Multi-wavelet fusion feature based neuron action feature extraction method

Abstract

The invention relates to a multi-wavelet fusion feature based neuron action feature extraction method. The method comprises the following steps of: denoising an original action potential signal by using fast wavelet transformation to acquire a denoising action potential signal; performing multi-wavelet base analysis on the denoising signal to acquire a plurality of groups of wavelet time frequency features; respectively fusing the wavelet features with different sizes to acquire a multi-wavelet feature of action potential; and respectively fusing a low-frequency component and a high-frequency component of the signal through the multi-wavelet fusion feature according to features of different wavelet bases to acquire a group of new time frequency features. By adopting the method, mutation and phase step features of the signal are kept, so that the high-frequency component and the low-frequency component of the signal are restored to a certain extent; meanwhile, information and position of a phase step or mutation point of the signal are kept unchanged.

Description

technical field [0001] The invention belongs to the field of biomedical engineering, and relates to a neuron action potential feature extraction method, in particular to a neuron action potential feature extraction method based on multi-wavelet fusion features. Background technique [0002] The feature extraction technology of neuron action potential is the preliminary basis for the analysis and research of action potential sequence encoding. Therefore, extracting effective features of action potentials, and classifying action potentials into their corresponding neurons according to the obtained effective feature information, plays a very important role in the subsequent analysis of neuron spontaneous and evoked action potentials. [0003] The current classification of neuron action potential mainly includes clustering method, template matching method and classification method based on feature analysis. The clustering method solves the superposition problem of action potent...

AI Technical Summary

Problems solved by technology

However, the single wavelet base used in wavelet analysis cannot characterize the complete characteristics of the signal, so it is often not comprehensive enough when analyzing the characteristics of action potentials.

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