臺灣博碩士論文加值系統

心房顫動是一個最常見的心律失常的疾病，它是由一些心房內不規則電子游離而產生的。全世界約0.4%的人口有心房顫動的疾病，這樣使得在住院治療方面造成很高的社會成本。針對心房顫動的疾病，有多種的治療方式。然而治療方式的選擇通常是利用臨床上的特徵。如果我們能提供分析的機制以及預測經過治療後的病況，使能提供給醫生一些額外的資訊去診斷需要利用哪一種治療方式。在我們的研究中利用到時間-空間以及非線性的分析來比較治療前後的差異以及個體之間的差異。我們發現經由藥物治療所減少的小波數目跟熵有著正相關的特性。此外我們利用到數學的方法建立藥物治療的模型，利用這個模型推測藥物治療後的特性。針對電燒方面我們利用一些時間空間上的模擬去預測電燒過後的特性。不管在藥物治療或者電燒方面我們均能預測出相似的結果。

Atrail fibrillation (AF) is the most common cardiac arrhythmias. In AF, the normal electrical impulses that are generated by the sinoatrial node are overwhelmed by disorganized electrical impulses. The prevalence of AF is 0.4% of the general population. Management of AF carries high social cost related to hospitalization. There are many kinds of therapy developed to terminate the AF. However the choice of the therapy is based on the clinical guideline. If we can understand the mechanism and forecast the condition after the treatment, it will help the doctor assess which treatment will be efficient. Since the animal model can mimick the clinical situation of human, our research was based on the swine model. In our research, we proposed spatial-temporal and non-linear methods to analyze the difference between before and after the treatment and the different between the subjects with successfully terminated AF and without terminated AF. The decrease of wavelet number after the treatment is positive correlation with the entropy. A mathematical method was proposed to predict the condition after medication. In the RF ablation, we proposed a spatial-temporal method to simulate the condition after the treatment. In both of the treatment, our estimated result is similar to the original condition after the treatment.

Chapter 1. Introduction 1
1.1. Research background 1
1.1.1. Atrial fibrillation 1
1.1.2. Electrocardiogram 2
1.1.3. Reentrant mechanisms of atrial fibrillation 3
1.1.4. Wavelet number, wavelength 4
1.1.5. Treatment 5
1.2. Related work 8
1.3. Research motivation 10
1.4. Research objective 11
1.5. Summary of the thesis 12
Chapter 2. Materials and methods 13
2.1. Data acquisition 15
2.2. Spatial-temporal analysis 17
2.2.1. Activation mapping and vector mapping 17
2.2.2. Wavelet and reentry 19
2.2.3. Activation mapping patterns 21
2.3. Approximate entropy 23
2.4. Prediction 25
2.4.1. State space model 25
2.4.2. ARX model 25
2.4.3. Ablation simulation 27
2.5. Correlation analysis 28
Chapter 3. Experimental results 29
3.1. Wavelet number and AA interval 29
3.2. Analysis and prediction of therapeutic effects by propafenone 33
3.2.1. Characteristic of propafenone 33
3.2.2. Entropy 35
3.2.3. Prediction 37
3.3. Analysis and prediction of therapeutic effects by sotalol 39
3.3.1. Characteristic of sotalol 39
3.3.2. Entropy 41
3.3.3. Prediction 44
3.4. Radiofrequency ablation 46
3.4.1. Characteristic of radiofrequency ablation 46
3.4.2. Prediction 48
Chapter 4. Discussion 49
4.1. Spatial-temporal pattern 49
4.2. Entropy 51
4.3. Prediction 52
Chapter 5. Conclusion 54
Reference 55

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