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研究生:賴漢樺
研究生(外文):LAI, HAN-HUA
論文名稱:探討知識蒸餾方法於心肌梗塞心電圖訊號之應用
論文名稱(外文):Exploring the application of knowledge distillation method in myocardial infarction ECG signal
指導教授:葛宗融
指導教授(外文):Ger, Tzong-Rong
口試委員:蘇美如陳賦郁羅畯義
口試委員(外文):SU,MEI-RUFu-Yu ChenChun-Yi Lo
口試日期:2022-06-27
學位類別:碩士
校院名稱:中原大學
系所名稱:生物醫學工程學系
學門:生命科學學門
學類:生物化學學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:104
中文關鍵詞:知識蒸餾心電圖心肌梗塞卷積神經網路
外文關鍵詞:knowledge distillationelectrocardiogrammyocardial infarctionconvolutional neural network
相關次數:
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背景與動機:心電圖是一種非侵入式且價格低廉的心肌梗塞診斷工具。現今各項電腦輔助診斷系統普遍使用卷積神經網路(Convolutional neural network, CNN),透過自動辨識心電圖來診斷心肌梗塞,得以早期診斷及預防。然而,現今診斷心肌梗塞模型為了追求高檢測性能,模型架構龐大和高耗能成為一大隱憂,因此,本研究以六分類心電圖心肌梗塞訊號作為深度學習分類資料集,並導入知識蒸餾方法縮小模型大小和減少耗能。
材料與方法:訊號預處理使用濾除基線飄移、反鋸齒濾波、下採樣、隨機切片採樣和數據增強。深度學習分類模型使用基於CNN深度學習網路的ML-ResNet和VGG-6模型進行訓練及分類;同時,透過知識蒸餾方法提升小模型準確率,並驗證5種不同蒸餾模式的有效性。模型測試是以準確率、精確率、召回率、F1-score和本研究所提出的成長率作為評估指標,並及以無母數檢定來驗證不同知識蒸餾方法之差異性,最後利用Qt designer工具及PyQt5套件建立心肌梗塞檢測系統,並驗證其系統預測性能。
結果與討論:使用知識蒸餾方法在二和六心肌梗塞分類之K折交叉平均驗證下,剩餘誤差知識蒸餾在準確率和成長率都有最好的性能表現,二分類準確率達86.69%,成長率為5.2%,六分類準確率達42.25%,成長率為9.76%。
結論:本研究使用知識蒸餾方法提升於心肌梗塞檢測,並於結果中有顯著提升差異。在心肌梗塞檢測系統中也有顯著的心肌梗塞檢測標記。透過知識蒸餾方法,能維持高準確率及模型減量,未來有望搭載於穿戴式或移動式裝置中,以及建立即時心肌梗塞檢測的健康APP,實際導入臨床應用。

Background and motivation: The electrocardiogram is a non-invasive and inexpensive diagnostic tool for myocardial infarction (MI). Nowadays, various computer-aided diagnosis systems generally use a convolutional neural network (CNN) to diagnose MI through automatic identification of electrocardiogram, to enable early diagnosis and prevention. However, to pursue high detection performance, the current diagnostic MI model has a huge model structure and high energy consumption. Therefore, this study uses the six-category ECG myocardial infarction signal as the deep learning classification data set, and introduces the knowledge distillation (KD) method to reduce the model. size and reduce energy consumption.
Materials and methods: Signal preprocessing used baseline drift removal, antialiasing, downsampling, random slice sampling, and augmentation. The deep learning classification model uses ML-ResNet and VGG-6 model based on the CNN deep learning network for training and classification. At the same time, the KD method is used to improve the accuracy of the small model and verify the effectiveness of 5 different distillation modes. Model testing is based on accuracy, precision, recall, F1-score, and the growth rate proposed in this study as evaluation indicators, and the no-matrix test to verify the differences between different knowledge distillation methods, and finally use the Qt designer tool and the PyQt5 suite to build a myocardial infarction detection system and verify its predictive performance.
Results and discussion: Using the KD method, Residual KD has the best performance in both accuracy and growth rate under the K-fold cross-average validation of two-class and six-class MI classification. The accuracy of the two-class classification is 86.69%, and the growth rate is 5.2%, the six-category accuracy rate reached 42.25%, and the growth rate was 9.76%.
Conclusion: This study used KD to improve MI detection, and there were significant differences in the results. There are also significant myocardial infarction detection markers in the myocardial infarction detection system. Through the knowledge distillation method, high accuracy and model reduction can be maintained. In the future, it is expected to be installed in wearable or mobile devices, and establish a health APP for real-time myocardial infarction detection, which will be practically introduced into clinical applications.

目錄
摘要 I
Abstract II
誌謝 IV
目錄 VI
圖目錄 X
表目錄 XIII
英文縮寫表 XIV
第一章、緒論 1
1.1 研究背景 1
1.2 動機與目的 1
1.3 文獻回顧 2
1.3.1 深度學習於心電圖訊號之應用 3
1.3.2 基於知識遷移的模型壓縮技術 4
1.4 研究目標 5
第二章、理論基礎 7
2.1 ECG生理訊號 7
2.2 心肌梗塞定義 8
2.3 模型架構 10
2.3.1 卷積神經網路(Convolutional neural network, CNN) 10
2.3.2 視覺幾何組模型(Visual geometry group, VGG) 11
2.3.3 深度殘差網路(Deep residual network, ResNet) 12
2.4 模型訓練函數 13
2.4.1 線性整流函數(Rectified Linear Unit, ReLU) 13
2.4.2 Softmax函數 14
2.4.3 均方誤差(Mean-square error, MSE) 14
2.4.4 交叉熵與KL散度 14
2.5 知識蒸餾(Knowledge distillation, KD) 16
第三章、材料與方法 18
3.1 數據集 18
3.2 研究架構 18
3.3 訊號處理(Signal processing) 21
3.4 資料重排(Data rearrangement) 22
3.5 參數驗證(Parameter verification) 24
3.5.2 心電圖訊號時間驗證 24
3.5.3 模型訓練優化器驗證 25
3.5.4 Residual KD模型比例驗證 27
3.6 介面設計(UI design) 27
3.7 知識蒸餾(Knowledge distillation) 28
3.7.1 模型架構(Model framework) 28
3.7.2 蒸餾方法(Distillation method) 33
3.8 模型訓練(Model training) 38
3.9 性能評估(Performance evaluation) 39
3.9.1 模型測試(Model test) 39
3.9.2 K折交叉驗證(K-fold validation) 41
第四章、結果 42
4.1 ECG訊號預處理結果 42
4.2 參數驗證 43
4.3 心肌梗塞檢測系統介面設計 47
4.4 模型訓練 48
4.4.1 二分類訓練曲線 49
4.4.2 六分類訓練曲線 51
4.5 混淆矩陣 53
4.5.1 二分類混淆矩陣 53
4.5.2 六分類混淆矩陣 55
4.6 知識蒸餾方法檢測和定位結果 57
4.6.1 二分類檢測比較結果(ACC) 57
4.6.2 二分類檢測比較結果(SEN) 59
4.6.3 二分類檢測比較結果(SPE) 60
4.6.4 二分類檢測比較結果(F1-score) 61
4.6.5 六分類檢測比較結果(ACC) 63
4.6.6 知識蒸餾總評估指標結果 64
4.7 心肌梗塞檢測系統檢測結果 66
4.7.1 類別預測分佈驗證 68
第五章、討論 70
5.1 心電圖人種差異 70
5.2 病患數據量對于模型訓練的影響 70
5.3 知識蒸餾老師模型選擇問題 70
5.4 模型驗證集問題 71
5.5 臨床醫生心電圖判別標準 71
第六章、結論與未來展望 72
6.1 結論 72
6.2 未來展望 72
參考文獻 73
附錄 76
A 訓練曲線 76
A.1 二分類檢測 76
A.2 六分類檢測 83

圖目錄
圖1-1、研究目標示意圖 6
圖2-1、心電圖節拍紀錄示意圖 8
圖2-2、常見急性心肌缺氧的心電圖特徵 9
圖2-3、CNN神經網路模型架構示意圖 11
圖2-4、VGG16模型架構示意圖 11
圖2-5、殘差網路結構圖 13
圖2-6、KD運算過程示意圖 17
圖3-1、研究架構圖 19
圖3-2、訊號處理流程圖 22
圖3-3、心肌梗塞心電圖數據集分配 23
圖3-4、不同訊號時間之訊號片段。時間分別為12 s、10 s、8 s、6 s、4 s、2 s、1 s、0.5 s 25
圖3-5、知識蒸餾Teacher模型(ML-ResNet) 29
圖3-6、知識蒸餾Student模型(VGG-6) 29
圖3-7、知識蒸餾Student及Assistant模型(VGG-6(S),VGG-6(A)) 30
圖3-8、一維卷積層示意圖 31
圖3-9、最大池化層(Max pooling)示意圖 31
圖3-10、全局平均池化層示意圖 32
圖3-11、Dropout層示意圖 33
圖3-12、知識蒸餾比較方法 33
圖3-13、剩餘誤差知識蒸餾方法示意圖 34
圖3-14、基礎知識蒸餾方法示意圖 35
圖3-15、反向知識蒸餾方法示意圖 36
圖3-16、老師自我知識蒸餾方法示意圖 37
圖3-17、學生自我知識蒸餾方法示意圖 38
圖3-18、ML-ResNet和VGG-6獨立訓練示意圖 39
圖3-19、混淆矩陣示意圖 40
圖3-20、K折交叉驗證示意圖 41
圖4-1、心電圖訊號時間驗證結果 45
圖4-2、訓練優化器驗證結果 45
圖4-3、二分類剩餘誤差知識蒸餾模型比例比較 46
圖4-4、六分類剩餘誤差知識蒸餾模型比例比較 46
圖4-5、心肌梗塞檢測介面設計介紹 48
圖4-6、第一折交叉驗證之二分類心肌梗塞檢測準確率訓練曲線 50
圖4-7、第一折交叉驗證之二分類心肌梗塞檢測損失訓練曲線 51
圖4-8、第一折交叉驗證之六分類心肌梗塞檢測準確率訓練曲線 52
圖4-9、第一折交叉驗證之六分類心肌梗塞檢測損失訓練曲線 53
圖4-10、二分類知識蒸餾之混淆矩陣 55
圖4-11、六分類知識蒸餾之混淆矩陣 57
圖4-12、二分類之知識蒸餾模型ACC成長率五折交叉驗證比較 58
圖4-13、二分類之知識蒸餾模型ACC成長率十折交叉驗證比較 58
圖4-14、二分類之知識蒸餾模型SEN成長率五折交叉驗證比較 59
圖4-15、二分類之知識蒸餾模型SEN成長率十折交叉驗證比較 60
圖4-16、二分類之知識蒸餾模型SPE成長率五折交叉驗證比較 61
圖4-17、二分類之知識蒸餾模型SPE成長率十折交叉驗證比較 61
圖4-18、二分類之知識蒸餾模型F1成長率五折交叉驗證比較 62
圖4-19、二分類之知識蒸餾模型F1成長率十折交叉驗證比較 63
圖4-20、六分類之知識蒸餾模型ACC成長率五折交叉驗證比較 64
圖4-21、六分類之知識蒸餾模型ACC成長率十折交叉驗證比較 64
圖4-22、心肌梗塞檢測系統健康者檢測結果 67
圖4-23、心肌梗塞檢測系統心肌梗塞患者檢測結果 67
圖4-24、病患內ECG心肌梗塞類別預測分佈 69
圖A-1、ML-ResNet模型於二分類心肌梗塞檢測準確率和損失訓練曲線 76
圖A-2、VGG-6模型於二分類心肌梗塞檢測準確率和損失訓練曲線 77
圖A-3、使用基礎知識蒸餾方法後之VGG-6模型於二分類心肌梗塞檢測準確率和損失訓練曲線 78
圖A-4、使用反向知識蒸餾方法後之VGG-6模型於二分類心肌梗塞檢測準確率和損失訓練曲線 79
圖A-5、使用自我知識蒸餾方法後之ML-ResNet模型於二分類心肌梗塞檢測準確率和損失訓練曲線 80
圖A-6、使用自我知識蒸餾方法後之VGG-6模型於二分類心肌梗塞檢測準確率和損失訓練曲線 81
圖A-7、使用剩餘誤差知識蒸餾方法後之VGG-6 (S)和VGG-6 (A)模型於二分類心肌梗塞損失訓練曲線 82
圖A-8、ML-ResNet模型於六分類心肌梗塞檢測準確率和損失訓練曲線 83
圖A-9、VGG-6模型於六分類心肌梗塞檢測準確率和損失訓練曲線 84
圖A-10、使用基礎知識蒸餾方法後之VGG-6模型於六分類心肌梗塞檢測準確率和損失訓練曲線 85
圖A-11、使用反向知識蒸餾方法後之VGG-6模型於六分類心肌梗塞檢測準確率和損失訓練曲線 86
圖A-12、使用自我知識蒸餾方法後之ML-ResNet模型於六分類心肌梗塞檢測準確率和損失訓練曲線 87
圖A-13、使用自我知識蒸餾方法後之VGG-6模型於六分類心肌梗塞檢測準確率和損失訓練曲線 88
圖A-14、使用剩餘誤差知識蒸餾方法後之VGG-6 (S)和VGG-6 (A)模型於六分類心肌梗塞損失訓練曲線 89

表目錄
表3-1、數據集資料數分佈 23
表3-2、訊號時間長度與優化器參數驗證之訓練參數配置 24
表4-1、六個類別之12導程心電圖訊號前處理結果 42
表4-2、五折交叉驗證知識蒸餾方法評估指標 65
表4-3、十折交叉驗證知識蒸餾方法評估指標 66


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