Wavelet analysis reveals differential lower limb muscle activity patterns long after anterior cruciate ligament reconstruction.
Wavelet analysis reveals differential lower limb muscle activity patterns long after anterior cruciate ligament reconstruction.
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DOI：
10.1016/j.jbiomech.2022.110957
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发表时间：
2022-03
影响因子：
2.4
通讯作者：
Beveridge JE
中科院分区：
文献类型：
--
作者：
Zandiyeh P;Parola LR;Fleming BC;Beveridge JE
本研究的目的是测试前交叉韧带重建患者（ACLR）和健康对照者之间的肌肉活动模式差异是否可以使用机器学习分类方法在术后10至15年检测到。从一项正在进行的前瞻性随机临床试验中招募了11名ACLR受试者和12名健康对照者。从腓肠肌内侧和外侧，胫骨前肌，股内侧肌，股直肌，股二头肌和半腱肌肌肉记录表面EMG信号。肌肉活动进行了分析，使用小波分析和检查内的四个子阶段的跳跃测试，以及平均的任务作为一个整体。使用K-最近邻机器学习结合留一法验证将肌肉活动模式分类为ACLR或对照。当肌肉活动在整个跳跃任务中平均时，除胫骨前肌外的所有肌肉的活动模式被确定为在研究队列之间不同。ACLR患者表现出持续的肌肉活动，跨越起飞，空中，着陆跳跃阶段与健康对照组谁显示定时和调节胰岛的肌肉活动，具体到每个跳跃阶段。最显著的特征是ACLR患者的相对股四头肌强度增加25 - 50%，股二头肌强度降低约66%。目前的研究结果与以前使用相同数据集的传统共收缩和肌肉激活发作EMG测量的工作相反，强调了小波方法与机器学习相结合的敏感性和潜力，以揭示这一高危人群中有意义的适应策略。
The purpose of this study was to test whether differences in muscle activity patterns between anterior cruciate ligament-reconstructed patients (ACLR) and healthy controls could be detected 10 to 15 years post-surgery using a machine learning classification approach. Eleven ACLR subjects and 12 healthy controls were recruited from an ongoing prospective randomized clinical trial. Surface EMG signals were recorded from gastrocnemius medialis and lateralis, tibialis anterior, vastus medialis, rectus femoris, biceps femoris, and semitendinosus muscles. Muscle activity was analyzed using wavelet analysis and examined within four sub-phases of the hop test, as well as an average of the task as a whole. K-nearest neighbor machine learning combined with a leave-one-out validation was used to classify the muscle activity patterns as either ACLR or Control. When muscle activity was averaged across the whole hop task, activity patterns for all muscles except the tibialis anterior were identified as being different between the study cohorts. ACLR patients demonstrated continuous muscle activities that spanned take-off, airborne, and landing hop phases versus healthy controls who displayed timed and regulated islets of muscle activities specific to each hop phase. The most striking features were 25-50% greater relative quadriceps intensity and approximately 66% diminished biceps femoris intensity in ACLR patients. The current findings are in contrast to previous work using conventional co-contraction and muscle activation onset EMG measures of the same dataset, underscoring the sensitivity and potential of the wavelet approach coupled with machine learning to reveal meaningful adaptation strategies in this at-risk population.
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影响因子：
2.8
作者：
Coats-Thomas, Margaret S.;Miranda, Daniel L.;Fleming, Braden C.
通讯作者：
Fleming, Braden C.
影响因子：
2.5
作者：
Kuntze, G.;von Tscharner, V.;Ronsky, J. L.
通讯作者：
Ronsky, J. L.
影响因子：
2.5
作者：
Conforto, S;D'Alessio, T;Pignatelli, S
通讯作者：
Pignatelli, S
DOI：
10.1002/jor.24794
发表时间：
2021-05
期刊：
Journal of orthopaedic research : official publication of the Orthopaedic Research Society
影响因子：
--
作者：
Fleming BC;Fadale PD;Hulstyn MJ;Shalvoy RM;Tung GA;Badger GJ
通讯作者：
Badger GJ
影响因子：
5.3
作者：
Chalmers, Peter N.;Mall, Nathan A.;Bach, Bernard R., Jr.
通讯作者：
Bach, Bernard R., Jr.