Electrophysiological Study in Acute Spinal Cord Injury Patients: Its Correlation to Neurological Deficit and Subsequent Recovery Assessment by ASIA Score

Singh, Roop; Wadhwani, Jitendra; Meena, Vijay Singh; Sharma, Pankaj; Kaur, Kiranpreet; Svareen,

doi:10.1007/s43465-020-00108-4

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…The analysis of the coherence of the muscles was performed in various frequency ranges within the 2–60 Hz band. The choice of these bands was based on the standard frequency band 15–30 Hz and the study results that demonstrated the physiological importance of low frequencies and high frequency consistency [ 9 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, as emphasized by numerous authors, the approach to the use of ASIS is subjective, and the scale does not measure the activity of the trunk muscles [ 6 – 8 ]. Significant recovery can be achieved without an improvement in the ASIS grade, therefore prospective studies should incorporate more detailed neurological outcomes to prevent any potential underestimation of neurological recovery associated with the exclusive use of the ASIS scale [ 5 , 6 , 9 ]. Previous research has shown that injured people do not have effective control over muscle activation below the level of injury, and can acquire a new pattern of motor control aimed at maintaining posture.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet coherence as a measure of trunk stabilizer muscle activation in wheelchair fencers

Błaszczyszyn

Borysiuk

Piechota

et al. 2021

BMC Sports Sci Med Rehabil

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Background Intermuscular synchronization constitutes one of the key aspects of effective sport performance and activities of daily living. The aim of the study was to assess the synchronization of trunk stabilizer muscles in wheelchair fencers with the use of wavelet analysis. Methods Intermuscular synchronization and antagonistic EMG–EMG coherence were evaluated in the pairs of the right and the left latissimus dorsi/external oblique abdominal (LD/EOA) muscles. The study group consisted of 16 wheelchair fencers, members of the Polish Paralympic Team, divided into two categories of disability (A and B). Data analysis was carried out in three stages: (1) muscle activation recording using sEMG; (2) wavelet coherence analysis; and (3) coherence density analysis. Results In the Paralympic wheelchair fencers, regardless of their disability category, the muscles were activated at low frequency levels: 8–20 Hz for category A fencers, and 5–15 Hz for category B fencers. Conclusions The results demonstrated a clear activity of the trunk muscles in the wheelchair fencers, including those with spinal cord injury, which can be explained as an outcome of their intense training. EMG signal processing application have great potential for performance improvement and diagnosis of wheelchair athletes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wavelet coherence as a measure of trunk stabilizer muscle activation in wheelchair fencers

Błaszczyszyn

Borysiuk

Piechota

et al. 2021

BMC Sports Sci Med Rehabil

View full text Add to dashboard Cite

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Intelligent Integration of Assessment Tools for Specialized Prognosis in Spinal Cord Injuries: A scoping review

Chrysanthakopoulou,

Koutsojannis,

Matzaroglou

et al. 2023

Preprint

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Spinal cord injury is a life-threatening condition resulting from spinal cord trauma, leading to paralysis, loss of sensation, bowel and bladder control. Accurate assessment tools are crucial for diagnosing and treating spinal cord injuries, and various scales have been developed for this purpose. Additionally, electrophysiological measures, including somatosensory evoked potentials, motor evoked potentials, and nerve conduction studies, can aid in patient stratification. Recent developments in spinal cord injury assessment have shown promise, particularly with the use of advanced imaging techniques and artificial intelligence. Neuroimaging and molecular biomarkers combined with electrophysiological measures, promise to predict outcomes and guide treatment decisions. Machine learning and Artificial intelligence have revolutionized the healthcare industry, including the field of spinal cord injuries, as they can facilitate personalized medicine by accurately predicting. Challenges remain in validating machine learning models and ensuring they are safe and effective for clinical use. Quality data and expertise are crucial for accurately interpreting and applying machine learning results in spinal cord injury management. Moreover, due to artificial intelligence entering healthcare to assist in processing data, electrophysiology can eventually meet the high-quality information it can provide, as it is easier to analyze data recordings from somatosensory evoked potentials and other electrophysiologic measures. Summing up, the integration of advanced imaging techniques, biomarkers, and machine learning leading to maximizing the use and importance of electrophysiology as far as the information it can reveal, has the potential to revolutionize the diagnosis, prognosis, and treatment of spinal cord injuries, leading to improved patient outcomes and personalized care.

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Electrophysiological Study in Acute Spinal Cord Injury Patients: Its Correlation to Neurological Deficit and Subsequent Recovery Assessment by ASIA Score

Cited by 2 publications

References 16 publications

Wavelet coherence as a measure of trunk stabilizer muscle activation in wheelchair fencers

Wavelet coherence as a measure of trunk stabilizer muscle activation in wheelchair fencers

Intelligent Integration of Assessment Tools for Specialized Prognosis in Spinal Cord Injuries: A scoping review

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