An Adaptive Filtering Method for Surface EMG Signals Based on Source Number Estimation

A technology of adaptive filtering and electromyographic signal, applied in the direction of adaptive network, electrical components, impedance network, etc.

Active Publication Date: 2017-11-17
四川华信智创科技有限公司
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  • Abstract
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Problems solved by technology

First, the ensemble empirical mode decomposition method is used to adaptively decompose the surface electromyography signal into multiple intrinsic mode function components, which solves the mode aliasing problem of the traditional empirical mode decomposition method and improves the decomposition effect

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  • An Adaptive Filtering Method for Surface EMG Signals Based on Source Number Estimation
  • An Adaptive Filtering Method for Surface EMG Signals Based on Source Number Estimation
  • An Adaptive Filtering Method for Surface EMG Signals Based on Source Number Estimation

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Embodiment Construction

[0017] Mark the surface electromyography signal collected under the continuous steady-state constant force contraction of the muscle as x(t), and use it as the signal object to be analyzed, and perform the collective empirical mode decomposition processing. The specific steps are as follows:

[0018] The first step is to count the standard deviation of the original surface EMG signal x(t) and mark it as SD, add α×SD additive random white noise to x(t), and mark the obtained new time series as

[0019] In the second step, the new sequence Perform empirical mode decomposition to obtain the intrinsic mode function components of the noised sequence;

[0020] In the third step, repeat the previous two steps m times to obtain a new time series after adding different white noises And m intrinsic modal function component sets can be obtained;

[0021] In the fourth step, the obtained intrinsic mode function component set is averaged to obtain the final intrinsic mode function co...

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Abstract

The invention discloses a source number estimation-based surface electromyogram signal adaptive filtering method. The method directs at surface electromyogram signals collected under continuous steady state constant force contraction of muscle, adopts an ensemble empirical mode decomposition method to adaptively decompose the surface electromyogram signals into a plurality of internal modal function components. A phase space singular value decomposition method uses screened internal modal function components as objects, and obtains phase space singular characteristic value distribution information thereof. An energy percentage maximum different optimization algorithm is designed to realize source number estimation of the internal modal function components, thereby realizing phase space singular matrix reconstruction. Finally signal superposition reconstruction is performed on the plurality of internal modal function components that are processed, thereby obtaining a high-quality filtering de-noising result of the surface electromyogram signals.

Description

technical field [0001] The invention belongs to the technical field of weak signal filtering and noise reduction, and relates to an adaptive filtering method for surface electromyographic signals based on source number estimation. Background technique [0002] Surface electromyography is a complex nonlinear and non-stationary electrophysiological signal generated on the surface of human skin during the activity of the neuromuscular system. Multiple motor neurons are stimulated to form a motor unit action potential sequence, and the comprehensive superposition result of these sequences at the measuring electrode after passing through the volume conductor composed of muscle, fat, skin and other tissues is the surface electromyographic signal. [0003] Since the surface electromyography signal is the direct output result of the activity of the neuromuscular system, the analysis of the surface electromyography signal can excavate the functional characteristics of the neuromuscul...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03H21/00
Inventor 李强秦明伟孙飞吴亚婷
Owner 四川华信智创科技有限公司
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