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                    基于確定學習及心電動力學圖的心肌缺血早期檢測研究

                    孫慶華 王磊 王聰 王乾 吳偉明 趙媛媛 王喜萍 董瀟男 周彬 唐閩

                    孫慶華, 王磊, 王聰, 王乾, 吳偉明, 趙媛媛, 王喜萍, 董瀟男, 周彬, 唐閩. 基于確定學習及心電動力學圖的心肌缺血早期檢測研究. 自動化學報, 2020, 46(9): 1908?1926 doi: 10.16383/j.aas.c190899
                    引用本文: 孫慶華, 王磊, 王聰, 王乾, 吳偉明, 趙媛媛, 王喜萍, 董瀟男, 周彬, 唐閩. 基于確定學習及心電動力學圖的心肌缺血早期檢測研究. 自動化學報, 2020, 46(9): 1908?1926 doi: 10.16383/j.aas.c190899
                    Sun Qing-Hua, Wang Lei, Wang Cong, Wang Qian, Wu Wei-Ming, Zhao Yuan-Yuan, Wang Xi-Ping, Dong Xiao-Nan, Zhou Bin, Tang Min. Early detection of myocardial ischemia based on deterministic learning and cardiodynamicsgram. Acta Automatica Sinica, 2020, 46(9): 1908?1926 doi: 10.16383/j.aas.c190899
                    Citation: Sun Qing-Hua, Wang Lei, Wang Cong, Wang Qian, Wu Wei-Ming, Zhao Yuan-Yuan, Wang Xi-Ping, Dong Xiao-Nan, Zhou Bin, Tang Min. Early detection of myocardial ischemia based on deterministic learning and cardiodynamicsgram. Acta Automatica Sinica, 2020, 46(9): 1908?1926 doi: 10.16383/j.aas.c190899

                    基于確定學習及心電動力學圖的心肌缺血早期檢測研究

                    doi: 10.16383/j.aas.c190899
                    基金項目: 國家重大科研儀器研制項目(61527811), 廣州市科技計劃項目(201704020078), 八師石河子市科技計劃項目(2018TD03)資助
                    詳細信息
                      作者簡介:

                      孫慶華:華南理工大學自動化科學與工程學院博士研究生. 主要研究方向為確定學習理論、動態模式識別及其在心肌缺血/心肌梗死/冠心病檢測上的應用. E-mail: ausunqinghua@mail.scut.edu.cn

                      王磊:石河子市人民醫院(石河子大學醫學院第三附屬醫院)心內科主治醫師. 主要研究方向為冠心病. 共同第一作者. E-mail: wangleishitoukang@163.com

                      王聰:山東大學控制科學與工程學院、山東大學智能醫學工程研究中心教授. 主要研究方向為動態環境機器學習與模式識別, 確定學習理論, 基于模式的智能控制, 振動故障診斷及在醫學領域的應用研究. 本文通信作者. E-mail: wangcong@sdu.edu.cn

                      王乾:山東大學控制科學與工程學院博士后. 主要研究方向為確定學習, 故障診斷與健康預測. E-mail: auwangqian@sdu.edu.cn

                      吳偉明:華南理工大學自動化科學與工程學院博士研究生. 主要研究方向為系統辨識, 確定學習, 動態模式識別. E-mail: auwuweiming@163.com

                      趙媛媛:石河子市人民醫院(石河子大學醫學院第三附屬醫院)副主任護師. 主要研究方向為急性心肌梗死患者的護理.E-mail: zyy457027952@163.com

                      王喜萍:石河子市人民醫院(石河子大學醫學院第三附屬醫院)心內科主任醫師. 主要研究方向為冠心病.E-mail: wangxiping1567@163.com

                      董瀟男:中國醫學科學院阜外醫院醫師. 主要研究方向為心律失常的診斷和介入治療. E-mail: guitardxn@163.com

                      周彬:中國醫學科學院阜外醫院博士研究生. 主要研究方向為心律失常. E-mail: zhoubinxhfw@163.com

                      唐閩:中國醫學科學院阜外醫院主任醫師. 主要研究方向為各種器質性心臟病、先天性心臟病和心功能不全合并心律失常的診療, 尤其是心房顫動、心房撲動、房性心動過速、室性早搏、室性心動過速、陣發性室上性心動過速等復雜心律失常的射頻消融治療和起搏器電極拔除治療. 本文共同通信作者. E-mail: doctortangmin@hotmail.com

                    Early Detection of Myocardial Ischemia Based on Deterministic Learning and Cardiodynamicsgram

                    Funds: Supported by National Major Scientific Instruments Development Project (61527811), the Science and Technology Program of Guangzhou (201704020078), and the Science and Technology Program of Shihezi (2018TD03)
                    • 摘要: 心肌缺血早期檢測是心血管疾病領域重要且困難的問題. 本文采用心電動力學圖(Cardiodynamicsgram, CDG)開展心電圖正常及大致正常時的心肌缺血早期檢測研究. 1) 在分析已有基于心電圖的心肌缺血檢測方法所取得的進展及不足基礎上, 構建一個既有心電圖發生缺血性改變、又有心電圖正常及大致正常、且包括經冠脈造影檢驗為冠脈阻塞性病變和非阻塞性病變的較大規模心肌缺血數據集. 2) 針對上述數據集中393例心電圖正常及大致正?;颊? 利用確定學習生成每份心電圖的心電動力學圖, 提取對心肌缺血和非缺血具有顯著區分能力的心電動力學特征. 并以冠脈狹窄$ \ge$50%為缺血標準, 采用機器學習算法構建心肌缺血檢測模型. 3) 針對上述試驗中假陽性病例, 利用由確定學習生成的具有明確物理意義的心電動力學圖進行逐例分析, 發現其中許多假陽性存在慢血流現象(即冠脈非阻塞性病變). 對這些慢血流病例重新進行缺血標注, 以改善心肌缺血數據集標注精度. 通過上述三個步驟構建了更為準確的心肌缺血檢測模型, 其缺血檢測結果: 靈敏度90.1%、特異度85.2%、準確率89.0%和受試者工作特征曲線(Receiver operating characteristic curve, ROC)下面積(Area under curve, AUC) 0.93. 綜上, 本文所構建的較大規模心肌缺血數據集可為心肌缺血檢測研究和臨床研究提供重要的數據基礎; 且構建的心肌缺血檢測模型對心電圖正常及大致正?;颊呔哂休^強的缺血檢測能力; 特別是, 由確定學習生成的心電動力學圖具有較好的可解釋性, 有助于發現缺血數據標注的偏差和模型的錯誤, 提高心肌缺血檢測準確率.
                    • 圖  1  心肌缺血病因及臨床類型

                      Fig.  1  The causes and clinical presentation of myocardial ischemia

                      圖  2  心肌缺血診斷方法

                      Fig.  2  Diagnostic methods of myocardial ischemia

                      圖  3  典型的心電圖[34]

                      Fig.  3  A standard electrocardiogram (ECG)[34]

                      圖  4  一例心肌缺血患者的心電動力學圖及CDG值

                      Fig.  4  The CDG and CDG value of a patient with myocardial ischemia

                      圖  5  冠脈狹窄與非狹窄組間的CDG值差異($ \lozenge $: $ p<0.01 $存在差異有高度統計顯著性; $ \bigstar $: 超出邊界的實例.)

                      Fig.  5  Differences of CDG values between coronary stenosis and non-stenosis groups ($ \lozenge $: $ p<0.01 $ was considered as statistically significant. $ \bigstar $: subjects that were out of boundaries.)

                      圖  6  心電動力學圖的心肌缺血檢測結果

                      Fig.  6  Results of myocardial ischemia detection via CDG

                      圖  7  一例冠脈單支病變男性患者, 55歲 ((a) 正常心電圖; (b) 心電動力學圖散亂;(c) 冠脈前降支存在80%狹窄;(d) CDG值陽性)

                      Fig.  7  A case of ischemic male patient with single vessel disease, 55 years old ((a) Nondiagnostic ECG; (b) Irregular CDG; (c) The left anterior descending branch of the coronary artery is with stenosis 80%; (d) The positive CDG value)

                      圖  8  一例冠脈雙支病變男性患者, 35歲 ((a) 正常心電圖; (b) 心電動力學圖散亂; (c) 冠脈回旋支中段50%狹窄,右冠近段100%狹窄; (d) CDG值陽性)

                      Fig.  8  A case of ischemic male patient with double-vessel disease, 35 years old ((a) Nondiagnostic ECG; (b) Irregular CDG; (c) The middle segment of the left circumflex artery is with stenosis 50% narrow, and the proximal segment of the right coronary artery is occluded; (d) The positive CDG value)

                      圖  9  一例冠脈三支病變男性患者, 50歲 ((a) 正常心電圖; (b) 心電動力學圖散亂; (c) 中間支開口90%狹窄, 回旋支遠段80%局限狹窄, 右冠遠段90%局限狹窄; (d) CDG值陽性)

                      Fig.  9  A case of ischemic male patient with triple-vessel disease, 50 years old ((a) Nondiagnostic ECG; (b) Irregular CDG; (c) The opening of the middle branch is 90% narrow, the distal segment of the left circumflex artery is with stenosis 80%, and the distal segment of the right coronary artery is with stenosis 90%; (d) The positive CDG value)

                      圖  10  一例非缺血女性患者, 47歲 ((a) 正常心電圖; (b) 心電動力學圖較為規整; (c) 正常冠脈; (d) CDG值陰性)

                      Fig.  10  A case of nonischemic female patient, 47 years old ((a) Normal ECG; (b) Regular CDG; (c) Normal coronary angiography; (d) The negative CDG value)

                      圖  11  一例非缺血男性患者, 47歲 ((a) 正常心電圖; (b) 心電動力學圖規整; (c) 正常冠脈; (d) CDG值陰性)

                      Fig.  11  A case of nonischemic male patient, 47 years old ((a) Normal ECG; (b) Regular CDG; (c) Normal coronary angiography; (d) The negative CDG value)

                      圖  12  不同缺血標注精度下分類模型的ROC曲線

                      Fig.  12  ROC curves of classification models at different accuracy of ischemic labeling

                      圖  13  一例慢血流男性患者, 48歲 ((a) 正常心電圖; (b) 心電動力學圖散亂; (c) 冠脈無狹窄前降支慢血流; (d) CDG值陽性)

                      Fig.  13  A case of ischemic male patient with slow coronary flow, 48 years old ((a) Normal ECG; (b) Irregular CDG; (c) The left anterior descending branch of the coronary artery is with coronary slow flow; (d) The positive CDG value)

                      圖  14  一例慢血流女性患者, 50歲 ((a) 正常心電圖; (b) 心電動力學圖散亂; (c) 冠脈無狹窄但前降支中段第一對角支慢血流; (d) CDG值陽性)

                      Fig.  14  A case of ischemic female patient with slow coronary flow, 50 years old ((a) Normal ECG; (b) Irregular CDG; (c) Coronary slow flow in the first diagonal branch of the coronary artery; (d) The positive CDG value)

                      表  1  疑似心肌缺血患者病例信息記錄

                      Table  1  A case of suspected myocardial ischemic patient

                      項目信息記錄
                      編號/來源SHZ2944/石河子市人民醫院
                      年齡/性別59/男
                      心率/血壓68 (次/分)/200 (高), 100 (低) (mmHg)
                      主訴半月前無誘因再次出現胸骨中下段拳頭大小范圍壓迫樣疼痛, 伴胸悶、心慌、出汗, 癥狀持續數分鐘休息后緩解, 癥狀頻繁發作, 偶有靜息下發作
                      既往史平素健康狀況一般, 高血壓 30 年, 最高達 200/100 mmHg, 無其他病史
                      心電圖竇性心律, 偶發室早, T波改變
                      冠脈造影前降支近段斑塊; 回旋支近段斑塊、遠段 100% 閉塞, 可見前降支到回旋支側枝形成; 右冠中段 80% 病變, 遠段 90% 彌漫性病變
                      臨床診斷1) 冠心病, 不穩定性心絞痛; 2) 高血壓 3 級 (很高危)
                      下載: 導出CSV

                      表  2  自建數據集與PTB數據集對比

                      Table  2  Comparison between PTB diagnostic dataset and the proposed dataset

                      來源PTB自建
                      總病例數290781
                      缺血病例148700
                      非缺血病例5281
                      心電圖基本發生缺血性改變393 例正?;蚍翘禺愋愿淖?/td>
                      缺血病因冠脈狹窄冠脈狹窄、慢血流
                      下載: 導出CSV

                      表  3  心電圖正?;虼笾抡;颊叩娜丝诨€特征

                      Table  3  Baseline characteristics of patients with normal or nearly normal ECG

                      類型冠脈狹窄 (299)非冠脈狹窄 (n = 94)p 值
                      冠脈慢血流 (13)非冠脈病變 (81)
                      性別 (男性)216/299 (72.2%)9/13 (69.2%)43/81 (53.7%)0.005**
                      年齡58±1056±954±100.022*
                      收縮壓 (mmHg)129±10131±18127±140.563
                      舒張壓 (mmHg)77±1085±1477±100.109
                      心率 (beats/min)72±1065±671±100.012*
                      高血壓171/299 (57.2%)10/13 (76.9%)42/81 (51.9%)0.226
                      糖尿病88/299 (29.4%)6/13 (46.2%)18/81 (22.2%)0.159
                      血脂異常190/299 (63.5%)8/13 (61.5%)53/81 (65.4%)0.937
                      注: 所有數據采用軟件 SPSS 21.0 進行統計分析; 計量資料采用 Mann-Whitney 秩和檢驗, 表示為 (均值±標準差); 計數資料采用卡方檢驗, 用%表示; *: p < 0.05為差異有統計顯著性; **: p < 0.01為差異有高度統計顯著性.
                      下載: 導出CSV

                      表  4  不同缺血標注精度下, 心電動力學圖的缺血檢測結果

                      Table  4  The results of CDG in the detection of ischemia at different precision of ischemia labeling

                      缺血標準靈敏度 (%)特異度 (%)準確率 (%)AUC
                      冠脈狹窄85.182.687.80.88
                      冠脈狹窄及慢血流90.185.289.00.93
                      下載: 導出CSV

                      表  5  本文方法與文獻中的方法在PTB數據集上的心肌缺血檢測結果對比

                      Table  5  Comparison of the CDG against the related literatures about myocardial ischemia detection

                      方法數據方法特點特征數分類器性能 (%)
                      準確率敏感度特異度
                      Sharma等 (2015)[20]導聯: 12 導聯心電記錄: 148 MI, 52 HC多尺度小波能量特征72KNN/SVM96.0093.0099.00
                      Han等 (2019)[15]導聯: 12 導聯心電記錄: 28 213 MI,
                      5 373 HC
                      能量熵; 形態學特征22SVM92.6980.9680.96
                      Diker等 (2018[17]導聯: 不可知心電信號: 148 MI, 52 HC形態學特征; 時域特征;
                      離散小波變換特征
                      9SVM87.8086.9788.67
                      Sharma等 (2018)[18]導聯: II、III、aVF 導聯心電信號: 3 240
                      下壁 MI, 3 037 HC
                      樣本熵; 歸一化子帶能量;
                      對數能量熵; 中值斜率
                      10KNN/SVM81.7179.0179.26
                      Acharya等 (2017)[27]導聯: II 導聯心拍: 40 182 MI, 10 546 HC卷積神經網絡?全連接網絡95.2295.4994.19
                      Han等 (2020)[29]導聯: 12 導聯心電記錄: 17 212 MI,
                      6 945 HC
                      多導聯殘差網絡?全連接 softmax95.4994.8597.37
                      本文方法導聯: 12 導聯心電記錄: 148 MI, 52 HC心電動力學圖特征2SVM-Linear97.0098.6592.31
                      下載: 導出CSV
                      360彩票
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                    • 收稿日期:  2019-12-31
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