A diagnostic model of nerve root compression localization in lower lumbar disc herniation based on random forest algorithm and surface electromyography

This study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and establish a diagnostic model based on SEMG parameters using random forest (RF) algorithm for localization diagnosis of compressed nerve root in LD...

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Vydáno v:	Frontiers in human neuroscience Ročník 17; s. 1176001
Hlavní autoři:	Wang, Hujun, Wang, Yingpeng, Li, Yingqi, Wang, Congxiao, Qie, Shuyan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland Frontiers Research Foundation 04.07.2023 Frontiers Media S.A
Témata:	Algorithms Cerebral palsy Compression Contraindications Data processing Diagnosis diagnosis model Disease Electromyography Gait Intervertebral discs Localization lumbar disc herniation Machine learning Magnetic resonance imaging Muscle contraction nerve root compression Neuroscience Patients random forest Rehabilitation Software surface electromyography Surgery Beijing China United States > US China random forest diagnosis model surface electromyography nerve root compression lumbar disc herniation
ISSN:	1662-5161, 1662-5161
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Abstract	This study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and establish a diagnostic model based on SEMG parameters using random forest (RF) algorithm for localization diagnosis of compressed nerve root in LDH patients. Fifty-eight patients with LDH and thirty healthy subjects were recruited. The SEMG of tibialis anterior (TA) and lateral gastrocnemius (LG) were collected bilaterally during walking. The peak root mean square (RMS-peak), RMS-peak time, mean power frequency (MPF), and median frequency (MF) were analyzed. A diagnostic model based on SEMG parameters using RF algorithm was established to locate compressed nerve root, and repeated reservation experiments were conducted for verification. The study evaluated the diagnostic efficiency of the model using accuracy, precision, recall rate, F1-score, Kappa value, and area under the receiver operating characteristic (ROC) curve. The results showed that delayed activation of TA and decreased activation of LG were observed in the L5 group, while decreased activation of LG and earlier activation of LG were observed in the S1 group. The RF model based on eight SEMG parameters showed an average accuracy of 84%, with an area under the ROC curve of 0.93. The RMS peak time of TA was identified as the most important SEMG parameter. These findings suggest that the RF model can assist in the localization diagnosis of compressed nerve roots in LDH patients, and the SEMG parameters can provide further references for optimizing the diagnosis model in the future.
AbstractList	This study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and establish a diagnostic model based on SEMG parameters using random forest (RF) algorithm for localization diagnosis of compressed nerve root in LDH patients.ObjectiveThis study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and establish a diagnostic model based on SEMG parameters using random forest (RF) algorithm for localization diagnosis of compressed nerve root in LDH patients.Fifty-eight patients with LDH and thirty healthy subjects were recruited. The SEMG of tibialis anterior (TA) and lateral gastrocnemius (LG) were collected bilaterally during walking. The peak root mean square (RMS-peak), RMS-peak time, mean power frequency (MPF), and median frequency (MF) were analyzed. A diagnostic model based on SEMG parameters using RF algorithm was established to locate compressed nerve root, and repeated reservation experiments were conducted for verification. The study evaluated the diagnostic efficiency of the model using accuracy, precision, recall rate, F1-score, Kappa value, and area under the receiver operating characteristic (ROC) curve.MethodsFifty-eight patients with LDH and thirty healthy subjects were recruited. The SEMG of tibialis anterior (TA) and lateral gastrocnemius (LG) were collected bilaterally during walking. The peak root mean square (RMS-peak), RMS-peak time, mean power frequency (MPF), and median frequency (MF) were analyzed. A diagnostic model based on SEMG parameters using RF algorithm was established to locate compressed nerve root, and repeated reservation experiments were conducted for verification. The study evaluated the diagnostic efficiency of the model using accuracy, precision, recall rate, F1-score, Kappa value, and area under the receiver operating characteristic (ROC) curve.The results showed that delayed activation of TA and decreased activation of LG were observed in the L5 group, while decreased activation of LG and earlier activation of LG were observed in the S1 group. The RF model based on eight SEMG parameters showed an average accuracy of 84%, with an area under the ROC curve of 0.93. The RMS peak time of TA was identified as the most important SEMG parameter.ResultsThe results showed that delayed activation of TA and decreased activation of LG were observed in the L5 group, while decreased activation of LG and earlier activation of LG were observed in the S1 group. The RF model based on eight SEMG parameters showed an average accuracy of 84%, with an area under the ROC curve of 0.93. The RMS peak time of TA was identified as the most important SEMG parameter.These findings suggest that the RF model can assist in the localization diagnosis of compressed nerve roots in LDH patients, and the SEMG parameters can provide further references for optimizing the diagnosis model in the future.ConclusionThese findings suggest that the RF model can assist in the localization diagnosis of compressed nerve roots in LDH patients, and the SEMG parameters can provide further references for optimizing the diagnosis model in the future. This study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and establish a diagnostic model based on SEMG parameters using random forest (RF) algorithm for localization diagnosis of compressed nerve root in LDH patients. Fifty-eight patients with LDH and thirty healthy subjects were recruited. The SEMG of tibialis anterior (TA) and lateral gastrocnemius (LG) were collected bilaterally during walking. The peak root mean square (RMS-peak), RMS-peak time, mean power frequency (MPF), and median frequency (MF) were analyzed. A diagnostic model based on SEMG parameters using RF algorithm was established to locate compressed nerve root, and repeated reservation experiments were conducted for verification. The study evaluated the diagnostic efficiency of the model using accuracy, precision, recall rate, F1-score, Kappa value, and area under the receiver operating characteristic (ROC) curve. The results showed that delayed activation of TA and decreased activation of LG were observed in the L5 group, while decreased activation of LG and earlier activation of LG were observed in the S1 group. The RF model based on eight SEMG parameters showed an average accuracy of 84%, with an area under the ROC curve of 0.93. The RMS peak time of TA was identified as the most important SEMG parameter. These findings suggest that the RF model can assist in the localization diagnosis of compressed nerve roots in LDH patients, and the SEMG parameters can provide further references for optimizing the diagnosis model in the future. Objective: This study aimed to investigate the muscle activation of patients with Lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and establish a diagnostic model based on SEMG parameters using Random Forest (RF) algorithm for localization diagnosis of compressed nerve root in LDH patients.Methods: Fifty-eight patients with LDH and thirty healthy subjects were recruited. The SEMG of Tibialis Anterior (TA) and lateral gastrocnemius (LG) were collected bilaterally during walking. The peak root mean square (RMS-peak), RMS-peak time, mean power frequency (MPF), and mean frequency (MF) were analyzed. A diagnostic model based on SEMG parameters using RF algorithm was established to locate compressed nerve root, and repeated reservation experiments were conducted for verification. The study evaluated the diagnostic efficiency of the model using accuracy, precision, recall rate, F1-score, Kappa value, and area under the ROC curve.The results showed that delayed activation of TA and decreased activation ofLG were observed in the L5 group, while decreased activation of LG and earlier activation of LG were observed in the S1 group. The RF model based on eight SEMG parameters showed an average accuracy of 84%, with an area under the ROC curve of 0.93. The RMS peak time of TA was identified as the most important SEMG parameter.: These findings suggest that the RF model can assist in the localization diagnosis of compressed nerve roots in LDH patients, and the SEMG parameters can provide further references for optimizing the diagnosis model in the future. ObjectiveThis study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and establish a diagnostic model based on SEMG parameters using random forest (RF) algorithm for localization diagnosis of compressed nerve root in LDH patients.MethodsFifty-eight patients with LDH and thirty healthy subjects were recruited. The SEMG of tibialis anterior (TA) and lateral gastrocnemius (LG) were collected bilaterally during walking. The peak root mean square (RMS-peak), RMS-peak time, mean power frequency (MPF), and median frequency (MF) were analyzed. A diagnostic model based on SEMG parameters using RF algorithm was established to locate compressed nerve root, and repeated reservation experiments were conducted for verification. The study evaluated the diagnostic efficiency of the model using accuracy, precision, recall rate, F1-score, Kappa value, and area under the receiver operating characteristic (ROC) curve.ResultsThe results showed that delayed activation of TA and decreased activation of LG were observed in the L5 group, while decreased activation of LG and earlier activation of LG were observed in the S1 group. The RF model based on eight SEMG parameters showed an average accuracy of 84%, with an area under the ROC curve of 0.93. The RMS peak time of TA was identified as the most important SEMG parameter.ConclusionThese findings suggest that the RF model can assist in the localization diagnosis of compressed nerve roots in LDH patients, and the SEMG parameters can provide further references for optimizing the diagnosis model in the future.
Author	Wang, Yingpeng Wang, Congxiao Li, Yingqi Qie, Shuyan Wang, Hujun
AuthorAffiliation	Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University , Beijing , China
AuthorAffiliation_xml	– name: Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University , Beijing , China
Author_xml	– sequence: 1 givenname: Hujun surname: Wang fullname: Wang, Hujun – sequence: 2 givenname: Yingpeng surname: Wang fullname: Wang, Yingpeng – sequence: 3 givenname: Yingqi surname: Li fullname: Li, Yingqi – sequence: 4 givenname: Congxiao surname: Wang fullname: Wang, Congxiao – sequence: 5 givenname: Shuyan surname: Qie fullname: Qie, Shuyan
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37469999$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1111/os.12362 10.1007/s11999-014-3674-y 10.1016/0022-510x(89)90237-2 10.1016/j.spinee.2013.02.007 10.1023/A:1010933404324 10.1016/j.jelekin.2013.06.006 10.1038/s41598-020-77150-7 10.21037/jss.2016.01.05 10.1097/BRS.0b013e3181b1c99f 10.3390/s140508235 10.1016/j.pmr.2012.08.011 10.3174/ajnr.A4498 10.1016/j.gaitpost.2017.01.010 10.1109/TBME.2006.873680 10.1016/j.jelekin.2008.09.002 10.1186/s12938-018-0443-2 10.1016/j.clineuro.2013.11.018 10.1007/s00586-015-4375-2 10.3233/BMR-181308 10.1097/00005373-196103000-00008 10.3174/ajnr.A4173 10.1016/j.jelekin.2007.09.006 10.2340/16501977-1034 10.1097/AJP.0000000000000179 10.1097/MD.0000000000017865 10.1016/j.clinbiomech.2008.08.006 10.1093/fampra/cmv097 10.19650/j.cnki.cjsi.J1905757 10.1007/s00586-011-2109-7
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Keywords	random forest diagnosis model surface electromyography nerve root compression lumbar disc herniation
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors have contributed equally to this work Edited by: Redha Taiar, Université de Reims Champagne-Ardenne, France Reviewed by: Sherwan Hamawandi, Hawler Medical University, Iraq; Anastasia Keller, University of California, San Francisco, United States; Yutong Gu, Fudan University, China
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Snippet	This study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and... Objective: This study aimed to investigate the muscle activation of patients with Lumbar disc herniation (LDH) during walking by surface electromyography... ObjectiveThis study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG)...
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StartPage	1176001
SubjectTerms	Algorithms Cerebral palsy Compression Contraindications Data processing Diagnosis diagnosis model Disease Electromyography Gait Intervertebral discs Localization lumbar disc herniation Machine learning Magnetic resonance imaging Muscle contraction nerve root compression Neuroscience Patients random forest Rehabilitation Software surface electromyography Surgery
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Title	A diagnostic model of nerve root compression localization in lower lumbar disc herniation based on random forest algorithm and surface electromyography
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Volume	17
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