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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/383


    Title: 以倒傳遞及卷積神經網路辨識心律不整心電圖之研究;Back-propagation and Convolutional Neural Networks for Arrhythmia Electrocardiogram Classification
    Authors: 簡婉軒;CHIEN, WOAN-SHIUAN
    Contributors: 電機工程研究所
    Keywords: 心律不整;心電圖;高階統計;雙層分類;倒傳遞類神經網路;卷積神經網路;留一病患驗證;arrhythmia;electrocardiogram (ECG);higher order statistics;two-level scheme;back-propagation neural network (BPNN);convolutional neural network (CNN);leave one patient out validation
    Date: 2017
    Issue Date: 2019-07-17
    Publisher: 電機工程研究所
    Abstract: 心電圖辨識(Electrocarfiogram discrimination)在臨床診斷不同心臟疾病方面扮演著相當重要的角色。目前已有很多關於心電圖辨識的方法被提出來,在許多議題上仍有很多空間可以加以探討與改善。本論文我們考慮以留一病患驗證來探討倒傳遞類神經網路(Back-propagation neural network, BPNN)與卷積神經網路(Convolutional neural network, CNN)的辨識率,並建構一套辨識心律不整的系統。本篇論文方法上使用離散小波轉換將訊號分解成不同次頻帶(Subband)成分,接著使用高階統計(Higher order statistics)來描述心電圖訊號,用以提升心搏辨識過程中抗雜訊的能力,另外再加上RR區間的相關特徵,期望對辨識能有所幫助,分類器方面則採用BPNN與CNN來探討。提出的辨識方法分為三個部分,第一個部分為利用BPNN並以排除個體差異的方式驗證,第二個部分為利用BPNN並以留一病患來驗證,第三個部分為討論BPNN與CNN的相關性來結合其優勢並分類。從研究結果得知,利用BPNN做排除個體驗證並搭配2-Folds可達到95.52%;以留一病患驗證方面,正確率僅有51.4%,其原由為每個心律不整的心搏數相對少於正常心搏,造成資料不對等而分類偏向於一類的情形,有鑑於此,本研究提出雙層分類、加入個體參考訊號、加入CNN擷取的特徵和改良BPNN的初始權重值等方法,嘗試提升留一病患驗證時的正確率,結果顯示,當採用雙層辨識架構時,其辨識率最佳可達91.89%,相較於直接分類辨識,其正確率提升超過40%。
    Electrocardiogram discrimination plays a very important role in the clinical diagnosis of heart diseases. A lot of methods for ECG beat classification have been proposed. However, many topics still leave room to be improved. In this paper, we build arrhythmia recognition systems based on back-propagation neural network (BPNN) and convolutional neural network (CNN). We will focus on the accuracy of leave one patient out.In this paper, discrete wavelet transform was used to decompose the signal into different subband components, and then higher order statistics were used to describe the ECG signal to enhance the noise-risisting ability. RR-Interval related features were further included to improve the accuracy. Finally, the beat types would be classified by BPNN and CNN.The proposed method is divided into three parts. First, the beat types are classified by BPNN and the validation method is excluding individual differences. Second, the beat types are classified by BPNN and the validation method is leave one patient out. Third, we discuss the relevance of BPNN and CNN and try to combine their advantages to classify the beat types. The results shows that the accuracy by BPNN with 2-Folds validation method is 95.52%. However, while using the leave one patient out validation, the accuracy is 51.4%. Since the arrhythmia beats are far less in number than the normal beats, this phenomenum causes bias in classification and leads to low accuracy. In order to solve these problems, we proposed a two-level classification scheme by adding reference signals of the test sample, adding the features extracted from CNN, and modifying the initial weight of BPNN intending to improve the accuracy of leave one patient out validation. By using the two-level scheme, the best accuracy can reach 91.89% with leave one patient out validation. Compared with the direct classification, the improvement of accuracy is more than 40%.
    Appears in Collections:[電機工程研究所] 學位論文

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