Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Analysis of QRS-wave Using Wavelet Transform of Electrocardiogram

웨이블릿 변환을 이용한 심전도의 QRS파 신호 분석

  • Choi, Chang-Hyun (Dept. of Bio-Mechatronic Engineering, Sungkyunkwan University) ;
  • Kim, Yong-Joo (Dept. of Bio-Mechatronic Engineering, Sungkyunkwan University) ;
  • Kim, Tae-Hyeong (Dept. of Bio-Mechatronic Engineering, Sungkyunkwan University) ;
  • Ahn, Yong-Hee (Dept. of Bio-Mechatronic Engineering, Sungkyunkwan University) ;
  • Shin, Dong-Ryeol (School of Information and Communication Engineering, Sungkyunkwan University)
  • Published : 2008.10.25
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Abstract

The electrocardiogram (ECG) measurement system consists of I/O interface to input the ECG signals from two electrodes, FPGA (Field programmable gate arrays) module to process the signal conditioning, and real time module to control the system. The algorithms based on wavelet transform were developed to remove the noise of the ECG signals and to determine the QRS-waves. Triangular wave tests were conducted to determine the optimal factors of the wavelet filter by analyzing the SNRs (signal to noise ratios) and RMSEs (root mean square errors). The hybrid rule, soft method, and symlets of order 5 were selected as thresholding rule, thresholding method, and mother wavelet, respectively. The developed wavelet filter showed good performance to remove the noise of the triangular waves with 10.98 dB of SNR and 0.140 mV of RMSE. The ECG signals from a total of 6 subjects were measured at different measuring postures such as lying, sitting, and standing. The durations of QRS-waves, the amplitudes of R-waves, the intervals of RR-waves were analyzed by using the finite impulse response (FIR) filter and the developed wavelet filter. The wavelet filter showed good performance to determine the features of QRS-waves, but the FIR filter had some problems to detect the peaks of Q and S waves. The measuring postures affected accuracy and precision of the ECG signals. The noises of the ECG signals were increased due to the movement of the subject during measurement. The results showed that the wavelet filter was a useful tool to remove the noise of the ECG signals and to determine the features of the QRS-waves.

Keywords

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