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)

    電子論文:使用者自訂權限:校內 5 年後、校外 5 年後公開

    論文語文/頁數 中文/70
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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。
    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%.
  • 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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  • 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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