| Summary: | 碩士 === 國立臺灣大學 === 電子工程學研究所 === 106 === According to WHO (World Health Organization) statistics, cardiovascular disease has become the top one among ten leading causes of death worldwide. Experts estimate that in 2012,17 million 500 thousand people died of cardiovascular disease, accounting for the 31% total number of worldwide deaths. Cardiovascular disease mortality rate is very high and it was difficult to be expected, so the development of a prevention and early detection system to distinguish between the normal and arrhythmia patients is very important, the difference between normal and arrhythmic patients can help doctors evaluate the conditions of the patients ,give them drugs or advice on medical cares, to further reduce the mortality of cardiovascular disease.
Among many cardiac detection techniques, using an electrocardiogram to detect heart disease is the most simple and effective method. Because electrocardiogram has the advantage of non-invasive, real-time detection, it is widely used in clinic application.
Traditional ECG analysis techniques uses HRV (Heart rate variability) to differentiate between normal and arrhythmogenic patients. This method counts multiple RR interval variants as the criterion, the larger number of variants were classified as arrhythmia patients, and the small number of variants were classified as normal people. Normal ECG are considered to be period according to traditional ECG analysis as well.
In the course of studying MIT database’s electrocardiogram, we found that even in normal people, the signal of heartbeat cycle does not overlap completely, that is,there are aperiodic components, aperiodic signals may come from psychological factors, especially the influence of main consciousness. So the heart is not fixed like a car engine, but there''s random behavior in it,and aperiodic signal is representative of random behavior. Because ECG is composed of periodic and aperiodic signal,we use two sets of bases: one periodic and the other aperiodic. The periodic base is the Fourier base,the disadvantage of Fourier base is larger boundary error. After adding the aperiodic base, we find that aperiodic base can lower the boundary error and stand for random behavior in the spectrum analysis.
This study can be subdivided into two different ways to analyze the ECG signal. The first method is called standing wave expansion solution,which includes standing wave in it, and the second method is called propagating wave expansion solution. The principle of the two expansion is described earlier, adding aperiodic base in the periodic base, and then using the spectrum for further analysis.
The results show that there is random behavior in the heart, that is, there are different aperiodic components in the spectrum with different cardiac cycles in the time domain. And there are a variety of combinations that can be used to describe the cardiac cycle of the same ECG, such as standing wave solutions and traveling wave solutions, which eventually correspond to very similar waveforms of even and odd functions. However,
the even function can be decomposed of two different functions. In addition, based on the power index, we can make a preliminary distinction between normal people and arrhythmia patients.
In the future, we will compare this technique with traditional Heart rate variability analysis. Applying in the treatment of clinical disease, we can help doctors to determine the patients’ disease, and provide the patients’ with their disease information. This way, they can take measures to prevent heart disease, and the goal of preventive medicine will be achieved.
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