1.Development of portable efficacy detector for battlefield treatment training
Zhigang FU ; Duyan GENG ; Na JIA ; Chunhong DIAO ; Huan SONG ; Chen ZHANG
Chinese Medical Equipment Journal 2017;38(2):32-34,44
Objective To develop a portable pressure detector to facilitate the battlefield self and buddy aids training for dressing,hemostasis and fixation.Methods The changes of pressure were converted into the ones of electric current with the pneumatic cuff,catheter and membrane pressure sensor,and then transmitted to the panel display by Bluetooth.The efficacy for the training was determined based on the acquired data.Results The detector implemented quantifying of the pressures during dressing,hemostasis and fixation,and non-medical staff obtained the results of battlefield treatment training easily to execute rapid assessment of battlefield self and buddy aids training.Conclusion The device gains advantages in visualized data,portability,easy operation and accurate measurement,and contributes to battlefield self and buddy aids training.
2. Influence of extremely low-frequency magnetic field on circadian rhythm of cryptochrome in mouse embryonic fibroblasts
Zhaoyu SUN ; Duyan GENG ; Chuanfang CHEN ; Pingping WANG ; Tao SONG
Chinese Journal of Industrial Hygiene and Occupational Diseases 2017;35(6):459-462
Objective:
To investigate the influence of extremely low-frequency magnetic field on periodical expression of cryptochrome (
3.Research on heart rate extraction algorithm in motion state based on normalized least mean square combining ensemble empirical mode decomposition.
Duyan GENG ; Jie ZHAO ; Chenxu WANG ; Jiaji DONG ; Qi NING ; Yan WANG
Journal of Biomedical Engineering 2020;37(1):71-79
In order to eliminate the influence of motion artifacts, high-frequency noise and baseline drift on photoplethysmographic (PPG), and to obtain the accurate value of heart rate in motion state, this paper proposed a de-noising method of PPG signal based on normalized least mean square (NLMS) adaptive filtering combining ensemble empirical mode decomposition(EEMD). Firstly, the PPG signal containing noise is passed through an adaptive filter with a 3-axis acceleration sensor as a reference signal to filter out motion artifacts. Secondly, the PPG signal is decomposed by EEMD to obtain a series of intrinsic modal function (IMF) according to the frequency from high to low. The threshold range of the signal is judged by the permutation entropy (PE) criterion, thereby filtering out the high frequency noise and the baseline drift. The experimental results show that the Pearson correlation coefficient between the calculated heart rate of PPG signal and the standard heart rate based on electrocardiogram (ECG) signal is 0.731 and the average absolute error percentage is 6.10% under different motion states, which indicates that the method can accurately calculate the heart rate in moving state and is beneficial to the physiological monitoring under the state of human motion.