本篇論文提出利用心率變異度來建立一個充血性心臟衰竭辨識及病況分級系統。在過去的研究中，大多數都針對二十四小時的心電圖資料來判斷病況的嚴重程度，因此我們提出了較為快速的方法來達到病況分級的目的。其中，在長時間分析的部分我們所使用的資料長度為四小時；而短時間分析使用的資料長度為五分鐘。所有的心電圖紀錄都是來自於國立台灣大學醫學院的二十四小時臨床實驗。
我們的辨識及病況分級系統主要分成四個部分：資料處理、特徵擷取、特徵選取、以及分類。首先，我們會取出每位測試者醒著的四小時及五分鐘的心跳間隔時間序列以及確認這些資料的心跳定位及心跳類型。為了萃取出適當的特徵，我們使用了不只是基本的線性特徵，還有一些非線性的方法像是多尺度熵分析、去趨勢波動分析、多重動態趨勢分析皆運用於我們的論文當中。為了更進一步地找出特徵與充血性心臟衰竭間的關聯，我們使用了統計方法來驗證每一個特徵在充血性心臟衰竭與對照組以及病況輕微與嚴重間是否有顯著差異。接著排除一些對於辨識無效的特徵後，我們使用了前饋式特徵選取方法分別為短期與長期的分析選擇出最佳的特徵組合。最後運用支持向量機的原理來尋找最好的支持超平面，並將所有的樣本區分為三類：健康、病況輕微以及病況嚴重。
在我們的論文中也討論了短期與長期分析的比較，最後得到長時間分析的辨識率為91.67%，而短時間分析的正確率為96.67%。

In this thesis, a detection and quantification system of congestive heart failure (CHF) based on heart rate variability was proposed. Since the majority of studies focused on 24-hour electrocardiogram (ECG) data analysis for risk assessment, we offered a faster manner to achieved this goal. For the long-term analysis, we accessed 4-hour data; for the short-term analysis, we captured 5-minute data. All the ECG data was acquired from the 24-hour recording of the clinical trial in National Taiwan University Hospital.
The proposed detection and quantification system was composed of four portions: data processing, feature extraction, feature selection, and classification. First, we retrieved 4-hour and 5-minute daytime RR interval time series for the subjects and checked the position and beat type of every R peak. To extract more physiological traits from limited amount of ECG data, not only the conventional linear methods but also the non-linear measurements, such as the multiscale entropy, detrended fluctuation analysis, and multi dynamic trend analysis, were applied in our research. To further dig out the relation between the features and congestive heart failure, we employed the statistical method to verify whether there existed a significant difference in these features between CHF and Control, also mild CHF and severe CHF. After excluding the features that were not effective enough, we chose the sequential forward selection (SFS) to search for the best feature subset for the short- and long-term analysis, respectively. Afterwards, the support vector machine (SVM) was applied to search for the best support hyperplanes in order to organize the subjects into three categories: no risk, mild risk, and severe risk.
The comparisons of short- and long-term analysis are also discussed in this thesis. The recognition accuracy of risk assessment is up to 91.67% for long-term analysis, and for short-term analysis achieved 96.67%.

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