dB steps. The conclusion is that is possible to classify the contamination type in sEMG signals with a RNN-LSTM with a 112.5 ms time window and to predicted with a small error the classification hit rate for each SNR level in some cases

Influence of parameter selection in fixed sample entropy of surface diaphragm electromyography for estimating respiratory activity

Fixed sample entropy (fSampEn) is a robust technique that allows the evaluation of inspiratory effort in diaphragm electromyography (EMGdi) signals, and has potential utility in sleep studies. To appropriately estimate respiratory effort, fSampEn requires the adjustment of several parameters. The aims of the present study were to evaluate the influence of the embedding dimension m, the tolerance value r, the size of the moving window, and the sampling frequency, and to establish recommendations for estimating the respiratory activity when using the fSampEn on surface EMGdi recorded for different inspiratory efforts. Values of m equal to 1 and r ranging from 0.1 to 0.64, and m equal to 2 and r ranging from 0.13 to 0.45, were found to be suitable for evaluating respiratory activity. fSampEn was less affected by window size than classical amplitude parameters. Finally, variations in sampling frequency could influence fSampEn results. In conclusion, the findings suggest the potential utility of fSampEn for estimating muscle respiratory effort in further sleep studies.Peer ReviewedPostprint (published version

Anatomical mapping of SEMG signal quality and controllability in skeletal muscle groups for myoelectric prosthesis

This thesis presents the hardware implementation for myoelectric signal processing and the experimental evaluation of myoelectric signals to characterize the controllability of the muscle groups in the upper body for controlling the myoelectric prosthetic device. Digital filters were implemented to improve the quality of raw myoelectric signals acquired from the targeted muscle groups. The 5th order median filter implementation provided the reliable noise reduction for the electrophysiological noise observed in the abdominal muscle groups. The real-time onset detection algorithm was implemented to determine the onset and the offset of myoelectric signals and to generate discrete control signals for the prosthetic device. The experiment was designed to investigate the adequacy of utilizing myoelectric signals from the muscle groups in the upper body–deltoids, pectoralis majors, latissimus dorsi, and external obliques–as used in the control of myoelectric prosthetic devices. The voluntary muscle contraction capability of each targeted muscle group was evaluated during the experiment. It was demonstrated that the precise and accurate myoelectric control was achieved using the deltoids muscle group. However, the pectoralis majors and the external obliques were proven to be more appropriate to apply to fast switching on/off control. The combinations of the myoelectric signals acquired from the deltoids and the latissimus dorsi were investigated to generate multiple output stages, and 4 discrete states of myoelectric output were obtained using those muscle groups simultaneously.M.S

Complexity Analysis of Surface EMG for Overcoming ECG Interference toward Proportional Myoelectric Control

Electromyographic (EMG) signals from muscles in the body torso are often contaminated by electrocardiography (ECG) interferences, which consequently compromise EMG intensity estimation. The ECG interference has become a barrier to proportional control of myoelectric prosthesis using a neural machine interface called targeted muscle reinnervation (TMR), which involves transferring the residual amputated nerves to nonfunctional muscles (typically pectoralis muscles for high level amputations). This study investigates a novel approach toward implementation of proportional myoelectric control by applying sample entropy (SampEn) analysis of surface EMG signals for robust intensity estimation in the presence of significant ECG interference. Surface EMG data from able-bodied and TMR amputee subjects with different degrees of ECG interference were used for performance evaluation. The results showed that the SampEn analysis had high correlation with surface EMG amplitude measurement but low sensitivity to different degrees of ECG interference. Taking this advantage, SampEn analysis of surface EMG signal can be used to facilitate implementation of proportional myoelectric control against ECG interference. This is particularly important for TMR prosthesis users

Non-Invasive Investigation of Human Foot Muscles Function

Appropriate functioning of the human foot is fundamental for good quality of life. The intrinsic foot muscles (IFM) are a crucial component of the foot, but their natural behaviour and contribution to good foot health is currently poorly understood. Recording muscle activation from IFM has been attempted with invasive techniques, but these generally only allow assessment of one muscle at a time and are not much used in many clinical populations (e.g. children, patients with peripheral neuropathy or on blood thinning medication). Here a novel application of multi-channel surface electromyography (sEMG) electrodes is presented to non-invasively, record sEMG and quantify activation patterns of IFMs from across the plantar region of the foot. sEMG (13×5 array), kinematics and force plate data were recorded from 30 healthy adult volunteers who completed six postural balance tasks (e.g. bipedal stance, one-foot stance, two-foot tip-toe). Linear (amplitude based) and non-linear (entropy based) methodologies were used to evaluate the physiological features of the sEMG, the patterns of activation, the association with whole body and foot biomechanics and the neuromuscular drive to the IFM. EMG signals features (amplitude and frequency) were shown to be in the physiological ranges reported in the literature (Basmajian and De Luca, 1985), with spatially clustered patterns of high activation corresponding to the Flexor digitorum brevis muscle. IFMs responded differently based on the direction of postural sway, with greater activations associated with sways in the mediolateral direction. Entropy based, non-linear analysis revealed that neuromuscular drive to IFM depends on the balance demand of the postural task, with greater drive evident for more challenging tasks (i.e. standing on tiptoe). Combining non-invasive measures of IFM activation and entropy based assessment of temporal organisation (or structure) of EMG signal variability is therefore revealing of IFM function and will enable a more detailed assessment of IFM function across healthy and clinical populations

Evaluación no invasiva del impulso neural respiratorio y su relación con la respuesta mecánica mediante el análisis de señales electromiográficas de músculos respiratorios

Respiratory muscle contraction occurs in response to the electrical stimulation of the muscles. These electrical stimuli originate in the respiratory neurons of the brainstem, are transmitted via motor nerves to the neuromuscular junctions and propagate along muscle fibers. Respiratory electromyography measures the electrical activity of respiratory muscles in response to this nerve stimulation. The neural respiratory drive (NRD) is best expressed in a phrenic neurogram, but this is not feasible in humans. Alternatively, measurements of the diaphragm electromyographic signal (EMGdi) would most likely reflect phrenic neurogram activity. EMGdi signal can be recorded using invasive methods, involving the use of needle electrodes or electrodes positioned in the esophagus at the level of the diaphragm. As a non-invasive alternative, the study of respiratory muscle activity can be addressed by surface electromyography. The onset and offset of the neural inspiratory time (nton and ntoff, respectively) are fundamentally important measurements in studies of patient-ventilator interaction, where the level of assistance delivered by the ventilator is controlled by patient demand. Cardiac artifacts (ECG) often make it difficult to utilize EMGdi. To overcome the shortcoming of the ECG, in this thesis is proposed to use sample entropy with fixed tolerance values (fSampEn), a robust technique against impulsive noise. To evaluate nton and ntoff estimation it has been carried out an experimental study with surface EMGdi signals recorded in healthy subjects during two respiratory protocols designed to evaluate the influence of different breathing patterns on the EMGdi. These protocols consisted of a stepwise increase in respiratory rate (RR) with constant fractional inspiratory time (Ti/Ttot) and a stepwise decrement in the Ti/Ttot with constant RR, respectively. The developed algorithms allowed to determine the nton and ntoff and derive the RR, Ti and Ti/Ttot neural ventilatory parameters. The EMGdi amplitude provides a real-time indirect measure of the NRD, which reflects the load on the respiratory muscles. The NRD, assessed by normalized EMGdi signals, is higher in patients with respiratory disease than in healthy subjects. To evaluate the behavior of the fSamp En, as a method for improving the measurement of NRD from EMGdi signals in the presence of cardiac activity, compared to the average rectified value and root mean square value approaches, first, these methods have been applied to synthetic EMGdi signals . Secondly, we tested the proposed methods in an experimental study with EMGdi signals recorded in healthy subjects during an incremental inspiratory load test. The EMGdi amplitude allowed to evaluate changes in the respiratory muscle activation patterns and estimate the NRD. Also, this thesis contributes to the study of the respiratory activity by the non-invasive recording of mechanomyographic low frequency (BF) activity in healthy subjects and in patients with chronic obstructive pulmonary disease, allowing the study of bilateral asynchrony of the diaphragm and the RR. Finally, we have proposed the use of concentric ring electrodes as an alternative to improve the spatial resolution of electromyographic recordings, and eliminate the problems associated with the location and orientation of the bipolar configuration. The approaches presented in this doctoral thesis based on the analysis of electromyographic and mechanomyographic signals of respiratory museles allow to extract complementary information to current use techniques of and contribute to the study of respiratory function in the clinical setting .La contracción de los músculos respiratorios se produce en respuesta a la estimulación eléctrica. Estos estímulos se originan en las neuronas respiratorias del tronco del encéfalo, se transmiten a través de los nervios motores a las uniones neuromusculares y se propagan a lo largo de las fibras musculares. La electromiografía respiratoria mide la actividad eléctrica de los músculos respiratorios en respuesta a esta estimulación nerviosa. El impulso neural respiratorio (NRD) se expresa mejor a través del neurograma frénico, pero esto no es factible en los seres humanos. Como alternativa, la medida de la señal electromiográfica del diafragma (EMGdi) refleja de forma indirecta la actividad frénica. La señal EMGdi puede registrarse utilizando métodos invasivos, lo que implica el uso de electrodos de aguja o electrodos colocados en el esófago a nivel del diafragma . Como alternativa no invasiva, el estudio de la actividad muscular respiratoria puede abordarse mediante la electromiografía de superficie. El inicio y fin del tiempo neural inspiratorio (nton y ntoff, respectivamente) son medidas de importancia en los estudios de interacción paciente-ventilador, donde el nivel de la asistencia proporcionada por el ventilador es controlado por la demanda del paciente. Los artefactos cardíacos (ECG) a menudo hacen que sea difícil de utilizar la señal EMGdi. Para superar el inconveniente de la interferencia ECG, en la presente tesis se propone utilizar la entropía muestra! con valores de tolerancia fijos (fSampEn), una técnica que es robusta contra el ruido de tipo impulsivo. Para evaluar la estimación del nton y ntoff se ha realizado un estudio experimental con señales EMGdi superficie registrada en sujetos sanos durante dos protocolos respiratorios, diseñados para evaluar la influencia de los diferentes patrones respiratorios sobre la señal EMGdi. Estos protocolos consistieron en un aumento gradual de la frecuencia respiratoria (RR) con un tiempo inspiratorio (Ti) fracciona! constante (Ti!Ttot) y en una disminución gradual en el Ti!Ttot con una RR constante, respectivamente. Los algoritmos desarrollados han permitido determinar el nton y el ntoff y derivar los parámetros ventilatorios RR, Ti, y TifTtot neurales. La amplitud de la EMGdi proporciona una medida indirecta del NRD, que refleja la carga sobre los músculos respiratorios. El NRD, evaluado en señales EMGdi normalizadas, es mayor en pacientes con enfermedades respiratorias que en sujetos sanos. Para evaluar el comportamiento de la fSampEn, como un método para mejorar la medición del NRD a partir de señales EMGdi en presencia de ECG, en comparación con los enfoques basados en el uso del valor rectificado medio y valor cuadrático medio, primero, se han aplicado estos métodos a señales EMGdi sintéticas . En segundo lugar, hemos probado los métodos propuestos en un estudio experimental con señales EMGdi registradas en sujetos sanos durante una prueba de carga inspiratoria incremental. La amplitud de la EMGdi permitió evaluar los cambios en el patrón de activación de los músculos respiratorios y estimar el NRO. Asimismo, esta tesis doctoral contribuye al estudio de la actividad respiratoria mediante el registro no invasivo de actividad mecanomiográfica de baja frecuencia (BF) en sujetos sanos y en pacientes con enfermedad obstructiva crónica, permitiendo el estudio de la asincronía bilateral del diafragma y la RR. Finalmente, hemos propuesto el uso de electrodos de anillos concéntricos como una alternativa para mejorar la resolución espacial de los registros electromiográficos, y eliminar los problemas asociados a la localización y orientación de la configuración bipolar. Los enfoques presentados en esta tesis doctoral basados en el análisis de señales electromiográficas y mecanomiográficas de los músculo