本文選題：Android　切入點：心電圖　出處：《杭州電子科技大學》2017年碩士論文　論文類型：學位論文

【摘要】：心血管疾病突發(fā)性高,且致死致殘率也極高,每年死于心臟病突發(fā)的人數占了死亡人數的1/3。突發(fā)性疾病最好的控制方法是長期監(jiān)測,做到早期預防�；颊呷暨x擇去醫(yī)院做長期的檢查,不僅就醫(yī)過程繁瑣,而且成本高,普通家庭難以承受。目前市場已有的家用式心電監(jiān)測儀,則存在體積龐大,不能進行本地診斷等不足之處。而基于移動平臺的心電監(jiān)測系統(tǒng),不僅降低了設備的成本,縮小了設備體積,而且能實現本地的心電分析以及遠程信息通信,將成為移動醫(yī)療產品的設計趨勢,因此文中選擇在Android系統(tǒng)上實現心電智能算法和終端軟件的開發(fā)。根據課題需求,首先設計整個系統(tǒng)的方案,分析本次開發(fā)的主要內容和需要解決的問題。文中具體介紹了心電智能檢測算法的設計和終端軟件的開發(fā)過程。心電智能檢測算法主要包括信號預處理、特征提取和分類,首先采用小波變換結合形態(tài)學算法對信號進行預處理,去除噪聲干擾,得到相對純凈的信號。然后通過K-means聚類算法提取QRS波群等特征參數,根據這些參數建立正常竇性心律和心律異常的正樣本和負樣本,最后結合極限學習機(Extreme Learning Machine,ELM)分類器對樣本進行訓練和匹配。文中以MIT-BIH心律異常數據庫中的數據作為分析對象,實驗結果證明文中提出的算法能準確診斷出室性早博(Premature Ventricular Contraction,PVC)和房性早搏(Atrial Premature Contraction,APC)。最終室性早博的陽性檢測率P+達到94.20%,檢測靈敏度Se達到96.30%,房性早搏的陽性檢測率P+達到98.02%,檢測靈敏度Se達到99%。算法經過測試驗證后,植入Android客戶端實現實時分析處理。Android客戶端軟件的功能設計包括藍牙接收、實時繪圖、用戶管理和界面設計等�？蛻舳藢⒎治龅玫降脑\斷結果,通過互聯網上傳至Web服務器。Web服務器實現實時響應客戶端的請求,將客戶端上傳的數據存入數據庫,或是讀取數據發(fā)送到客戶端,實現數據的管理維護。本系統(tǒng)最后經測試,操作簡單,運行穩(wěn)定,可擴展性好,后續(xù)可以按需擴展到網絡互傳,遠程診斷,這對心血管疾病的防治有積極的意義。
[Abstract]:Cardiovascular disease is sudden, and the rate of death and disability is extremely high. One third of the deaths from heart attacks occur every year. The best way to control sudden disease is to monitor it for a long time. To achieve early prevention. If patients choose to go to hospital for long-term examination, not only the process of seeking medical treatment is cumbersome, but also the cost is high, and it is difficult for ordinary families to bear it. At present, there is a huge volume of household ECG monitors that are available in the market. The ECG monitoring system based on mobile platform not only reduces the cost and volume of equipment, but also realizes local ECG analysis and remote information communication. It will become the design trend of mobile medical products, so we choose to develop ECG intelligent algorithm and terminal software on Android system. According to the demand of the subject, we first design the scheme of the whole system. This paper introduces the design of ECG intelligent detection algorithm and the development process of terminal software. ECG intelligent detection algorithm mainly includes signal preprocessing, feature extraction and classification. Firstly, wavelet transform combined with morphological algorithm is used to pre-process the signal to remove noise interference and get the relatively pure signal. Then K-means clustering algorithm is used to extract the characteristic parameters such as QRS wave group. According to these parameters, positive and negative samples of normal sinus rhythm and arrhythmia were established. Finally, the samples were trained and matched with extreme Learning machine classifier. The experimental results show that the proposed algorithm can accurately diagnose premature Ventricular PVCs and atrial premature beats. The positive detection rate of ventricular premature beats (P = 94.20), the sensitivity of se (96.30%) and the positive rate of atrial premature beats (P. The detection sensitivity is up to 99%. The algorithm has been tested and verified. The functional design of Android client software includes Bluetooth receiving, real-time drawing, user management and interface design. Through the Internet upload to the Web server. The web server can respond to the request of the client in real time, save the data uploaded by the client into the database, or read the data to the client to realize the management and maintenance of the data. Finally, the system is tested. The operation is simple, the operation is stable, and the expansibility is good. The follow-up can be extended to network transmission and remote diagnosis as needed, which has positive significance for the prevention and treatment of cardiovascular diseases.
【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.52;TP316
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本文編號：1580925