Electrocardiogram signal sparse decompression and compression system based on concise common dictionary base

Abstract

The invention relates to an electrocardiogram signal sparse decompression and compression system based on a concise common dictionary base. The electrocardiogram signal sparse decompression and compression system based on the concise common dictionary base comprises a concise common dictionary base offline training module, an electrocardiogram signal online compression module and an electrocardiogram signal reconstruction module, wherein the electrocardiogram signal online compression module is connected with the electrocardiogram signal reconstruction module. The concise common dictionary base offline training module is used for integrating multisource electrocardiogram signal training samples offline to construct a common training sample set, segmenting sample signals, discretizing a Gabor function to form a super complete Gabor dictionary, sparsely decomposing the segmented signals by the Gabor dictionary to obtain a primary common dictionary base, and simplifying the dictionary by clustering algorithm to obtain a concise common dictionary base. Based on the concise common dictionary base, the electrocardiogram signal online compression module is used for sparely decomposing the signals. Based on the concise common dictionary base, signal segments of the electrocardiogram signals are reconstructed to restore complete signals and display signal waveform. The electrocardiogram signal sparse decompression and compression system based on the concise common dictionary base is suitable for implementation on embedded or mobile hardware platforms with resources limited.

Description

technical field [0001] The invention relates to the technical field of human physiological signal processing, in particular to a system for sparsely decomposing and compressing electrocardiographic signals based on a simplified public dictionary library. Background technique [0002] Embedded ECG monitoring technology enables human ECG signals to be collected and monitored around the clock without interference, but the storage and transmission of ECG signals are limited by the hardware resources of the embedded platform, so high precision, high compression ratio, low complexity and Compression technology for low storage space. Sparse decomposition method is a hot spot in signal processing in recent years, but compared with the low-power sensor network embedded platform, its huge amount of calculation and dictionary library limit its application in this field. ECG signal compression techniques can be roughly divided into three categories: time domain direct compression, tran...

AI Technical Summary

Problems solved by technology

[0004] The function of sparse decomposition of ECG signals in the embedded ECG monitoring system has three main limitations: 1. The sparse decomposition search algorithm has a huge amount of calculation; A large amount of resources and time are required to generate a dictionary when performing discrete, and the number of generated dictionary atoms is huge; 3. If a dictionary suitable for individual characteristics is used, different dictionary libraries are required to complete the decomposition for different individuals

Images