Quantifying Spasticity With Limited Swinging Cycles Using Pendulum Test Based on Phase Amplitude Coupling

Chien Hung Yeh, Hsu Wen Vincent Young, Cheng Yen Wang, Yung Hung Wang, Po Lei Lee, Jiunn-Horng Kang, Men Tzung Lo

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Parameters derived from the goniometer measures in the Pendulum test are insufficient in describing the function of abnormal muscle activity in the spasticity. To explore a quantitative evaluation of muscle activation-movement interaction, we propose a novel index based on phase amplitude coupling (PAC) analysis with the consideration of the relations between movement and surface electromyography (SEMG) activity among 22 hemiplegic stroke patients. To take off trend and noise, we use the empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMFs) of the angular velocity due to its superior decomposing ability in nonlinear oscillations. Shannon entropy based on angular velocity (phase)-envelope of EMG (amplitude) distribution was calculated to demonstrate characteristics of the coupling between SEMG activity and joint movement. We also compare our results with those from traditional methods such as the normalized relaxation index derived from the Pendulum test and the mean root mean square (RMS) of the SEMG signals in the study. Our results show effective discrimination ability between spastic and nonaffected limbs using our method p < 0.0001. This study indicates the feasibility of using the novel indices based on the PAC in evaluation the spasticity among the hemiplegic stroke patients with less than three swinging cycles.

Original languageEnglish
Article number7393838
Pages (from-to)1081-1088
Number of pages8
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume24
Issue number10
DOIs
Publication statusPublished - Oct 1 2016

Keywords

  • Empirical mode decomposition (EMD)
  • modulation index
  • muscle strength level
  • surface electromyography (SEMG)

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biomedical Engineering
  • Computer Science Applications

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