Objective: To evaluate the changes of human heart rate (HR) and heart rate variability (HRV) during and after moxa smoke inhalation and to investigate the effects of moxa smoke on human autonomic nervous system. Metho...Objective: To evaluate the changes of human heart rate (HR) and heart rate variability (HRV) during and after moxa smoke inhalation and to investigate the effects of moxa smoke on human autonomic nervous system. Methods: 24 healthy volunteers were exposed to moxa smoke with their HRV parameters measured before, during and after the moxa smoke inhalation. Results: The healthy volunteers exposed to moxa smoke had significant reductions in HR and also significant changes in HRV parameters. Conclusions: Moxa smoke can improve the autonomic nervous system activity. The inhalation of moxa smoke will induce a depressant effect on human body.展开更多
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
In this study,single-channel photoplethysmography(PPG)signals were used to estimate the heart rate(HR),diastolic blood pressure(DBP),and systolic blood pressure(SBP).A deep learning model was proposed using a long-ter...In this study,single-channel photoplethysmography(PPG)signals were used to estimate the heart rate(HR),diastolic blood pressure(DBP),and systolic blood pressure(SBP).A deep learning model was proposed using a long-term recurrent convolutional network(LRCN)modified from a deep learning algorithm,the convolutional neural network model of the modified inception deep learning module,and a long short-term memory network(LSTM)to improve the model’s accuracy of BP and HR measurements.The PPG data of 1,551 patients were obtained from the University of California Irvine Machine Learning Repository.How to design a filter of PPG signals and how to choose the loss functions for deep learning model were also discussed in the study.Finally,the stability of the proposed model was tested using a 10-fold cross-validation,with an MAE±SD of 2.942±5.076 mmHg for SBP,1.747±3.042 mmHg for DBP,and 1.137±2.463 bpm for the HR.Compared with its existing counterparts,the model entailed less computational load and was more accurate in estimating SBP,DBP,and HR.These results established the validity of the model.展开更多
文摘Objective: To evaluate the changes of human heart rate (HR) and heart rate variability (HRV) during and after moxa smoke inhalation and to investigate the effects of moxa smoke on human autonomic nervous system. Methods: 24 healthy volunteers were exposed to moxa smoke with their HRV parameters measured before, during and after the moxa smoke inhalation. Results: The healthy volunteers exposed to moxa smoke had significant reductions in HR and also significant changes in HRV parameters. Conclusions: Moxa smoke can improve the autonomic nervous system activity. The inhalation of moxa smoke will induce a depressant effect on human body.
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).
基金This study was supported in part by the Ministry of Science and Technology MOST108-2221-E-150-022-MY3 and Taiwan Ocean University.
文摘In this study,single-channel photoplethysmography(PPG)signals were used to estimate the heart rate(HR),diastolic blood pressure(DBP),and systolic blood pressure(SBP).A deep learning model was proposed using a long-term recurrent convolutional network(LRCN)modified from a deep learning algorithm,the convolutional neural network model of the modified inception deep learning module,and a long short-term memory network(LSTM)to improve the model’s accuracy of BP and HR measurements.The PPG data of 1,551 patients were obtained from the University of California Irvine Machine Learning Repository.How to design a filter of PPG signals and how to choose the loss functions for deep learning model were also discussed in the study.Finally,the stability of the proposed model was tested using a 10-fold cross-validation,with an MAE±SD of 2.942±5.076 mmHg for SBP,1.747±3.042 mmHg for DBP,and 1.137±2.463 bpm for the HR.Compared with its existing counterparts,the model entailed less computational load and was more accurate in estimating SBP,DBP,and HR.These results established the validity of the model.
