急性心肌梗塞(AMI)發作於營養及氧氣無法供給至心肌細胞，導致心肌細胞缺氧受損或壞死。壞死的區域可能會妨礙心肌正常的收縮，加重心臟健康部位的負荷，造成更多心肌死亡，使受損的心臟越來越衰弱，最後可能演變成心臟衰竭等更嚴重的病症。因此我們希望能從急性期找到ㄧ些能預測患者慢性期的心臟狀態的參數，做為心臟惡化的預測指標。
在此論文中，患者依阻塞的血管可分為右冠狀動脈(RCA)組與左前降支(LAD)組。每個患者在經過心導管手術(PCI)之後會記錄4次24小時心電圖信號(ECG)，分別在急性心肌梗塞後三天內(急性期)、三個月、六個月、十二個月(慢性期)。我們擷取該四個時期的非睡眠連續四小時片段進行心律變異性分析(HRV)，再用曼惠特尼U檢定評估急性心肌梗塞病人與對照組之間的心律變異性分析參數差異。結果顯示患者與對照組之間存在差異。在慢性期，頻域參數與多尺度熵(MSE)曲線展現急性心肌梗塞病人與對照組之間的差異。在急性期，急性心肌梗塞病人去趨勢波動分析(DFA) 的長尺度參數(α2)顯著低於對照組。此結果也證明了非線性的心律變異性分析方法如多尺度熵和去趨勢波動分析，提供傳統心律變異性分析時域與頻域參數無法展現的資訊。而右冠狀動脈組與左前降支組跟對照組之間有顯著差異的心律變異指標略有不同，印證了阻塞血管的不同會對心臟造成不一樣的影響。因此，右冠狀動脈組與左前降支組不應混在一起分析。
另外我們希望能夠從急性期的心電圖信號找出可用的參數去預測患者將來復發急性心肌梗塞、心臟衰竭或死亡的機率。但由於追蹤期內未有患者復發急性心肌梗塞、心臟衰竭或死亡。我們根據患者急性心肌梗塞ㄧ年後的左心室射出分率(LVEF)數值，將患者分成兩組。左心室射出分率數值較低者視為心臟狀態ㄧ般好，左心室射出分率數值較高者視為心臟狀態較好。兩組患者在急性期即有顯著差異的心律變異性分析參數有：正常心律間其標準差(sdNN)、低頻(LF)、多尺度熵曲線的短尺度斜率(slope1-5)、去趨勢波動分析的長尺度參數(α2)。從血液測量測得且與心臟受損面積相關的標幟物，肌酸激酶-MB(CKMB)，亦存在顯著差異。我們利用接收者操作特徵(ROC)曲線分析觀察各參數與肌酸激酶-MB的鑑別能力，擁有最高鑑別能力的是低頻(曲線下面積AUC=0.8051)，其次是多尺度熵曲線的短尺度斜率(曲線下面積=0.7721)，第三者為肌酸激酶-MB (曲線下面積=0.739)。我們用肌酸激酶-MB、低頻、多尺度熵曲線的短尺度斜率建構一個邏輯回歸模型，其曲線下面積為0.8235。表示將肌酸激酶-MB與心律變異性分析參數結合確實能提升預測患者一年後狀態的預測能力。

Acute myocardial infarction (AMI) onset when an interruption in the supply of myocardial oxygen and nutrients occurs and causes damage to the myocardium. The infarct area may hinder the normal contraction of the myocardium, increasing the loading of healthy parts of the heart, and even cause death of more cardiac cell. Finally, it may evolve into heart failure or other more severe conditions. Therefore, we want to find some parameters that can be used as a predictor of cardiac deterioration.
In the thesis, according to the blocking blood vessel, AMI patients are divided into right coronary artery (RCA) group and left anterior descending (LAD) group. Every patient recorded 24 hours ECG during the acute stage (within 72 hours after AMI), 3 months post-MI (90 ± 14 days after AMI), 6 months (180 ± 28 days after AMI), and chronic phase (1 year). At each stage, we only extracted four hours data with waking period in heart rate variability (HRV) analysis, and then used Mann-Whitney U test to assess the difference in HRV parameters between AMI patients and the controls. The HRV results shown that there is a difference between AMI patients and the control subjects. At the chronic phase, the parameters of frequency domain and multiscale entropy (MSE) curve display the difference between AMI patients and the controls. At the acute stage, the long-term fractal scaling exponent (α2) in detrended fluctuation analysis (DFA) of AMI patients is significantly lower than that of the controls. The results also proved that the nonlinear methods of HRV such as MSE and DFA can provide some information that traditional parameters not showed. Since the HRV parameters, which had a significant difference between RCA group and the controls, are slightly different to LAD group, it confirms that the effect on the heart caused by the blocking of dissimilar blood vessels is not the same. Therefore, RCA group and LAD group should not be mixed into one group.
Moreover, we want to find out some available parameters from acute stage ECG signal to predict the probability of AMI recurrence, heart failure or death. However, none of the patients died or developed heart failure during 1 year of follow-up. Therefore, we divided patients into two groups according to the left ventricular ejection fraction (LVEF) after AMI attack one year. The group with lower LVEF was regarded as a group of AMI patients who have a good status of the heart, and the other group was regarded as a group of AMI patients who have a better status of the heart. The HRV parameters, the standard deviation of all normal to normal intervals (sdNN), low frequency (LF), the slope of MSE curve in short scale (slope1-5) and DFA α2 were significantly different at the acute stage between two groups. Creatinine kinase -MB (CKMB), which is obtained from blood tests and related with the damaged area of the heart, also had a significant difference between two groups. In order to compare the discrimination ability of those parameters and CKMB, we used receiver operating characteristic (ROC) curve analysis. LF has the best discriminatory power (AUC=0.8051), the second one is slope1-5 (AUC=0.7721), and the third one is CKMB (AUC=0.739). Then we used LF, slope1-5 and CKMB to establish a logistic regression model, and the AUC of this model is 0.8235. It indicated that combining CKMB and HRV parameters can really enhance the discrimination rate.

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