臺灣博碩士論文加值系統

人類仰賴於科技的進步，知識的快速傳遞，電腦的普及發展，使得資訊傳遞更加迅速，也加快了工作效率與品質。另一方面，現代人的飲食習慣及生活壓力等因素也隨著工作效率的提升而改變，速食的普與生活壓力變大，加速導致心臟方面的疾病產生，造成近年來因心臟功能不良的死亡率越來越高。因此，在醫療技術裡，如何事先有效且準確地發現心臟疾病的問題是個非常重要的技術，在這些技術中，又以心電圖 (ECG) 來檢測心臟疾病最為直觀且廣為應用。
ECG是測量和診斷異常心臟節律的最好的方法，尤其是用來診斷心電傳導組織受損時心臟的節律異常以及因電解質平衡失調所引起的心臟節律之改變。
近幾年來，許多研究都在探討疾病與心律變異的關聯，而心律變異的數據有很多，這些數據用以觀察人體健康情況。郭正典與陳高揚（1997）指出，心率變異度(Heart Rate Variability，HRV)的分析簡單，可藉由定量方式來辨別交感與副交感的神經活性，為評估心臟與自律神經強度的一種方法。再者，Kleiger等[3]最早報導SDNN可預測心肌梗塞後的死亡，據此觀點，延伸至其他HRV分析後的SDNN、MeanB、MeanHR、SDHR、RMSSD、NNx、pNNx、SDNNi數據，做數據整理之後，經由SPSS統計分析結果，來了解是否這些指標是否能有『機會』來做出區分健康與否的『顯著差異』。
本研究利用PhysioBank中的兩組資料（Normal Sinus Rhythm R-R intervals、MIT-BIH Arrhythmia Database），提取其ECG資料中的R-R interval，利用HRV分析軟體（HRVAS_v1.0.0）來分析出數據，再經由統計分析軟體SPSS做不同分類（健康型與疾病型，健康型的男性與女性，疾病型的男性與女性）下的『獨立樣本的t檢定』，經由軟體顯示結果，我們發現在健康型與疾病型的分類比較中，MeanB、MeanHR、RMSSD、NNx、pNNx、Lomb-Scargle PSD、Burg PSD、Welch PSD、Power(n.u.)-LF、Power(n.u.)-HF都出現顯著差異，但是在『健康型的男性與女性』中，只有頻域出現顯著差異，而『疾病型的男性與女性』的分類比較，就沒有顯著差異。

With the fast growing of technology and wide popularity of computers, new information has been spread more quickly and working efficiency and quality have been improved accordingly. However, the change of working environment not only leads to high work pressure but also influences dietary habits, both of which further give rise to the high occurrence of heart-related diseases. Therefore, how to beforehand detect heart diseases effectively and accurately is a crucial technique. Among these techniques, using an ECG to detect heart diseases is most widely employed.
ECG is the best way used to measure the rate and regularity of heartbeats, as well as the size and position of the chambers, the presence of any damage to the heart, and the effects of drugs or devices used to regulate the heart, such as a pacemaker.
Recently, many studies have focused on the connection between disease and heart rate variability. Quo and Chen (1997) indicated that HRV is an easy way to distinguish the sympathetic and parasympathetic nervous activity, which can serve as an approach to evaluate the heat and automatic nerve. Besides, Kleiger pointed out that the use of SDNN can predict the death after myocardial infarction. Bases on this perspective, the data of SDNN, MeanB, MeanHR, SDHR, RMSSD, NNx, pNNx and SDNNi on the SPSS may be possibly adopted to find out whether there is significance between being healthy or non-healthy.
This research first retrieve two sets of data (Normal Sinus rhythm R-R intervals and MIT-BIH Arrhythmia Database) from the PhysioBank. Second, the data of R-R interval from ECG is analyzed by HRVAS_ v1.0.0. Further, the t-test is conducted with SPSS to examine the four groups based on gender and health condition. The statistic results indicated that there is significance in MeanB, MeanHR, RMSSD, NNx , pNNx ,Lomb-Scargle PSD,Burg PSD,Welch PSD,Power(n.u.)-LF and Power(n.u.)-HF. But, the significance is only shown in frequency-domain between healthy males and healthy females while no significance was found between sick males and sick females.

目 錄 …………………………………………………I
圖表索引 …………………………………………………II
中文摘要 …………………………………………………IV
Abstract ……………………………………………VII


第 一 章 緒論………………………………………1
第 二 章 相關理論與分析工具…………3
第 三 章 研究方法………………………………9
第 四 章 分析結果………………………………20
第 五 章 結論與討論………………………35

參考文獻 …………………………………………………41

參考文獻
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