Technology Review the Use of Surface Emg in the Diagnosis and Treatment
Introduction
The current country-of-the-art rehabilitation for individuals with spinal cord injuries (SCI) utilizes technologies such as neuromodulation using exoskeleton robotics, functional electrical stimulation (FES), treadmill training with and without body-weight back up (BWS) in add-on to the traditional exercise-based rehabilitation. The contempo developments in neurorehabilitation research and technologies have resulted in a shift in focus toward the recovery of part through high intensity repetitive training afterwards SCI (1). Some of the technologies such as epidural or transcutaneous spinal stimulation, robotic exoskeletons are currently under investigation while techniques such as FES (eastward.g., FES cycling, rowing) (ii) and treadmill grooming using BWS (3) are commonly used in clinics to assist with the functional tasks such every bit respiration, mobility, hand function, metabolism, float, bowel or sexual function (two, 4, v). Irrespective of the intervention approach used, the functional status besides as the evolutions of motor impairments and motor recovery are ofttimes tracked past visual and manual assessments in the dispensary. To engagement, the primary method for evaluating the motor function for SCI is the American Spinal Injury Association Harm Scale (AIS), which tests transmission muscle strength in 5 cardinal muscles in each limb and examines sensory function (6). Although easy to perform, such approaches are subjective and not sensitive to agreement the changes at the neuromuscular levels. Especially for the interventions that target the neuromuscular mechanisms via application of electrical stimulation to the nerves, or peripheral musculature, the objective and quantifiable information on the myoelectric output of targeted muscles is highly relevant. For instance, when a clinician uses FES -the technique that involves the application of electrical current to the neuromuscular junction and crusade contractions in paralyzed muscles (seven)- it is clinically desirable to evaluate the resultant myoelectric output of the stimulated muscle. The questions such as "is the stimulation intensity sufficient to induce the desired wrinkle for the targeted movement?" "is the stimulation causing the targeted muscle to fatigue?" or "are the selected parameters advisable for the patient to perform the desired task?" become highly relevant to the clinician to deliver patient-specific and effective interventions. Such questions are highly significant for any intervention that targets mobility and motor rehabilitation.
Surface electromyography (sEMG), a non-invasive technique for assessing the myoelectric output of a muscle, can provide objective answers to these meaning questions. sEMG has shown great promise in neurorehabilitation enquiry and has been a widely-utilized tool to assess neuromuscular outcomes in research (eight). Yet, the awarding of sEMG in a clinical surroundings has been limited (ix). The clinicians' perspectives on the use of sEMG accept reported several barriers including express fourth dimension and resource, clinically inapplicable sEMG organisation features and the majority of clinicians' lack of training and/or conviction in utilization of sEMG technology (10, 11). In the domain of the SCI population, in addition to the aforementioned challenges of using sEMG in the dispensary, severely dumb physiological and structural country of the spinal cord after SCI (compared to other pathologies such as stroke, traumatic brain injury, multiple sclerosis, etc.) farther limits sEMG usage to provide time-efficient, meaningful interpretations. In this perspective report, we hash out these barriers and the directions toward overcoming these limitations that hinder the widespread apply of sEMG technology in the clinical rehabilitation of individuals with SCI.
Barriers in the Apply of sEMG in SCI neurorehabilitation
General Barriers to Employ sEMG in a Clinical Setting
Several barriers can be identified that restrict the adoption of sEMG technology in a clinical environment.
Lack of Information at Motor Unit of measurement (MU) Level
Needle EMG (nEMG) and fine wire EMG (fwEMG) are the invasive forms of EMG for accessing neurophysiological attributes of neuromuscular diseases. Nonetheless, the invasiveness, discomfort, and limited applicability of these techniques on multiple muscles during dynamic tasks limit their utilise in the clinic. Nevertheless, nEMG yet is gilt standard for clinical diagnosis of nervus and musculus pathologies and preferred over non-invasive sEMG (12) for neurophysiological applications. This is because of the limited spatial resolution of sEMG that results in poor fidelity recordings of loftier-frequency signals (east.m., polyphasic potentials, fibrillation potentials, and positive abrupt waves) (12). In improver, the electrical cross-talk between two or more neighboring muscles restricts the sEMG to identify the origin of the electrical betoken when these muscles are active simultaneously (12). Further, the sEMG recorded from a musculus does non yield a non-ambiguous extraction of unmarried MU information. Every bit a issue, the report of the therapeutics and technology cess subcommittee of the American Academy of Neurology reported the sEMG technique unusable for clinical neurophysiological purposes (13). While the bipolar sEMG is used to measure muscle activations, the advent of loftier-density surface EMG (HDEMG) has made the extraction of MU features possible (14–16). Availability of such a sensitive tool is even more pregnant for individuals with clinically diagnosed motor and sensory consummate SCI who practise not take intact reflexes and who may still have intact neuronal axons beyond the injury lesion (17). Still, in lodge to accomplish this, a conscientious application of sEMG decomposition and expertise in signal conquering, interpretation of results, and manual assessment of decomposition quality is required (14–16). Further, the examination of the Motor Unit of measurement Number Index (MUNIX) in paralyzed muscles has been implemented to monitor MU loss after SCI (18). However, this approach requires intense experimental and computational setup, and specific option criteria which may not be clinically viable.
Lack of Bachelor Time for a Clinician
A study collected perspectives of 22 clinicians [physical therapists (PT), occupational therapists (OT), and physiatrists] and reported limited clinician time as one of the barriers to the uptake of sEMG technology in clinics (11). The time-consuming aspect of sEMG technology presents a pregnant barrier to its translation into clinical practices. Electrodes and peel preparations, electrode placements, equipment setup, collecting maximal volitional contractions (MVC) for normalization prior to recording the data during activities of involvement have pregnant time. Effigy 1 illustrates the sEMG placements for recording of lower extremity responses from an individual with an SCI. Balancing a busy work schedule has been reported every bit 1 of the barriers to caring for patients, particularly for novice PTs (nineteen). Therefore, the credence of sEMG engineering that requires significant prep-fourth dimension is depression as the added time could adversely affect PT performance and intendance in the dispensary.
Effigy ane. An EMG gear up for assessing neuromuscular responses prior to a rehabilitation intervention for an individual with SCI.
Limited Groundwork and Training Through Professional Curricula
Most of the PT and OT programs offer wide-ranging coursework in the rehabilitation domain including human beefcake, neuroscience, biomechanics, kinesiology, move assay, evidence-based practice, pharmacological interventions, etc. Irrespective of the breadth of topics covered, in that location is minimal focus on the technological aspects of rehabilitation. As a issue, rehabilitation tools such as sEMG are theoretically taught, but practical knowledge imparted is limited. Further, the educational content may not cover ever-evolving aspects of sEMG applied science and its applications. Feldner et al. reported that many clinicians felt less confident to use sEMG in clinics due to their limited feel (11). According to the study, newer clinicians pointed the "need for do" and the seasoned clinicians weren't "tech savvy," making the clinical adoption of sEMG technology difficult (eleven). However, 1 limitation of this survey was the limited geographical spread of the clinicians who participated equally they were recruited from rehabilitation settings within the Seattle metropolitan area (WA, U.s.a.). In a more recent survey by Manca et al., 35 EMG experts from unlike educational, professional person and geographical backgrounds supported the clinical utility of sEMG for optimizing the quantification of muscle and concrete function, to define the intervention plan, and optimize other methods used to quantify muscle and physical function (xx). However, the collective opinion of these experts also confirmed the utilization of sEMG was more common in technical/methodological research than clinical research (xx). The barriers that preclude prompt transfer of sEMG into exercise were reported to exist boring dissemination of enquiry findings and the lack of education on sEMG (xx). Farther, successful adoption of any technology in the clinic not only involves collecting the information/data merely also helps in making data-driven clinical decisions in functional diagnosis, recommending appropriate interventions, and optimizing the rehabilitation outcomes. In terms of sEMG, the processing and estimation of the information require a multidisciplinary approach. This involves the working knowledge of several technical domains such equally instrumentation, betoken processing and analysis, algorithm development, and statistical analyses. The availability of such expertise can exist challenging in a clinical setting. Identifying the experts with such a skillset and establishing collaborations could be time-consuming, and impractical for daily-workflow at the clinic. If a clinician wants to proceeds the necessary working knowledge on sEMG engineering, in that location is no centralized knowledge-base where clinicians tin can, non only develop their understanding of sEMG procedures and data analyses but also collaborate with other clinicians and researchers in this specific domain to share ideas, discuss outcomes and even collaborate at the institutional levels. The grooming and education of teachers who are educating time to come clinicians is another important factor. In many countries where there are no doctoral-level programs in rehabilitation or physiotherapy, there is a scarcity of academic professors with doctorate-level credentials. Therefore, the educational experience of students in such countries may lack rigor, practical exposure to the technology, and the land-of-the-art information on sEMG practices and guidelines.
Lack of Technology Transfer From Enquiry to Clinic
The field of sEMG is ever evolving and new algorithms for sEMG processing, assay, and classifications are continuously existence developed. However, the rate at which these technological advances are oftentimes integrated into the existing sEMG systems is express. For case, many of the existing off-the-shelf sEMG systems have not gone beyond implementing the basic sEMG features such as hateful and root-mean-square (RMS) amplitudes, moving average or RMS envelopes, bones filtering and rectifications, and basic Fourier-based analysis. Automatic flare-up or ON-OFF detections, activation timing analyses, indicate decomposition, and time-frequency analyses are widely published (21–25) and accepted EMG analysis techniques that have not been integrated into virtually of the commercial systems; as a result, these techniques have not been transferred from enquiry to clinic. This issue stems from lack of education or grooming on the application of such analysis methods in a clinical setting, resulting in virtually no need for a commercial EMG system with these capabilities, which in turn creates an insignificant market to industry such EMG devices. Therefore, the absence of commercial pressure farther limits the development of said devices and education of operators to ultimately transfer enquiry findings into the dispensary.
Institutional Level Barriers
In addition to the sEMG setup time, other challenges hinder the adoption of sEMG technology in the dispensary. Such barriers include the functionality in multiple environments, portability, the facility layout, purchasing cost and maintenance, providing testify to support returns on such investments, and staff training.
Barriers Specific to the SCI Population
The need for assessing neuromuscular responses is highly pregnant for individuals with SCI, particularly motor complete SCI (cSCI). Studies have demonstrated the presence of intact neuronal axons across the lesion, even later cSCI (17). For instance, Calancie et al. (26) reported retained voluntary EMG control over one muscle in the foot in a small group of participants classified as motor consummate. These findings highlight the significance of the power to monitor neuromuscular responses during neuromuscular electrical stimulation (NMES) for cSCI for whom any functional and motor-related changes may not be apparent, while intrinsic electrophysiological changes and residual volitional neuromuscular drive may still be present. In evaluating the efficacy of any clinical therapy, the effects may not be visible at the functional or biomechanical levels but changes could exist present at the neuromuscular level. Therefore, assessing neuromuscular output is critical to optimize the effects of any rehabilitation intervention for SCI. Currently, there are no standardized procedures for processing and interpreting sEMG data specific to the cSCI population; this may accept vastly contributed to the diverse sEMG interpretations and/or continued reliance on consequence measures, such as forcefulness and torque. In addition, the lack of standards for sensors, configurations, electrode placement, and recording protocols has adversely afflicted the possibility of its integration into routine clinical use (nine). Despite the 20-year presence of the EU project on "Surface EMG for Non-Invasive Assessment of Muscles (SENIAM)," real international standards are still missing (ten). The macerated or weaker sEMG signals yield limited consensus on answers to the about basic questions such as, "is the muscle agile?" "what is the forcefulness of the activation?" or more complicated ones such equally, "what is the volitional contribution and how it relates to the applied stimuli during electrically induced activations?" Answers to such questions remain unclear every bit there is no standardized approach to first process and then interpret such data. The existing off-the-shelf systems are not specifically tuned to address these SCI-specific challenges. For example, the well-nigh pregnant bulwark in using sEMG during FES is interpreting the recorded sEMG signals due to the overpowering presence of stimulation artifact. The stimulation antiquity is a broadband betoken with widespread stimulation frequency harmonics at high amplitudes that engulf the myoelectric responses in sEMG. Especially when a train of ES pulses is applied, the sEMG recordings are accompanied by ES artifact spikes with magnitudes that are manifold compared to the actual MU outputs. Moreover, the presence of stimulation artifact is not bars in the time-domain; information technology is besides observed in the frequency domain. The harmonics of stimulation frequency overlap with the majority of the energy bands in a typical sEMG frequency spectrum (xx–350 Hz). As a upshot, traditional selected-filtering of frequency bands, to remove ES artifacts, is ineffective and results in significant data loss (23). The ES antiquity affects features derived from the sEMG signal; for instance, it biases conduction velocity estimations, spectral characteristic frequencies, and M-moving ridge amplitudes (27). In the domain of SCI rehabilitation, where ES waveforms are often delivered equally bursts (train of pulses) with high intensities and wide-ranging frequencies, the resultant contagion of sEMG recordings obstructs the understanding of the direct implication of FES on the neuromuscular output in terms of activation intensity (voluntary or ES induced), MU recruitment, and muscle fatigue. This is particularly impeding in studies where FES is combined with volitional efforts that need to be monitored or modulated in real-time to accomplish optimal outcomes.
Future Directions
Rehabilitation professionals' acceptance and adoption of technologies rely on conditions that facilitate their apply such equally scheduling, support and a conductive environment (28). The post-obit are the steps toward achieving these key aspects of sEMG utilization in the clinical neurorehabilitation.
Enhancing Knowledge and User-Experience
In order to ensure all rehabilitation professionals, specially clinicians, get an early on exposure to the sEMG technology, the educational and professional training programs could integrate hands-on sEMG experience through example studies or small research projects. The clinicians could also enhance their involvement in ongoing sEMG-related research activities and get exposed to the several practical aspects of sEMG through interactions with their non-clinical counterparts (eastward.g., engineers, technicians, data scientists). The interfaces running the EMG information collection and processing algorithms with minimal user inputs could be beneficial for their widespread implementations. Another goal could exist set to successfully transfer EMG-related research products (information drove, processing and analysis algorithms) into a clinical environment. Irrespective of the programming platforms (Matlab, Python, etc.) on which these algorithms are congenital upon, elementary user-interfaces, application programming interfaces (APIs) and/or open-source executables can be created for their unobstructive and intuitive use by the clinicians with non-technical backgrounds. A centralized knowledge-base can exist used to create and disseminate the sEMG tutorials on topics ranging from the basics of sEMG technology to stride-by-footstep guidelines for information processing. Such a centralized open-source platform can likewise facilitate the collaborations among investigators and sEMG users with overlapping interests. With the assist of well-established societies such every bit International Society of Electromyography and Kinesiology (ISEK), IEEE Engineering in Medicine and Biology Social club (EMBS), Society for Neuroscience (SFN), and several societies of clinical motion assay [Gait Clinical Movement Analysis Lodge (GCMAS), the European Society for Motility Analysis in Adults and Children (ESMAC), Societa' Italiana di Analisi del Movimento in Clinica (SIAMOC) etc.], the long-term goal can exist fix to developing international scientific meetings or chapters specific to sEMG applications in specific rehabilitation domain (e.g., FES) where the specific pool of researchers tin encounter, share knowledge and collaborate. In recent years, efforts have been fabricated to provide open-access tutorials and consensus articles on sEMG-related all-time practices, such every bit the consensus standards and guidelines on the sEMG detection (29), sEMG signal conditioning and preprocessing (xxx), and analysis of MU discharge characteristics using HDEMG (14). The Consensus for Experimental Design in Electromyography (CEDE) project, an international initiative which aims to guide decision-making in recording, analysis, and interpretation of sEMG have published the guidelines on the sEMG electrode pick and aamplitude normalization (31, 32). Despite of these past and present efforts, these well-accepted guidelines, procedures and standards are not known to many clinicians. The paradigm shift in transferring such meaning knowledge to clinic is simply possible when the new generations of students pursuing education and professional training in clinical rehabilitation (e.1000., PT, PTA, MPT, DPT, DScPT, PhD) are taught these "best practices in sEMG" by qualified teachers.
EMG for Real-Fourth dimension Monitoring and Biofeedback During Rehabilitation
The instantaneous quantification of muscle response tin serve as an of import marker to track the impairment likewise equally recovery during rehabilitation. With access to the EMG in existent-fourth dimension, the clinicians or researchers can quantify, rails, and manipulate levels of voluntary efforts by modulating intervention parameters. For example, if a clinician observes that the FES frequency of 100 Hz is causing a muscle to fatigue faster with less voluntary participation (shown past EMG features such as aamplitude), s(he) could change to a lower stimulation frequency, which could potentially increase voluntary contribution and reduce fatigue due to stimulation, thus making the session still productive. Such modulations could happen simply by patient's own feedback on fatigue just the data-driven nature of this decision making could make the grooming more objective, patient-specific, safety and less ad hoc. This could result in more constructive interventions for better long-term benefits.
A Ranking System for Standardization of EMG Interpretations for the SCI
Motivated by the ranking system provided by Heald et al. (17), a standardized sEMG ranking organisation can be adult to quantify the land of the residual neuromuscular output, peculiarly during FES-based rehabilitation for SCI. For case, Rank 1 – sEMG indicate tin can be classified as no activeness, baseline dissonance; Rank 2 – Sparse MU activeness potentials; Rank 3 – Burst of activeness only no articulate correlation to stimulation profile (east.g., FES, etc.); Rank iv – Outburst of activity with partial correlation to stimulation; Rank 5 – Repeated burst of visible activity that is significantly correlated with applied stimulation. Ranking procedures tin be validated by visual inspection as well as automated, software-driven inspections. Such a standardized arroyo can track progress during or afterward different interventions. In one case accepted and implemented, common standardized outcomes would enable comparison different interventions for efficacy.
The Potential Impact on the Rehabilitation Costs for SCI
For many of the SCI patients, functional or motor changes may non be present but electrophysiological changes or residual voluntary muscle activations may however be nowadays (17, 26, 33, 34). If a clinician cannot directly track the volitional efforts or functional improvements, and so medical reimbursement is suspended after only a few weeks with no ultimate do good to the participant. If sEMGs show the neuromuscular changes during an intervention for individuals with SCI with no changes in functional condition, researchers and clinicians can notwithstanding go along with ongoing interventions and anticipate better outcomes. On the other paw, investing in expensive interventions for several months for non-responders is a financial liability. Thus, sensitive and reliable measures of neuromuscular recovery, designed specifically for the spectrum of SCI-induced deficits can lead to long-term functional improvement that would accept a dramatic touch on both on the quality of life and fiscal liability for those suffering from SCI.
In summary, addressing the current barriers in widespread utilize of sEMG in SCI rehabilitation will crave a collaborative, interdisciplinary, and unified arroyo. Nonetheless, sEMG technology has the potential to present significant opportunities that tin can allow clinicians and researchers to transform future interventions into constructive and impactful rehabilitation modalities for individuals with SCI.
Information Availability Statement
The data sharing volition exist contingent upon the regulations applied by the funding agency. Requests to admission the datasets should be directed to rpilkar@kesslerfoundation.org.
Ethics Argument
The studies involving human being participants were reviewed and approved by Kessler Foundation Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
RP and KM drafted the manuscript. All authors contributed to the revisions.
Funding
This work was supported by the New Bailiwick of jersey Committee on Spinal Cord Research grants (CSCR20ERG013, 314 CSCR14ERG007) and Kessler Foundation.
Conflict of Interest
The authors declare that the research was conducted in the absence of whatsoever commercial or fiscal relationships that could exist construed as a potential conflict of involvement.
References
1. Musselman KE, Shah M, Zariffa J. Rehabilitation technologies and interventions for individuals with spinal cord injury: translational potential of electric current trends. J Neuroeng Rehabil. (2018) fifteen:40. doi: 10.1186/s12984-018-0386-seven
CrossRef Full Text | Google Scholar
2. Luo S, Xu H, Zuo Y, Liu X, All AH. A review of functional electrical stimulation treatment in spinal string injury. Neuromolecular Med. (2020) 22:447–63. doi: ten.1007/s12017-019-08589-nine
PubMed Abstract | CrossRef Full Text | Google Scholar
3. Alajam R, Alqahtani AS, Liu W. Effect of body weight-supported treadmill preparation on cardiovascular and pulmonary function in people with spinal cord injury: a systematic review. Acme Spinal Cord Inj Rehabil. (2019) 25:355–69. doi: 10.1310/sci2504-355
PubMed Abstruse | CrossRef Total Text | Google Scholar
4. Ho CH, Triolo RJ, Elias AL, Kilgore KL, DiMarco AF, Bogie Grand, et al. Functional electrical stimulation and spinal cord injury. Phys Med Rehabil Clin Due north Am. (2014) 25:631–54, ix. doi: ten.1016/j.pmr.2014.05.001
PubMed Abstract | CrossRef Full Text | Google Scholar
five. Ragnarsson KT. Functional electrical stimulation after spinal cord injury: current use, therapeutic effects and future directions. Spinal Cord. (2008) 46:255–74. doi: 10.1038/sj.sc.3102091
PubMed Abstract | CrossRef Full Text | Google Scholar
6. Marino RJ, Barros T, Biering-Sorensen F, Burns SP, Donovan WH, Graves DE, et al. International standards for neurological classification of spinal string injury. J Spinal String Med. (2003) 26 (Suppl. 1):S50–vi. doi: 10.1080/10790268.2003.11754575
PubMed Abstruse | CrossRef Total Text | Google Scholar
8. Merletti R, Farina D. Surface Electromyography : Physiology, Engineering science, and Applications. Hoboken, NJ: Wiley-IEEE Press (2016).
Google Scholar
10. Campanini I, Disselhorst-Klug C, Rymer WZ, Merletti R. Surface EMG in clinical assessment and neurorehabilitation: barriers limiting its use. Front end Neurol. (2020) 11:934. doi: 10.3389/fneur.2020.00934
PubMed Abstract | CrossRef Full Text | Google Scholar
eleven. Feldner HA, Howell D, Kelly VE, McCoy SW, Steele KM. "Look, Your Muscles Are Firing!": a qualitative study of clinician perspectives on the utilise of surface electromyography in neurorehabilitation. Curvation Phys Med Rehabil. (2019) 100:663–75. doi: 10.1016/j.apmr.2018.09.120
PubMed Abstract | CrossRef Full Text | Google Scholar
12. Pullman SL, Goodin DS, Marquinez AI, Tabbal S, Rubin M. Clinical utility of surface EMG: report of the therapeutics and technology cess subcommittee of the American Academy of Neurology. Neurology. (2000) 55:171–7. doi: 10.1212/WNL.55.two.171
CrossRef Total Text | Google Scholar
13. Drost G, Stegeman DF, van Engelen BG, Zwarts MJ. Clinical applications of high-density surface EMG: a systematic review. J Electromyogr Kinesiol. (2006) 16:586–602. doi: 10.1016/j.jelekin.2006.09.005
PubMed Abstruse | CrossRef Full Text | Google Scholar
fourteen. Del Vecchio A, Holobar A, Falla D, Felici F, Enoka RM, Farina D. Tutorial: analysis of motor unit of measurement discharge characteristics from loftier-density surface EMG signals. J Electromyogr Kinesiol. (2020) 53:102426. doi: 10.1016/j.jelekin.2020.102426
PubMed Abstract | CrossRef Full Text | Google Scholar
15. Farina D, Holobar A, Merletti R, Enoka RM. Decoding the neural drive to muscles from the surface electromyogram. Clin Neurophysiol. (2010) 121:1616–23. doi: 10.1016/j.clinph.2009.10.040
PubMed Abstract | CrossRef Full Text | Google Scholar
17. Heald East, Hart R, Kilgore G, Peckham PH. Characterization of volitional electromyographic signals in the lower extremity later motor complete spinal cord injury. Neurorehabil Neural Repair. (2017) 31:583–91. doi: ten.1177/1545968317704904
PubMed Abstract | CrossRef Full Text | Google Scholar
18. Li Ten, Jahanmiri-Nezhad F, Rymer WZ, Zhou P. An Examination of the Motor Unit of measurement Number Index (MUNIX) in muscles paralyzed by spinal cord injury. IEEE Trans Inf Technol Biomed. (2012) 16:1143–nine. doi: 10.1109/TITB.2012.2193410
PubMed Abstract | CrossRef Full Text | Google Scholar
xix. Greenfield BH, Anderson A, Cox B, Tanner MC. Meaning of caring to seven novice physical therapists during their first year of clinical practise. Phys Ther. (2008) 88:1154–66. doi: 10.2522/ptj.20070339
PubMed Abstruse | CrossRef Full Text | Google Scholar
20. Manca A, Cereatti A, Bar-On L, Botter A, Della Croce U, Knaflitz M, et al. A survey on the utilise and barriers of surface electromyography in neurorehabilitation. Front Neurol. (2020) 11:573616. doi: ten.3389/fneur.2020.573616
PubMed Abstract | CrossRef Total Text | Google Scholar
21. Pilkar R, Ramanujam A, Nolan KJ. Alterations in spectral attributes of surface electromyograms after utilization of a foot driblet stimulator during postal service-stroke gait. Front Neurol. (2017) 8:449. doi: 10.3389/fneur.2017.00449
PubMed Abstract | CrossRef Full Text | Google Scholar
22. Pilkar R, Yarossi Chiliad, Nolan KJ. EMG of the tibialis anterior demonstrates a training issue after utilization of a foot drop stimulator. NeuroRehabilitation. (2014) 35:299–305. doi: 10.3233/NRE-141126
PubMed Abstract | CrossRef Full Text | Google Scholar
23. Pilkar R, Yarossi M, Ramanujam A, Rajagopalan V, Bayram MB, Mitchell M, et al. Application of empirical style decomposition combined with notch filtering for interpretation of surface electromyograms during functional electric stimulation. IEEE Transact Neural Syst Rehabil Eng. (2017) 25:1268–77. doi: 10.1109/TNSRE.2016.2624763
PubMed Abstract | CrossRef Total Text | Google Scholar
24. Solnik S, Passenger P, Steinweg K, DeVita P, Hortobagyi T. Teager-Kaiser free energy operator betoken workout improves EMG onset detection. Eur J Appl Physiol. (2010) 110:489–98. doi: 10.1007/s00421-010-1521-eight
CrossRef Total Text | Google Scholar
26. Calancie B, Molano MR, Broton JG. Abductor hallucis for monitoring lower-limb recovery subsequently spinal cord injury in homo. Spinal Cord. (2004) 42:573–80. doi: 10.1038/sj.sc.3101640
PubMed Abstruse | CrossRef Full Text | Google Scholar
27. Mandrile F, Farina D, Pozzo 1000, Merletti R. Stimulation artifact in surface EMG betoken: effect of the stimulation waveform, detection organisation, and electric current aamplitude using hybrid stimulation technique. IEEE Trans Neural Syst Rehabil Eng. (2003) 11:407–15. doi: 10.1109/TNSRE.2003.819791
PubMed Abstract | CrossRef Full Text | Google Scholar
28. Liu 50, Miguel Cruz A, Rios Rincon A, Buttar 5, Ranson Q, Goertzen D. What factors decide therapists' credence of new technologies for rehabilitation - a study using the Unified Theory of Acceptance and Use of Technology (UTAUT). Disabil Rehabil. (2015) 37:447–55. doi: 10.3109/09638288.2014.923529
PubMed Abstract | CrossRef Full Text | Google Scholar
30. Merletti R, Cerone GL. Tutorial. Surface EMG detection, conditioning and pre-processing: best practices. J Electromyogr Kinesiol. (2020) 54:102440. doi: 10.1016/j.jelekin.2020.102440
PubMed Abstruse | CrossRef Total Text | Google Scholar
31. Besomi One thousand, Hodges PW, Clancy EA, Van Dieën J, Hug F, Lowery G, et al. Consensus for experimental pattern in electromyography (CEDE) projection: amplitude normalization matrix. J Electromyogr Kinesiol. (2020) 53:102438. doi: 10.1016/j.jelekin.2020.102438
PubMed Abstract | CrossRef Full Text | Google Scholar
32. Besomi Chiliad, Hodges PW, Van Dieën J, Carson RG, Clancy EA, Disselhorst-Klug C, et al. Consensus for experimental design in electromyography (CEDE) project: electrode selection matrix. J Electromyogr Kinesiol. (2019) 48:128–44. doi: ten.1016/j.jelekin.2019.07.008
PubMed Abstruse | CrossRef Total Text | Google Scholar
33. Dimitrijevic MR, Faganel J, Lehmkuhl D, Sherwood A. Motor control in man afterwards partial or complete spinal cord injury. Adv Neurol. (1983) 39:915–26.
Google Scholar
34. McKay WB, Lim HK, Priebe MM, Stokic DS, Sherwood AM. Clinical neurophysiological cess of residual motor control in post-spinal cord injury paralysis. Neurorehabil Neural Repair. (2004) eighteen:144–53. doi: x.1177/0888439004267674
PubMed Abstruse | CrossRef Full Text | Google Scholar
Source: https://www.frontiersin.org/articles/10.3389/fneur.2020.578559/full