Rapid compression perception reconstruction method facing to wearable device

Abstract

The invention discloses a rapid compression perception reconstruction method facing to a wearable device, and belongs to the signal processing field. The method comprises the steps of: performing compression measurement to an original electrocardiogram signal x through a constructed m*n dimension random sparse binary observation matrix phi, so as to get an observation vector y, the length of which is m; and based on the observation matrix phi and the observation vector y, reconstructing the original signal by the compression perception reconstruction method learned from the alternating direction multiplier method and the block sparse Bayes, so as to get an estimation vector of the original signal, which is as shown in the description. Compared with the conventional compression perception reconstruction method learned from the block sparse Bayes, the method provided by the invention is advantaged in reaching quicker reconstruction speed on the premise of ensuring the reconstruction precision.

Description

technical field [0001] The invention belongs to the field of signal processing, and in particular relates to a compressed sensing reconstruction method suitable for non-sparse signals, which is used for fast and accurate reconstruction of non-sparse signals compressed by wearable devices. Background technique [0002] With the gradual practical application of telemedicine, more and more advanced wearable devices and technologies have been put into medical diagnosis. There are two main types of theoretical technologies for physiological signal sampling and processing in wearable remote health monitoring systems: 1. One is the traditional Nyquist sampling theorem based on signal bandwidth; the other is the emerging Compressed Sensing (CS) theory that requires the signal to be sparse or compressible. Compressed sensing is a new signal sampling theory proposed in 2004. The idea is to make a global observation of the sparse signal, and then reconstruct the source signal from the...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the invention is to solve the technical problems of the existing compressive sensing algorithm applied to wearable devices with poor sparsity when reconstructing and recovering physiological signals with poor sparsity, and propose a method based on alternating direction multiplier method and block sparseness. Bayesian Learning for Compressed Sensing Reconstruction

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