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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Veröffentlicht in:Frontiers in human neuroscience Jg. 17; S. 1176001
Hauptverfasser: Wang, Hujun, Wang, Yingpeng, Li, Yingqi, Wang, Congxiao, Qie, Shuyan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Research Foundation 04.07.2023
Frontiers Media S.A
Schlagworte:
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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Zusammenfassung: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.
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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
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2023.1176001