Electrocardio-compression method and decrypting method of wireless cardiogram monitor

Abstract

The invention belongs to the biomedical engineering and computer field, in particular relates to a method that carries through embedded zero-tree coding to realize the high-efficient compression of ECG data on the basis of lifting the optimal space of wavelet packet. The method carries through the parity sequence decomposition to ECG signals obtained by pretreatment and obtains a first-order general picture and a first-order detail node by the lifting algorithm; the method analyzes whether the first-order father node needs to be decomposed into child nodes according to the cost function and decomposes the first-order father node that needs to be divided into second-order child nodes by the lifting algorithm, in the same way, till the lifting wavelet packet space structure of the lowest information entropy is obtained; finally, the embedded zero-tree coding algorithm is carried out so as to realize the high-efficient compression to the ECG data according to the low redundancy mapping relation on the basis of optimally lifting the wavelet packet. The method aims at the system characteristics of the long-distance ECG monitoring terminal and realizes the high-efficient compression algorithm of the ECG data with transferable embedded monitoring terminal, thereby solving the problems of the data storage and the real-time transmission in the long-distance ECG monitoring.

Description

technical field [0001] The invention belongs to the technical field of biomedical engineering, and in particular relates to a method for compressing and decompressing an electrocardiogram of a wireless electrocardiogram monitor. Background technique [0002] In recent years, with the increase of people's life and work pressure and the aging of society, the incidence of various heart diseases has been increasing year by year. The traditional face-to-face diagnosis mode between patients and doctors can no longer meet the huge growing demand for health care. The medical system is gradually tilted towards family-centered, and people are paying more attention to disease prevention. These changes directly drive the development of ECG monitoring from non-real-time off-line monitoring to remote ECG monitoring based on mobile communication networks, and wearable wireless ECG monitors emerge as the times require. [0003] As a diagnostic monitoring terminal for telemedicine and mobil...

AI Technical Summary

Problems solved by technology

This method has been successfully applied to image compression JPEG2000 by virtue of the time-frequency analysis advantages of wavelet transform. In the aspect of ECG signal compression, the application of this algorithm has also made some progress, but so far, this algorithm is mainly used in system resources. Rich ECG storage and backup system at the server side, if it is to be applied to ECG compression at the monitoring side, the algorithm must solve two major problems: First, the embedded zero-tree coding algorithm in the wavelet domain has large redundancy in the compression results Bottleneck problem: From the perspective of information theory, the essence of data compression is to extract the order of signals and remove redundancy, that is, the process of reducing information entropy

Because the traditional wavelet domain embedded zero tree coding adopts the fixed frequency band decomposition method, it often leads to the generation of isolated zeros with high information entropy in the encoded stream, which will inevitably make the information entropy unable to be fully reduced, and the compression results still have large redundancy.

Secondly, the embedded zerotree coding algorithm in the wavelet domain still has high algorithm complexity and resource occupancy: floating-point calculations are used in the algorithm process, which not only takes up a lot of resources, but also has low execution efficiency; there are still many redundant steps in the algorithm process , for example, in the algorithm, the scale transformation is performed first, and then the second extraction discards half of the redundant data on each scale, which wastes a lot of memory space and increases the amount of calculation

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