A Portable Real-time ECG Recognition System Based onSmartphone

• Main Authors: Tzu-Hao Yen, 顏子豪
• Other Authors: Sung-Nien Yu
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/55440724202241293323

碩士 === 國立中正大學 === 電機工程研究所 === 101 === This paper proposed an smartphone-based real-time ECG monitoring and recognition system toassist the physicians in heart disease diagnosis. ECG measurement usually requires the patients to carry a device, The recorded ECG signals are then brought back to the hospital to be examined by the physicians. This process would take a long period of time and some mistakes or ignorance of minor signs could be made. These issues give rise to the requisite of portable ECG recording and recognition system. This system use bluetooth to receive ECG data and computing algorithms on the Android platform. It is divided into some functional blocks, include signal acquisition, R-point detection, feature calculation, classification and result display on the screen ECG data from the highly recognized MIT-BIH database were selected as the test signals. The data were transformed in to analog signals using the I/O card. Then the low power MSP430FG4618 module was used to perform the A-to-D transform. The digitized 12-bit data were transmitted to the smartphone through Bluetooth. We remove the noise through a bandpass filter. Then, the improved R-point localization algorithm was used to locate the R points of the heartbeats. A 64-point QRS segment centered at the R point was extracted. A five-level discrete wavelet transformation was used to decompose the segment into different subband components. 27 features calculated from higher-order statistics were extracted based on the components. Four RR-interval related feature were also used. In addition, Four over-sampling profiles combined with the proposed two-stage classifier were tested to verify performance of the algorithm, The optimal weights were downloaded onto the smartphone as the final version of the real-time classifier. This system achieved a high accuracy of 97.46% in identifying seven heartbeat types on the smartphone, The heartbeat types were recognized in real-time; only 3.825 ms was required to identify a heartbeat. The portability, real-time processing, and high recognition rate of the system demonstrate the efficiency and effectiveness of the device as a practical computer-aided diagnosis (CAD) system.