The System Of Analysis And Auxiliary Diagnosis Of Electrocardiogram

Electrocardiogram (ECG for short) is the synthetic reflection of the heart electricity on body surface, analyzing the electrocardiogram signal and then diagnosing heart's diseases has a very important researching value. At present, there are four portions in the research of the ECG signal analyzing and auxiliary diagnosing, which are data filter, character points recognition, classification and recognition of pathological signals and signal compression. According to these portions, scholars bring forward a lot of analysis methods, there are insufficiencies yet. For example, the result of filter is not so fine, the precision of character point location should be heightened, the methods of the signal compression awaiting improved. In addition, most scholars are inclined to time-frequency analyzing on the auxiliary diagnosing, however, the heart is a chaos system, it may achieve more information with by non-linear dynamics analysis, and it has space to research deeply. The automatic analyzing instruments which achieve the clinical electrocardiogram are always too big and expensive, and it is hard for the common families to be accepted. So, for the sake of extending the diagnosis and treat of early cardiopathy, it is a very significant work to develop a cheap and portable electrocardiogram detecting system and put it in the market. Based on the necessary of theory research and the factual requirement of market, this thesis carries on the study in four main aspects which are development of the electrocardiogram detecting system, the preprocession of ECG signals and the character point recognition, the ECG auxiliary diagnosis and the data compression.Research on the development of the ECG detecting system: aiming at the high price of the electrocardiograph and its hardly popularization, the thesis develops a portable ECG signal recorder. Compared with the other products, this recorder has more functions and is very flexible. There are three operating modes to choice, and it can be used in different situations, the sampling parameter can be adapted, it consumes very low under the idle mode. Basing on the above groundwork, the thesis innovatively achieves the ECG analysis and management system on a smartphone, which realizes the data's collection, processing, memory and display, even the database management of the users'information and remote transfers.Research on the preprocession of ECG signal and the character point recognition: based on the realistic requirement of the ECG analyzing, the thesis designs wavelet filter and morphological filter. It indicates after much analysis that the morphological filter has its particular advantage in correcting baseline drift, but it will bring truncation error when disposing high frequency signals; the wavelet filter has distinct effect on high frequency restraining, but will lose P and T wave energy when disposing the drift. Then, the thesis discusses about the two typical R wave detecting methods: differentiator threshold method and wavelet modulus maxima method, whose shortages are discussed. Aiming at the questions above, the thesis puts forward an R wave identifying algorithm which basing on empirical mode decomposition, combining the mathematics morphology, wavelet and the Hilbert algorithm. This algorithm can achieve a higher detecting precision and a batter effect for noises wiping in the process. Compared to the wavelet filter and the morphological filter, the new algorithm can obtain much higher SNR but not lost the signal quality.Research on the ECG auxiliary diagnosis: the thesis validates the feasibility using the non-linear dynamics in the ECG auxiliary diagnosis field. Through four methods, the thesis studies chiefly on the different ECG signals and their HRV characteristics of different illness in a short time. 1) by calculating the Lyapunov exponent of six states of illness, proves that the heart movement is a weak chaos which has the special meaning to the diagnose, but needs more reseach.2) puts forward the short-time multi-fractal detrended fluctuation half-spectrum analysis of HRV signals, through the calculating, the multi-fractal parameterαof normal arrhythmia, occurrent illness arrhythmia and supraventricular arrhythmia presents a trend, using box-plot, the classifying precision can achieve more than 75%. 3) for malignant ventricular arrhythmia, analyzes its dynamic character, puts forward the multi-parameter method of entropy and fractal to detect the onset and classify these illness, and gets a higher detecting precision. 4) firstly introduces the dynamic time warping from speech recognition to classify ECG abnormal wave, through the compare of close degree, the result of detecting is satisfying, and when used at practical workstation the effect is excellent.Research on the ECG signals compression: the thesis discusses the necessary of the ECG signals compression, for lossy compression, brings forward an improved zero tree coding algorithm, compared with other algorithm which can achieve higher compression ratio; for lossless compression, according to the weak chaos character of ECG signal, puts forward a based chaos forecasting method, and through the experiment proves the validity of this algorithm.