Method for extracting action potential feature based on multiple wavelet transformation

A technology of action potential and wavelet transform, applied in the field of biomedical engineering, can solve problems such as description and lack of wavelet transform basis functions, and achieve effective expression, overcome singleness and limitations, good robustness and universality

Inactive Publication Date: 2013-01-09
HANGZHOU DIANZI UNIV
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Problems solved by technology

Due to the diversity of wavelet base functions, different wavelet bases can effectively describe different parts or characteristics of a signal, but a single wavelet base cannot fully describe the action potential, and there is still a lack of a universally applicable wavelet transform base function

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  • Method for extracting action potential feature based on multiple wavelet transformation
  • Method for extracting action potential feature based on multiple wavelet transformation
  • Method for extracting action potential feature based on multiple wavelet transformation

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Embodiment Construction

[0017] An action potential feature extraction method based on multiple wavelet transforms, the specific steps are:

[0018] Step (1) Sampling the firing of neuron action potentials, the Spike signal after sampling, amplification and truncation processing is expressed as a matrix form S N×M , where N is the number of Spike signals, and M is the number of sampling points for each Spike signal. Neuronal action potential sampling such as figure 1 shown.

[0019] In step (2), wavelet transform is used to transform the signal from the measurement space to the feature space, so as to remove the correlation and redundancy in the high-dimensional data. Its essence is to decompose the signal into two different and mutually orthogonal function spaces, one is the multi-scale function space, and the other is the wavelet function space. From the perspective of the filter, it is to decompose the signal into wavelet coefficients and approximation coefficients through high and low frequency...

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Abstract

The invention relates to a method for extracting action potential features based on multiple wavelet transformation. The single wavelet can not comprehensively describe the action potential at present. The method comprises the following steps: extracting wavelet features on a wavelet basis with Db, Sym and Bior wavelets as a basic wavelet transformation function, respectively detecting componentsof each group of wavelet features according to KS detection rules, calculating the weighted coefficient of each feature component through dimensionality reduction of high dimensional feature space, selecting three groups of feature components to synthesize an original confederate matrix, and obtaining a weighted confederate feature under multiple wavelet transformation through multiplying the original confederate matrix by a weighted matrix. The method of the invention overcomes the simplicity and limitation of the single wavelet feature description and combines multiple wavelet features. Therefore, the method can express the specific feature components of the action potential and the fused confederate features after weighting more effectively, and can also realize more comprehensive and effective expression of the signal features.

Description

technical field [0001] The invention belongs to the field of biomedical engineering and relates to a method for processing implanted electroencephalogram signals, in particular to a method for feature extraction of neuron action potentials. Background technique [0002] Using implantable brain-computer interface technology to solve common neurological diseases in human society, such as limb paralysis and loss of hearing and vision, has become a hot and cutting-edge topic in the world today. Extracting the implanted EEG signal - the effective feature of the action potential (Spike) is an important guarantee for subsequent signal processing. The widely used PCA feature based on principal component analysis reflects the difference of non-homologous action potentials through mutually orthogonal feature quantities, but it lacks the description of the frequency domain characteristics of the signal. Therefore, wavelet time-frequency features are increasingly used to characterize a...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
Inventor 范影乐丁颖钟华
Owner HANGZHOU DIANZI UNIV
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