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研究生(外文):LIN, SHENG
論文名稱(外文):The Study of Deep Learning-based Heart Sound Recognition
指導教授(外文):WU, YU-CHI
外文關鍵詞:heart soundshidden Markov modellong short-term memory modelsupport vector data description
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此研究計畫分為心音週期中的類別辨識演算法和心音週期投票演算法兩部分,(1)使用ECGdeli開源工具箱、LR-HSMM、Bi-LSTM模型求出心音週期中的各類別,過程中使用特徵包絡和K-fold等手法將訊號有效地做處理,最後辨識出第一心音、心音收縮期、第二心音、心音舒張期。(2)將辨識過的心音,做週期性的分割,使用Bi-LSTM和SVDD作為模型基礎,對於每個心音週期,使用時域的統計量和頻域的梅爾頻率倒譜係數做為特徵,之後使用投票的方法,將心音以特定投票比例,分類辨識正異常。經過實驗,投票率在5~30%時,辨識準確度92.85%、靈敏度89.44%、特異度93.71%和F1 Score 83.74%。

Every year, many people die from illnesses, and cardiovascular diseases account for most of the deaths caused by diseases. Most doctors use stethoscopes to identify whether a patient's heart sound is abnormal or not, but in fact, the diagnosis of heart sounds relies heavily on the clinical experience and examination skills of doctors, and even well-trained doctors may not be able to identify the same answer. Therefore, the development of a heart sound classificatiton algorithm to effectively identify normal and abnormal heart sounds can provide doctors with clinical reference data in the first instance, allowing them to confirm with certainty whether a patient has an abnormal heart sound, so that the patient can receive correct treatment earlier.
This research project is divided into two parts: the classification algorithm in heart sound cycle and the heart sound cycle voting algorithm. (1) The ECGdeli open source toolbox, LR-HSMM, and Bi-LSTM models were used to find out the various stages of heart sound cycles, and the signals were processed effectively using feature envelopes and K-folds. Finally, the first heart sound, systolic heart sound, second heart sound, and diastolic heart sound were identified. (2) The classified heart sounds were segmented periodically using Bi-LSTM and SVDD as the basis of the model, and for each heart sound cycle, the time domain statistics and the frequency domain inverse coefficient of Meier frequency were used as the features. Then, the heart sounds were categorized as normal and abnormal using a voting method with a specific voting ratio. Then, the heart sounds were categorized as normal and abnormal using a voting method with a specific voting ratio. After the experiments, the accuracy of classification was 92.85%, the sensitivity was 89.44%, the specificity was 93.71%, and the F1 Score was 83.74% when the voting rate ranged from 5 to 30%.

致謝 i
摘要 ii
目錄 iv
圖目錄 vii
表目錄 xi
第一章 緒論 1
1.1研究動機與目的 1
1.2文獻探討 3
1.3研究方法 6
1.4論文架構 8
第二章 心電圖標記心音位置 10
2.1心電圖與心音圖的關聯性 10
2.2使用心電圖標註點預測心音類別 18
2.3 特徵選取 18
2.4 HSMM模型 20
2.5 邏輯回歸 22
第三章 心音分割演算法 25
3.1分幀 25
3.2 K-fold 26
3.3 特徵處理 26
3.4 LSTM模型 27
3.5 Bi-LSTM模型 29
3.6 整體架構 30
第四章 心音週期投票演算法 31
4.1特徵處理 32
4.2 模型使用介紹 34
4.3 整體架構 37
4.4 投票方式 39
第五章 實驗結果 40
5.1 樣本來源 40
5.2 評分方式 41
5.3 心音分割結果 42
5.4 心音週期排列 48
5.5 Bi-LSTM模型心音週期切割結果 51
5.6 SVDD模型心音週期切割結果 57
5.7 實驗討論 60
第六章 結論 64
參考文獻 65
附錄 實驗結果 71

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