Bioelectric signal processing circuit and a method based on compressed sensing

Abstract

The invention relates to the technical field of bioelectric signal processing, in particular to a bioelectric signal processing circuit based on compression sensing, comprising a clock frequency dividing module, a main control module and a data processing module, wherein, the clock frequency dividing module and the main control module are connected; The clock frequency dividing module is used forproviding sampling frequency and transmission frequency to the data processing module, the main control module is used for setting compression ratio of the data processing module and the type of the collected bioelectric signal, and the data processing module is used for selecting the corresponding processing module for bioelectric signal processing, and the clock frequency dividing module is usedfor providing sampling frequency and transmission frequency to the data processing module. The types of bioelectrical signals include neuro-electrical signals and non-neuro-electrical signals. The invention also discloses a bioelectric signal processing method based on compression sensing. The bioelectric signal processing circuit based on compressed sensing provided by the invention can satisfythe processing of neural signals and non-neural signals, and solves the problem that only one bioelectric signal can be processed in the prior art.

Description

technical field [0001] The invention relates to the technical field of bioelectrical signal processing, in particular to a bioelectrical signal processing circuit based on compressed sensing and a bioelectrical signal processing method based on compressed sensing. Background technique [0002] Compressed Sensing (CS) is a new signal acquisition and processing theory, which has been widely used in communication, image processing, language processing, wireless body area network and medical health and other fields. Compressed sensing allows the sampling of analog signals at a sub-Nyquist rate while avoiding the traditional aliasing problem, and can effectively compress the signal during the sampling process. The process of compressed sensing can be described as follows: First, the signal is required to have sparsity in a specific domain Ψ, where sparsity means that most of the values ​​of the signal in a specific domain are 0 or minimum values, and only a few values ​​are large...

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Problems solved by technology

[0006] Since the amplitude-frequency characteristics of various bioelectrical signals in the human body are not the same, and the selection of sampling frequency and compression ratio has a significant impact on the quality of the reconstructed signal, in the existing related work, most circuits can only process one bioelectrical signal , a small number of circuits can process two kinds of bioelectrical signals, but no circuit has the ability to process neural electrical signals and other electrical signals at the same time

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