透過您的圖書館登入
IP:18.119.141.169
  • 學位論文

架構於Android行動平台之心律異常偵測系統

Development of an Android-Based Heart Rate Abnormality Detection System

指導教授 : 譚旦旭
若您是本文的作者,可授權文章由華藝線上圖書館中協助推廣。

摘要


現代人生活壓力大且步調緊湊,導致罹患心血管疾病的民眾日益增多,如何利用科技產品協助民眾偵測自己的健康狀態是十分值得探討的議題。本研究應用Android-based行動裝置,結合心電訊號擷取電路、心臟QRS波偵測,以及心律異常偵測演算法,發展一套可攜式心律異常偵測系統,除供一般民眾隨時偵測自己的健康狀態,更可供心血管疾病患者及早發現心律異常徵兆,因而避免猝死危機。此系統利用MIT-BIH資料庫偵測包括心房早期收縮心跳(PACB)、心室早期收縮心跳(PVCB)、融合心跳(FB)、第二度房室傳導阻滯(2o A-V Block)、心室跳脫心跳(VB)等五種心律異常以及其他類之症狀,結果顯示本系統偵測準確率可達97 %,因此具備實用潛力。

並列摘要


Nowadays, a great number of people suffer from cardiovascular diseases due to busy work and big life pressures. Therefore, how to apply technology to help people monitor their health has received much attention in recent years. This study aims to develop a heart rate abnormality detection system by using Android-based mobile devices, which consists of ECG acquisition circuit, QRS complex detection, and heart rate abnormality detection algorithms. This system can not only help people monitor their health anytime and anywhere, but also detect their symptoms of heart rate abnormalities as early as possible, thus avoiding sudden death. The proposed system has been verified by employing the MIT-BIH database to detect 5 categories of arrhythmia, which are Premature Atrial Contraction Beat (PACB), Premature Ventricular Contraction Beat (PVCB), Fusion of Paced and Normal Beat (FB), Second-Degree Atrioventricular Block (2o A-V Block), and Ventricular Escape Beats (VB) and achieved a detection accuracy of 97 %, which demonstrates practical potential of the proposed system.

參考文獻


[23] 陳星同,「架構於個人電腦之心電訊號監測系統實作」,國立台北科技大學電機工程系碩士論文,2007。
[1] World Health Organization, Media Center Cardiovascular, http://www.who.int/.
[5] A. S. M. Mosa, I. Yoo, and L. Sheets, “A Systematic Review of Healthcare Applications for Smartphones,” BMC Medical Informatics and Decision Making, vol. 12, no. 1, pp. 67-98, 2012.
[6] T. H. Tan et al., “Development of a Portable Linux-Based ECG Measurement and Monitoring System,” Journal of Medical Systems, vol. 35, Issue 4, pp. 559-569, 2011.
[7] A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals,” Circulation, vol. 101, no. 23, pp. e215-e220, Jun. 2000.

延伸閱讀