| Summary: | 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.
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