Guijin Li, G. Balbinot, Julio C Furlan, Sukhvinder Kalsi-Ryan, J. Zariffa
{"title":"海报(技术创新) ID 1984794","authors":"Guijin Li, G. Balbinot, Julio C Furlan, Sukhvinder Kalsi-Ryan, J. Zariffa","doi":"10.46292/sci23-1984794s","DOIUrl":null,"url":null,"abstract":"Cervical spinal cord injury (SCI) can cause significant impairment and disability with an impact on individuals’ quality of life and independence. Surface electromyography (SEMG) is a sensitive and non-invasive technique to measure muscle activity and has demonstrated great potential in capturing the impact from SCI. The mechanisms of SCI damage on SEMG signal characteristics are multi-faceted and difficult to study in vivo. Use validated computational models to characterize changes in SEMG signal after SCI and identify SEMG features that are sensitive and specific to the impact from different aspects of SCI. Starting from existing computational models for motor neuron pool organization and for motor unit action potential generation for healthy neuromuscular systems, we set up scenarios to model alterations in upper motor neurons, lower motor neurons, and the number of muscle fibers within each motor unit after SCI. After simulating SEMG signals from each scenario, we extracted time and frequency domain features and investigated the impact of SCI disruptions on SEMG features using the Pearson correlation between a feature and the extent of a given disruption. Commonly used amplitude-based SEMG features cannot differentiate between injury scenarios. A broader set of features provides greater specificity to the type of damage present. We demonstrated a novel approach to mechanistically relate SEMG features to different types of neuromuscular alterations after SCI. This work contributes to a deeper understanding and exploitation of SEMG in clinical applications, which will ultimately improve patient outcomes after SCI.","PeriodicalId":46769,"journal":{"name":"Topics in Spinal Cord Injury Rehabilitation","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Poster (Technology Innovation) ID 1984794\",\"authors\":\"Guijin Li, G. Balbinot, Julio C Furlan, Sukhvinder Kalsi-Ryan, J. Zariffa\",\"doi\":\"10.46292/sci23-1984794s\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cervical spinal cord injury (SCI) can cause significant impairment and disability with an impact on individuals’ quality of life and independence. Surface electromyography (SEMG) is a sensitive and non-invasive technique to measure muscle activity and has demonstrated great potential in capturing the impact from SCI. The mechanisms of SCI damage on SEMG signal characteristics are multi-faceted and difficult to study in vivo. Use validated computational models to characterize changes in SEMG signal after SCI and identify SEMG features that are sensitive and specific to the impact from different aspects of SCI. Starting from existing computational models for motor neuron pool organization and for motor unit action potential generation for healthy neuromuscular systems, we set up scenarios to model alterations in upper motor neurons, lower motor neurons, and the number of muscle fibers within each motor unit after SCI. After simulating SEMG signals from each scenario, we extracted time and frequency domain features and investigated the impact of SCI disruptions on SEMG features using the Pearson correlation between a feature and the extent of a given disruption. Commonly used amplitude-based SEMG features cannot differentiate between injury scenarios. A broader set of features provides greater specificity to the type of damage present. We demonstrated a novel approach to mechanistically relate SEMG features to different types of neuromuscular alterations after SCI. This work contributes to a deeper understanding and exploitation of SEMG in clinical applications, which will ultimately improve patient outcomes after SCI.\",\"PeriodicalId\":46769,\"journal\":{\"name\":\"Topics in Spinal Cord Injury Rehabilitation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Topics in Spinal Cord Injury Rehabilitation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46292/sci23-1984794s\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"REHABILITATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Topics in Spinal Cord Injury Rehabilitation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46292/sci23-1984794s","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REHABILITATION","Score":null,"Total":0}
Cervical spinal cord injury (SCI) can cause significant impairment and disability with an impact on individuals’ quality of life and independence. Surface electromyography (SEMG) is a sensitive and non-invasive technique to measure muscle activity and has demonstrated great potential in capturing the impact from SCI. The mechanisms of SCI damage on SEMG signal characteristics are multi-faceted and difficult to study in vivo. Use validated computational models to characterize changes in SEMG signal after SCI and identify SEMG features that are sensitive and specific to the impact from different aspects of SCI. Starting from existing computational models for motor neuron pool organization and for motor unit action potential generation for healthy neuromuscular systems, we set up scenarios to model alterations in upper motor neurons, lower motor neurons, and the number of muscle fibers within each motor unit after SCI. After simulating SEMG signals from each scenario, we extracted time and frequency domain features and investigated the impact of SCI disruptions on SEMG features using the Pearson correlation between a feature and the extent of a given disruption. Commonly used amplitude-based SEMG features cannot differentiate between injury scenarios. A broader set of features provides greater specificity to the type of damage present. We demonstrated a novel approach to mechanistically relate SEMG features to different types of neuromuscular alterations after SCI. This work contributes to a deeper understanding and exploitation of SEMG in clinical applications, which will ultimately improve patient outcomes after SCI.
期刊介绍:
Now in our 22nd year as the leading interdisciplinary journal of SCI rehabilitation techniques and care. TSCIR is peer-reviewed, practical, and features one key topic per issue. Published topics include: mobility, sexuality, genitourinary, functional assessment, skin care, psychosocial, high tetraplegia, physical activity, pediatric, FES, sci/tbi, electronic medicine, orthotics, secondary conditions, research, aging, legal issues, women & sci, pain, environmental effects, life care planning