A new non-invasive blood glucose measuring apparatus (NBGMA) made up of MSP430F149 SCM (single chip micyoco) was developed,which can measure blood glucose level (BGL) frequently,conveniently and painlessly. The hardwa...A new non-invasive blood glucose measuring apparatus (NBGMA) made up of MSP430F149 SCM (single chip micyoco) was developed,which can measure blood glucose level (BGL) frequently,conveniently and painlessly. The hardware and software of this apparatus were designed,and detecting algorithms based on conservation of energy method (COEM) were presented. According to the law of conservation of energy that the energy derived by human body equals energy consumed by metabolism,and the relationship between convection,evaporation,radiation and the BGL was established. The sensor module was designed. 20 healthy volunteers were involved in the clinical experiment. The BGL measured by an automatic biochemical analyzer (ABA) was set as the reference. Regression analysis was performed to compare the conservation of energy method with the biochemical method,using the 20 data points with blood glucose concentrations ranging from 680 to 1 100 mg/L. Reproducibility was measured for healthy fasting volunteers. The results show that the means of BGL detected by NBGMA and ANA are very close to each other,and the difference of standard deviation (SD) is 24.7 mg/L. The correlative coefficient is 0.807. The coefficient of variation (CV) is 4% at 921.6 mg/L. The resultant regression is evaluated by the Clarke error grid analysis (EGA) and all data points are included in the clinically acceptable regions (region A:100%,region B:0%). Accordingly,it is feasible to measure BGL with COEM.展开更多
This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography(PPG)sensors and a deep learning(DL)that can be used for continuous and rapid measurement of blood ...This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography(PPG)sensors and a deep learning(DL)that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators.The proposed platform measured the signal changes in PPG and converted them into physiological indicators,such as pulse transit time(PTT),pulse wave velocity(PWV),perfusion index(PI)and heart rate(HR);these indicators were then fed into the DL to calculate blood pressure.The hardware of the experiment comprised 2 PPG components(i.e.,Raspberry Pi 3 Model B and analog-todigital converter[MCP3008]),which were connected using a serial peripheral interface.The DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure(SBP),diastolic blood pressure(DBP)and mean arterial pressure(MAP).To increase the robustness of the DL model,this study input data of 100 Asian participants into the training database,including those with and without cardiovascular disease,each with a proportion of approximately 50%.The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17±0.46 mmHg.The mean absolute error and standard deviation of DBP was 0.27±0.52 mmHg.The mean absolute error and standard deviation of MAP was 0.16±0.40 mmHg.展开更多
基金Project(07JJ6133) supported by the Natural Science Foundation of Hunan Province, China
文摘A new non-invasive blood glucose measuring apparatus (NBGMA) made up of MSP430F149 SCM (single chip micyoco) was developed,which can measure blood glucose level (BGL) frequently,conveniently and painlessly. The hardware and software of this apparatus were designed,and detecting algorithms based on conservation of energy method (COEM) were presented. According to the law of conservation of energy that the energy derived by human body equals energy consumed by metabolism,and the relationship between convection,evaporation,radiation and the BGL was established. The sensor module was designed. 20 healthy volunteers were involved in the clinical experiment. The BGL measured by an automatic biochemical analyzer (ABA) was set as the reference. Regression analysis was performed to compare the conservation of energy method with the biochemical method,using the 20 data points with blood glucose concentrations ranging from 680 to 1 100 mg/L. Reproducibility was measured for healthy fasting volunteers. The results show that the means of BGL detected by NBGMA and ANA are very close to each other,and the difference of standard deviation (SD) is 24.7 mg/L. The correlative coefficient is 0.807. The coefficient of variation (CV) is 4% at 921.6 mg/L. The resultant regression is evaluated by the Clarke error grid analysis (EGA) and all data points are included in the clinically acceptable regions (region A:100%,region B:0%). Accordingly,it is feasible to measure BGL with COEM.
基金This study was supported in part by the Ministry of Science and Technology MOST 108-2221-E-150-022-MY3 and Taiwan Ocean University.
文摘This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography(PPG)sensors and a deep learning(DL)that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators.The proposed platform measured the signal changes in PPG and converted them into physiological indicators,such as pulse transit time(PTT),pulse wave velocity(PWV),perfusion index(PI)and heart rate(HR);these indicators were then fed into the DL to calculate blood pressure.The hardware of the experiment comprised 2 PPG components(i.e.,Raspberry Pi 3 Model B and analog-todigital converter[MCP3008]),which were connected using a serial peripheral interface.The DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure(SBP),diastolic blood pressure(DBP)and mean arterial pressure(MAP).To increase the robustness of the DL model,this study input data of 100 Asian participants into the training database,including those with and without cardiovascular disease,each with a proportion of approximately 50%.The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17±0.46 mmHg.The mean absolute error and standard deviation of DBP was 0.27±0.52 mmHg.The mean absolute error and standard deviation of MAP was 0.16±0.40 mmHg.
