Automatic decomposition method of array type sEMG (surface EMG) signal

Abstract

The invention provides an automatic decomposition method of an array type sEMG (surface EMG) signal. The automatic decomposition method comprises the following steps: firstly, pretreating the array type sEMG signal, and calculating an initial distribution sequence vector; determining the number of kinematic units, namely, the cluster number, by using time domain subtraction; according to the cluster number, clustering array type sEMG waveforms by using a minimum distance classifier; finally, recalculating a new kinematic unit distribution sequence vector, circulating the executive routine till decomposition is finished, classifying all the distribution sequences, and optimizing the result. The decomposition method is high in accuracy, quick in calculation speed and easy to operate.

Description

technical field [0001] The invention relates to an array type sEMG signal automatic decomposition method. Background technique [0002] Surface EMG (sEMG) is the use of surface electrodes to detect electromyographic signals from the human body surface. Compared with needle electrode electromyographic signals (Needle EMG, NEMG), it is non-invasive and easy for patients to accept. Therefore, The application prospect is broad. Experiments have shown that using array sEMG can improve the detection rate of motor units (MU), especially the detection and identification of small-amplitude motor unit action potentials (MUAP). The origin of sEMG is MUAP, the action potential released by each motor unit activated during a given muscle contraction. In any given recruitment pattern, numerous motor units are activated in an asynchronous pattern, and the sum of the activities of these motor units constitutes the intensity of the EMG signal. Array sEMG signal is essentially the sum of m...

AI Technical Summary

Problems solved by technology

The convolution kernel compensation algorithm method is a blind signal decomposition method, and the effect of this method is still unsatisfactory when the waveform superposition is serious

The signal-to-noise ratio of array-type sEMG is low, and the variability of MUAP waveforms is strong and the degree of mutual superposition is large, which is the main reason for the difficulty of its decomposition

Many researchers have made some modifications to the decomposition method of the inserted EMG signal and applied it to the decomposition research of the surface EMG signal, but none of them can achieve the decomposition effect of the inserted EMG signal.

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