Method and device for identifying somatosensory evoked potential components based on support vector machine

Abstract

The invention discloses a method and device for identifying a somatosensory evoked potential component based on a support vector machine, comprising an acquisition building unit for acquiring median nerve somatosensory evoked potentials of known origin. The time-frequency components of median nerve somatosensory evoked potentials (MNEPs) are obtained by time-frequency conversion. The time-frequency components of MNEPs were matrices transformed to obtain the time-frequency component eigenvectors. The time-frequency component eigenvectors were used as inputs to construct a multi-stage support vector classifier (SVC). The identification unit is used for classifying and identifying the time-frequency components of the median nerve somatosensory evoked potential to be identified according to the multistage support vector classifier, and determining the source corresponding to the time-frequency components of the median nerve somatosensory evoked potential to be identified, so that a multi-stage support vector classifier is designed to improve the sensitivity and specificity of time-frequency classification of somatosensory evoked potentials and effectively overcome the problem of smallsample size.

Description

technical field [0001] The invention belongs to the technical field of biomedical signal processing, and in particular relates to a recognition method and device of a somatosensory evoked potential component based on a support vector machine. Background technique [0002] Somatosensory evoked potential (SEP) is the electrophysiological response recorded when the nerve is stimulated by the outside world, which can reflect the integrity of the spinal cord and central nervous system. The SEP signal directly reflects the nervous system and contains a large amount of direct key information. Compared with the traditional For radiological examination and neurological examination, the signal recognition technology based on SEP signal components has higher application value. [0003] In the prior art, the recognition and classification of the SEP components using the artificial graphic labeling method has the disadvantage of taking a long time, and at the same time, due to unavoidabl...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, the recognition and classification of the somatosensory evoked potential components using the artificial graphic labeling method has the disadvantage of taking a long time, and at the same time, due to inevitable subjective factors and external interference, it is easy to cause misjudgment

Images