High-resolution time-frequency analysis method for evoked potential signals

Abstract

The invention relates to a high-resolution time-frequency analysis method for evoked potential signals. The method is technically characterized by comprising the following steps that an input end of asignal acquisition module is connected with an evoked potential signal source to be tested, and the signal acquisition module is sequentially connected with a signal preprocessing module and a signalanalysis module; the signal acquisition module acquires and processes the evoked potential signals and outputs the evoked potential signals to the signal preprocessing module; the signal preprocessing module preprocesses the evoked potential signals and outputs the evoked potential signals to the signal analysis module; and the signal analysis module performs time-frequency analysis on the signals, matches the signals with a large redundant dictionary in an iterative manner, decomposes the signals into a series of time-frequency components, and finally outputs parameter information of distribution components of the signals in a time-frequency domain. According to the method, the design is reasonable, the resolution of time-frequency analysis is improved while the representation problem oftime-frequency components is solved, the signal-to-noise ratio is high and the robustness is good, and the method can adapt to the condition that external noise interference exists.

Description

technical field [0001] The invention belongs to the technical field of biomedical signals, and relates to evoked potential signals, in particular to a high-resolution time-frequency analysis method for evoked potential signals. Background technique [0002] In the process of biomedical signal analysis and processing, time-frequency analysis techniques are commonly used to extract signal features that are stable and easy to identify evoked potentials. Among them, time-frequency analysis methods such as wavelet analysis and short-time Fourier transform can improve time-frequency analysis by adjusting the window function. However, due to the uncertainty of time-frequency, the above two resolutions cannot be improved at the same time, and the time-frequency components cannot be characterized. Therefore, it is difficult to establish an objective standard to perform time-frequency domain analysis of evoked potential signals. Evaluate. Contents of the invention [0003] The purp...

AI Technical Summary

Problems solved by technology

[0002] In the process of biomedical signal analysis and processing, time-frequency analysis techniques are commonly used to extract signal features that are stable and easy to identify evoked potentials. Among them, time-frequency analysis methods such as wavelet analysis and short-time Fourier transform can improve time-frequency analysis by adjusting the window function. However, due to the uncertainty of time-frequency, the above two resolutions cannot be improved at the same time, and the time-frequency components cannot be characterized. Therefore, it is difficult to establish an objective standard to perform time-frequency domain analysis of evoked potential signals. Evaluate

Images