A Machine Learning-based Surface Electromyography Topography Evaluation for Prognostic Prediction of Functional Restoration Rehabilitation in Chronic Low Back Pain
A Machine Learning-based Surface Electromyography Topography Evaluation for Prognostic Prediction of Functional Restoration Rehabilitation in Chronic Low Back Pain
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DOI：
10.1097/brs.0000000000002159
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发表时间：
2017-11-01
期刊：
影响因子：
3
通讯作者：
Hu, Yong
中科院分区：
文献类型：
--
作者：
Jiang, Naifu;Luk, Keith Dip-Kei;Hu, Yong
研究设计。回顾性研究。目的：本研究的目的是探讨支持向量机（SVM）算法对LBP患者在功能恢复康复方案后取得满意或不满意进展进行分类的可行性和适用性。背景资料摘要。动态表面肌电图（SEMG）在预测下腰痛（LBP）患者功能恢复康复的预后方面具有潜在的应用价值。然而，对临床医生来说，从原始的表面肌电信号形貌进行处理来进行预测并不容易。招募30例非特异性LBP患者，根据视觉模拟疼痛评定量表和Oswestry残疾指数的变化分为有反应组和无反应组。每位患者接受为期12周的功能恢复康复计划。从48名健康参与者的对照组中计算出正常数据库。从躯干对称和不对称运动时记录的动态表面肌电信号形貌中提取均方根差（RMSD）。基于顺序浮动前向选择（SFFS）算法选择的优化特征，将支持向量机和交叉验证应用于预测。“应答”组康复后RMSD特征参数差异有统计学意义(P
Study Design. A retrospective study.Objective. The aim of this study was to investigate the feasibility and applicability of support vector machine (SVM) algorithm in classifying patients with LBP who would obtain satisfactory or unsatisfactory progress after the functional restoration rehabilitation program.Summary of Background Data. Dynamic surface electromyography (SEMG) topography has demonstrated the potential use in predicting the prognosis of functional restoration rehabilitation for patients with low back pain (LBP). However, processing from raw SEMG topography to make prediction is not easy to clinicians.Methods. A total of 30 patients with nonspecific LBP were recruited and divided into "responding'' and "non-responding'' group according to the change of Visual analog pain rating scale and Oswestry Disability Index. Each patient received a 12-week functional restoration rehabilitation program. A normal database was calculated from a control group from 48 healthy participants. Root-mean-square difference (RMSD) was extracted from the recorded dynamic SEMG topography during symmetrical and asymmetrical trunk-movement. SVM and cross-validation were applied to the prediction based on the optimized features selected by the sequential floating forward selection (SFFS) algorithm.Results. RMSD feature parameters following rehabilitation in the "responding'' group showed a significant difference (P