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

Frontiers in Human Neuroscience

Neuroscience

Hujun Wang¹  Yingpeng Wang¹  Yingqi Li¹  Shuyan Qie²  Congxiao Wang² 

[1] Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China;null;

关键词: lumbar disc herniation;    surface electromyography;    random forest;    diagnosis model;    nerve root compression;   

DOI  :  10.3389/fnhum.2023.1176001

 received in 2023-04-23, accepted in 2023-06-19,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Wang, Li, Wang and Qie.