本論文提出一心臟衰竭疾病的快速辨識系統，基於快速篩檢的目的，我們提出了使用短時間的分析方法，其中分析的資料長度為五~十五分鐘。
我們的辨識系統主要從兩個方法來分析，一是支持向量機(SVM)、二為多輸入神經網路(MINN)。支持向量機為過去相關的研究常用之技術，多輸入神經網路為此研究新提出的方法。
第一個方法主要可以分為三個部分: 特徵擷取、特徵選取、及分類。我們使用了短時間的心律變異度分析，得到時域及頻域的線性特徵，此外、還有ㄧ些非線性的分析方法如多尺度熵分析、去趨勢波動分析等。為了進一步找出這些特徵與疾病的關聯性，我們使用了統計方法來驗證每一個特徵在充血性心衰竭與對照組間是否有顯著差異。在排除ㄧ些對於辨識沒有充分有效的特徵以及歧異點後，我們使用了前饋式特徵選取方法選出最佳的特徵組合，最後運用支持向量機的原理來尋找最好的支持超平面，分類健康與心衰竭的病患。
第二個方法同樣可分為三個部分: 資料前處理、模型建置、及分類。由第一個方法得到的啟發，我們發現兩組心電圖資料在時域及頻域上的特徵皆有顯著的差異性，因此我們不僅使用時域的心跳間期訊號，也將其作傅立葉變換取出頻域訊號作為神經網路的輸入端，並且使用不同的神經網路如稀疏自動編碼器(SAE)、長短期記憶網路(LSTM)、卷積神經網路(CNN)建置模型，最後再由多輸入神經網路架構建立輸入端，對模型進行訓練，並得到分類結果。本篇論文中也探討了支持向量機及神經網路的結果與比較，也比較了不同的資料長度的表現，也使用了麻省理工學院(MIT)的資料庫做進一步的測試，最後得到以卷積神經網路的架構下，在五百點(大約五~七分鐘)的心跳間期可達到訓練集準確率 93.36%，測試集準確率為 86.74%。

In this study, a fast detection system for congestive heart failure (CHF) based on multiple input neural network was proposed. To rapid screen, we offered a short-term analysis method to achieved this goal, which data size was 5 to 15 minute ECG data.

The proposed detection system was compared by two approaches: one was support vector machine (SVM), the other was multiple input neural network (MINN). The SVM method was majority applied in other related researches, and The MINN method was the novel approach that was first proposed in this thesis.

First approach was composed of three portions: feature extraction, feature selection, and classification. We used short-term heart rate variability (HRV) analysis to extract time and frequency domain features. Some non-linear methods were also used in this thesis such like multiple entropy analysis (MSE), and de-trended fluctuation analysis (DFA). Furthermore, we used statistical methods to verify whether there existed significant differences in these features between CHF and Control groups. We chose the sequential forward selection (SFS) to search for the best feature subset in the short-term analysis. Afterwards, the SVM was applied to search for the best support hyperplanes in order to classify CHF and Control groups.

Second approach could be composed of three portions similarly: data pre-processing, model-building, and classification. We employed deep learning neural network to train the features by input RR interval signal in both time and frequency domain. We used different type of neural network such as sparse-auto-encoder (SAE), long-short-term memory (LSTM), and convolution neural network (CNN) to build the model. Afterwards, through the multiple input neural network structure to train the model and get the classification result.

The comparisons of two approaches were discussed in this thesis, also we compared different data length performance. To verify the classification ability in other database, MIT-BIH database were also applied to test our system. With our current model, the recognition accuracy between CHF and Control is up to 93.36\% for training set, and 86.74\% for testing set.

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