博碩士論文 etd-0621120-203312 詳細資訊


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姓名 林彥廷(Yan-Ting Lin) 電子郵件信箱 E-mail 資料不公開
畢業系所 電機工程學系研究所(Department of Electrical Engineering)
畢業學位 碩士(Master) 畢業時期 108學年第2學期
論文名稱(中) 深度學習應用於十二導程心電圖病徵分類之研究
論文名稱(英) Applying Deep Learning to Classification of 12-lead Electrocardiography Symptoms
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    紙本論文:5 年後公開 (2025-07-21 公開)

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    摘要(中) 十二導程心電圖是醫院在心臟疾病中常用的輔助診斷指標,心電圖的檢測由於其價格低、無侵入的特性被廣泛用於心臟疾病的篩檢、診查及體檢中,且每天的檢測量巨大。目前多導程的心電圖設備已經廣泛用於臨床當中,部分設備已經具有自動分析診斷功能,但自動分析對於多種心電圖異常事件的判別還不夠精確,需要醫生做進一步確認。
    近年來人工智慧在心電圖的分類及預測領域有了許多應用,深度學習的技術發展有望助力心電圖波形、心電圖異常事件的分類及預測,從而達到提升預測精度的目標。本研究藉由高雄醫學大學附設醫院的研究數據庫所提供之十二導程心電圖,並由心臟內科的專業醫師進行標記,在標記的過程中為了避免誤判,利用了異常檢測的方式排除及修正。
    在此研究的數據庫中,由於心電圖類別標記的筆數非常不平衡,而且各個病徵類別包含了許多不同的特徵,因此本研究建立了兩階段式的多尺度深度學習模型,運用兩階段式的學習,在第一階段可以將數量較多的類別區分出來,避免在訓練的過程中產生過度凝合,以至於結果偏向於數量多的那一類,因此先將資料分為是否有病徵兩類進行訓練;第二階段再將第一階段分類有病徵的資料,輸入多尺度深度學習模型,利用多尺度深度學習模型的特性,進行不同尺度的縮放,可以提取不同尺度的特徵進行病徵分類,使用該模型建立十二導程心電圖判讀分類系統,最後結果顯示,在精確率及召回率中分別得到95.49%及98.31%,在F1-Measure可得到96.88%。
    摘要(英) The 12-lead Electrocardiography (ECG) is a commonly used auxiliary diagnostic indicator in hospital for heart disease. Electrocardiography detection is widely used in screening, diagnosis, and physical examination of heart diseases due to its low price and non-invasive characteristics. And the daily detection volume is huge. At present, multi-lead ECG equipment has been widely used in clinical. Some devices already have automatic analysis and diagnosis functions. However, the automatic analysis is not accurate enough to discriminate against many abnormal ECG events. The doctor needs further confirmation.
    In recent years, artificial intelligence has many applications in the field of ECG classification and prediction. The development of deep learning technology is expected to help the classification and prediction of ECG waveform and ECG abnormal events. To achieve the goal of improving prediction accuracy. This study was based on the 12-lead ECG provided by the research database of the Kaohsiung Medical University Hospital (KMUH). And marked by professional doctor in cardiology. To avoid misjudgment during the marking process, the method of anomaly detection is used to eliminate and correct.
    In the database of this study, since the number of ECG category markers was very unbalanced. And each symptom category contains many different characteristics. Thus, this study established a two-stage multi-scale deep learning model. Using two-stage learning, in the first stage, a large number of categories can be distinguished to avoid over-fitting during training. So that the result is biased towards the larger number. And so, the information is divided into two types for training. In the second stage, the data with symptoms from the first stage, input to multi-scale deep learning model. Using the characteristics of multi-scale deep learning models, scaling at different scales can extract features of different scales to classify symptoms. Use this model to establish a 12-lead ECG interpretation classification system. The result shows that precision rate and recall rates are 95.49% and 98.31% respectively, and F1-Measure can get 96.88%.
    關鍵字(中)
  • 兩階段式學習
  • 多尺度網路
  • 深度學習
  • 異常檢測
  • 十二導程心電圖
  • 關鍵字(英)
  • Multi-scale network
  • 12-lead Electrocardiography
  • anomaly detection
  • deep learning
  • two-phase learning
  • 論文目次 目錄
    論文審定書 i
    誌謝 ii
    摘要 iii
    Abstract iv
    圖目錄 vii
    表目錄 viii
    第一章 導論 1
    1.1. 研究背景 1
    1.2. 研究動機與目的 3
    1.3. 心臟傳導系統 4
    1.4. 十二導程心電圖概述 5
    1.5. 十二導程心電圖資料數據 6
    1.5.1. 高醫的心電圖資料及性別年齡數據 6
    1.5.2. 中國生理訊號挑戰賽公開資料庫 8
    1.5.3. 將高醫的研究數據庫及中國生理訊號挑戰賽公開資料庫混合 9
    1.6. 論文架構 9
    1.7. 論文貢獻 10
    第二章 文獻探討 11
    第三章 研究方法 16
    3.1. 相關前置處理 17
    3.1.1. 心電圖資料數位化 17
    3.1.2. 醫生進行心電圖標記 21
    3.1.3. 去除標記中的異常資料 23
    3.2. 深度學習演算法介紹 26
    3.2.1. 卷積神經網路 26
    3.2.2. 多尺度卷積神經網路 29
    3.2.3. 循環神經網路 30
    3.2.4. 卷積循環神經網路 32
    3.2.5. 兩階段式的多尺度深度學習模型 33
    3.3. 心電訊號特徵分析 36
    3.4. 本章小結 44
    第四章 實驗結果 45
    4.1. 實驗測量指標 45
    4.2. 高醫的研究數據庫所提供之十二導程心電圖 46
    4.3. 中國生理訊號挑戰賽公開資料庫 50
    4.4. 將高醫的研究數據庫及挑戰賽公開資料庫混合訓練 51
    4.5. 本章小結 52
    第五章 結論與未來展望 53
    5.1. 結論 53
    5.2. 未來研究方向 53
    參考文獻 54
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    口試委員
  • 吳志宏 - 召集委員
  • 侯俊良 - 委員
  • 歐陽振森 - 委員
  • 蔡維中 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2020-07-23 繳交日期 2020-07-21

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