Evidence map›Paper›PMID 32903351›Full record

ArticleBMC biomedical engineering2019

Detection of stretch reflex onset based on empirical mode decomposition and modified sample entropy.

Mingjia Du, Baohua Hu, Feiyun Xiao, Ming Wu, Zongjun Zhu, Yong Wang

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Article in BMC biomedical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Mingjia DuSchool of Mechanical Engineering, Hefei University of Technology, No. 193 Tunxi Road, Hefei, 230009 China.
Baohua HuSchool of Mechanical Engineering, Hefei University of Technology, No. 193 Tunxi Road, Hefei, 230009 China.
Feiyun XiaoSchool of Mechanical Engineering, Hefei University of Technology, No. 193 Tunxi Road, Hefei, 230009 China.
Ming WuDepartment of Rehabilitation Medicine, Anhui Provincial Hospital, No. 1 Swan Lake Road, Hefei, 230001 China.
Zongjun ZhuAcupuncture and Rehabilitation Department, The First Affiliated Hospital of Anhui University of Chinese Medicine, No. 117 Meishan Road, Hefei, 230031 China.
Yong WangSchool of Mechanical Engineering, Hefei University of Technology, No. 193 Tunxi Road, Hefei, 230009 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurate spasticity assessment provides an objective evaluation index for the rehabilitation treatment of patients with spasticity, and the key is detecting stretch reflex onset. The surface electromyogram of patients with spasticity is prone to false peaks, and its data length is unstable. These conditions decrease signal differences before and after stretch reflex onset. Therefore, a method for detecting stretch reflex onset based on empirical mode decomposition denoising and modified sample entropy recognition is proposed in this study.

resultsThe empirical mode decomposition algorithm is better than the wavelet threshold algorithm in denoising surface electromyogram signal. Without adding Gaussian white noise to the electromyogram signal, the stretch reflex onset recognition rate of the electromyogram signal before and after empirical mode decomposition denoising was increased by 56%. In particular, the recognition rate of stretch reflex onset under the optimal parameter of the modified sample entropy can reach up to 100% and the average recognition rate is 93%.

conclusionsThe empirical mode decomposition algorithm can eliminate the baseline activity of the surface electromyogram signal before stretch reflex onset and effectively remove noise from the signal. The identification of stretch reflex onset using combined empirical mode decomposition and modified sample entropy is better than that via modified sample entropy alone, and stretch reflex onset can be accurately determined.

Indexed as

Empirical mode decompositionModified sample entropySpasticityStretch reflex onsetSurface electromyography

Identifiers

PMID32903351
PMCPMC7421583

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