Correlation Between ECG Signal Compression Based On Wavelet Transform And Stroke

In recent years, with the economic development, the quality of people’s living standards continue to increase. However, the patients with various cardiovascular diseases are increasing year by year, cardiovascular is a disease that endanger human life and health, its treatment of the diseases without delay. ECG as an important physiological signals, provides an important scientific basis for analysis, diagnosis and treatment of various cardiovascular diseases. ECG data for high-quality compression and real-time transmission has become a hot research topic in today’s society.We know that the ECG signal is a quasi-periodic signal, has a wealth of redundancy. If you are using a reasonable compression method to reduce these redundant, coding system could not only saves storage space, but also reduce the data transmission effectively. Could achieve the purpose of compression and could avoid the bandwidth usage and cost savings. This requires that we make good use of its relevance:a single cycle or during the week, and we can call them as Cardiac or stroke Correlation. We analyzed ECG unique correlation and give little improvement, in order to achieve the effective compression, according to its characteristics and create the appropriate compression scheme.This paper presents an innovative solution that a signal Compression algorithm based on wavelet transform and Correlation between cardiac. First introduced the basic knowledge of the wavelet transform, conditional entropy and entropy coding; Then we do the experiment. We use the continuous wavelet transform modulus maxima value method detecting the Location of the ECG R-wave. And cutting restructuring of the original ECG image based on the location of R wave, to establish a new image of the appropriate coding scheme; Secondly according to the ECG characteristics, establish appropriate context model (encoded probability environment); Again according to the environment give bit-plane conditions coded:divided into significant coding, symbol coding and refinement coding; Finally, compared the results of the experimental results with the literature algorithms, give a summarize and further outlook. To illustrate the effectiveness of the algorithm, In this paper, We have done the experiments using many data in the arrhythmia database. In the fourth chapter we described the arithmetic coding process and in the fifth chapter we described the experimental results comparing with the literature algorithms. We have the following conclusions:(1) This compression algorithm is generally applicable to a variety of the ECG signal with waveform characteristics different waveform characteristics;(2) It has a high compression efficiency and be able to compresse signal in a real-time.