Photoplethysmogram(PPG)is a noninvasive method for detecting human cardiovascular pulse wave using optical technology.The PPG containing a lot of physiological information is from the MIMIC database.This paper propose...Photoplethysmogram(PPG)is a noninvasive method for detecting human cardiovascular pulse wave using optical technology.The PPG containing a lot of physiological information is from the MIMIC database.This paper proposes a combinatorial method of ensemble empirical mode decomposition(EEMD),cepstrum,fast Fourier transform(FFT)and zero-crossing detection to improve the robustness of the estimation of pulse rate(PR),heart rate(HR)and respiratory rate(RR)from the PPG.First,the PPG signal was decomposed into finite intrinsic mode functions(IMF)by EEMD.Because of its adaptive filtering property,the different signals were reconstructed using different IMFs when estimating different physiological parameters.Second,the PR was obtained by zero-crossing detection after rejecting low frequency IMFs containing artifacts.Third,IMFs with frequency between 1.00 Hz to 1.67 Hz(60 beats/min to 100 beats/min)were selected for estimating HR.Then,the frequency band that reflects the heart activity was analyzed by the cepstrum method.Finally,the respiratory signal can be extracted from PPG signal by IMFs with frequency between 0.05 Hz to 0.75 Hz(3 breahts/min to 45 breaths/min).Then the spectrum of signal was obtained by FFT analysis and the RR was estimated by detecting the maximum frequency peak.The algorithm has been tested on MIMIC database obtained from 53 adults.The experiment results show that the physiological parameters extracted by this integrated signal processing method are consistent with the real physiological parameters.And the computation load of this method is small and the precision is high(not larger than 1.17%in error).展开更多
文摘Photoplethysmogram(PPG)is a noninvasive method for detecting human cardiovascular pulse wave using optical technology.The PPG containing a lot of physiological information is from the MIMIC database.This paper proposes a combinatorial method of ensemble empirical mode decomposition(EEMD),cepstrum,fast Fourier transform(FFT)and zero-crossing detection to improve the robustness of the estimation of pulse rate(PR),heart rate(HR)and respiratory rate(RR)from the PPG.First,the PPG signal was decomposed into finite intrinsic mode functions(IMF)by EEMD.Because of its adaptive filtering property,the different signals were reconstructed using different IMFs when estimating different physiological parameters.Second,the PR was obtained by zero-crossing detection after rejecting low frequency IMFs containing artifacts.Third,IMFs with frequency between 1.00 Hz to 1.67 Hz(60 beats/min to 100 beats/min)were selected for estimating HR.Then,the frequency band that reflects the heart activity was analyzed by the cepstrum method.Finally,the respiratory signal can be extracted from PPG signal by IMFs with frequency between 0.05 Hz to 0.75 Hz(3 breahts/min to 45 breaths/min).Then the spectrum of signal was obtained by FFT analysis and the RR was estimated by detecting the maximum frequency peak.The algorithm has been tested on MIMIC database obtained from 53 adults.The experiment results show that the physiological parameters extracted by this integrated signal processing method are consistent with the real physiological parameters.And the computation load of this method is small and the precision is high(not larger than 1.17%in error).
