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. 2019 Sep 26:1:23.
doi: 10.1186/s42490-019-0023-y. eCollection 2019.

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

Mingjia Du  1 , Baohua Hu  1 , Feiyun Xiao  1 , Ming Wu  2 , Zongjun Zhu  3 , Yong Wang  1
Affiliations

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

Mingjia Du et al. BMC Biomed Eng. .

Abstract

Background: Accurate 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.

Results: The 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%.

Conclusions: The 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.

Keywords: Empirical mode decomposition; Modified sample entropy; Spasticity; Stretch reflex onset; Surface electromyography.

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Conflict of interest statement

Competing interestsThe authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The modified sample entropy under noise (SNR = 20 dB) environment
Fig. 2
Fig. 2
The modified sample entropy after denoising
Fig. 3
Fig. 3
The evaluation process of Spasticity
See this image and copyright information in PMC

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