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Muscle disease monitoring method based on sEMG

A muscle disease and muscle state technology, applied in the field of muscle disease monitoring, can solve problems such as slow progress

Active Publication Date: 2016-02-17
NINGBO UNIVERSITY OF TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Therefore, the results of drug research are progressing very slowly

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  • Muscle disease monitoring method based on sEMG
  • Muscle disease monitoring method based on sEMG
  • Muscle disease monitoring method based on sEMG

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Embodiment Construction

[0023] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily realize the contents disclosed in this specification.

[0024] A method for monitoring muscle disease based on multi-channel sEMG, comprising the steps of:

[0025] Step 1) Preprocess the multi-channel sEMG signal, the specific steps are as follows: a band-pass filter is used to retain the 5Hz--500Hz frequency band signal, and then a notch filter is used to filter out 50Hz power frequency interference. The band-pass filter and band-stop filter adopt Butterworth digital filter, and the setting parameters include order, filter pass band, stop band cut-off frequency, 3dB cut-off frequency, etc.

[0026] Step 2) Take the first channel as a reference value, and differentiate the signals of other channels from the first channel to obtain a new multi-channel signal. Use the signal of channel 1 as a reference value to get a new channel sEMG signal, , ...

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Abstract

The invention aims at providing a muscle disease monitoring method based on multi-channel surface electromyography (sEMG). The method comprises the steps that preprocessing is conducted on multi-channel sEMG signals firstly, a first channel is selected as a reference, and differencing is conducted on the signals of other channels and the signal of the first channel; then distribution moments are extracted through a K-means clustering convolution kernel compensation (KMCKC) method, and a single waveform is extracted; finally, multiple features of the waveform are fused, and the muscle state is evaluated. Due to the fact that external interference has influence on all electrodes, by meas of differencing between the signal of the first channel and the sEMG signals of original multiple channels, the external interference is effectively reduced, subsequent detection results cannot be affected, and the accuracy of muscle detection is improved. By means of application of the multi-feature parameters, the instability of single-parameter monitoring is effectively avoided, and the monitoring robustness is promoted. By means of the muscle disease monitoring method based on the multi-channel surface electromyography (sEMG), the defects in the prior art are effectively overcome, and important application value is achieved.

Description

technical field [0001] The invention relates to a method for monitoring muscle diseases based on multi-channel sEMG. Background technique [0002] With the development of our society, people pay more and more attention to health, and the diagnosis or monitoring of muscle diseases has become the research focus in recent years. Common neurodegenerative muscle diseases include Alzheimer's, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. Taking amyotrophic lateral sclerosis (ALS) as an example, ALS is a neurodegenerative disease characterized by progressive destruction of motor neurons in the motor cortex, brainstem and spinal cord, leading to muscle atrophy, paralysis and death in patients. The annual incidence rate of the general population is about (2-3) / 100000, and the prevalence area is about (4-6) / 100000. In the United States and the United Kingdom, ALS accounts for 1 / 500-1 / 1000 adult deaths [3], which also means that in the United States al...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0488
CPCA61B5/7221A61B5/7225A61B5/725A61B5/389
Inventor 何金保骆再飞胡劲松
Owner NINGBO UNIVERSITY OF TECHNOLOGY
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