Diabetes imposes a substantial burden on global healthcare systems.Worldwide,nearly half of individuals with diabetes remain undiagnosed,while conventional diagnostic techniques are often invasive,painful,and expensiv...Diabetes imposes a substantial burden on global healthcare systems.Worldwide,nearly half of individuals with diabetes remain undiagnosed,while conventional diagnostic techniques are often invasive,painful,and expensive.In this study,we propose a noninvasive approach for diabetes detection using photoplethysmography(PPG),which is widely integrated into modern wearable devices.First,we derived velocity plethysmography(VPG)and acceleration plethysmography(APG)signals from PPG to construct multi-channel waveform representations.Second,we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal,statistical,and nonlinear features with recursive feature elimination and deep feature extraction using a one-dimensional statistical convolutional neural network(1DSCNN).Finally,we developed an interpretable diabetes detection method based on XGBoost,with explainable artificial intelligence(XAI)techniques.Specifically,SHapley Additive exPlanations(SHAP)and Local InterpretableModel-agnostic Explanations(LIME)were employed to identify and interpret potential digital biomarkers associated with diabetes.To validate the proposed method,we extended the publicly available Guilin People’s Hospital dataset by incorporating in-house clinical data from ten subjects,thereby enhancing data diversity.A subject-independent cross-validation strategy was applied to ensure that the testing subjects remained independent of the training data for robust generalization.Compared with existing state-of-the-art methods,our approach achieved superior performance,with an area under the curve(AUC)of 80.5±15.9%,sensitivity of 77.2±7.5%,and specificity of 64.3±18.2%.These results demonstrate that the proposed approach provides a noninvasive,interpretable,and accessible solution for diabetes detection using PPG signals.展开更多
Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body.Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes,where abn...Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body.Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes,where abnormal hemoglobin levels can indicate significant health issues.Traditional methods for hemoglobin measurement are invasive,causing pain,risk of infection,and are less convenient for frequent monitoring.PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure,sleep,blood glucose,and stress analysis.In this work,we propose a hemoglobin estimation method using an adaptive lightweight convolutional neural network(HMALCNN)from PPG.The HMALCNN is designed to capture both fine-grained local waveform characteristics and global contextual patterns,ensuring robust performance across acquisition settings.We validated our approach on two multi-regional datasets containing 152 and 68 subjects,respectively,employing a subjectindependent 5-fold cross-validation strategy.The proposed method achieved root mean square errors(RMSE)of 0.90 and 1.20 g/dL for the two datasets,with strong Pearson correlations of 0.82 and 0.72.We conducted extensive posthoc analyses to assess clinical utility and interpretability.A±1 g/dL clinical error tolerance evaluation revealed that 91.3%and 86.7%of predictions for the two datasets fell within the acceptable clinical range.Hemoglobin range-wise analysis demonstrated consistently high accuracy in the normal and low hemoglobin categories.Statistical significance testing using the Wilcoxon signed-rank test confirmed the stability of performance across validation folds(p>0.05 for both RMSE and correlation).Furthermore,model interpretability was enhanced using Gradient-weighted Class Activation Mapping(Grad-CAM),supporting the model’s clinical trustworthiness.The proposed HMALCNN offers a computationally efficient,clinically interpretable,and generalizable framework for noninvasive hemoglobin monitoring,with strong potential for integration into wearable healthcare systems as a practical alternative to invasive measurement techniques.展开更多
[目的]通过PPG监测跟骨骨折传统L型与改良外侧切口的血运变化及术后切口愈合情况,探讨改良切口的优越性,为下一步临床治疗提供理论支持。[方法]比较2012年1月~2014年10月间单侧跟骨骨折患者行外侧传统L型切口及改良L型切口手术内固定80...[目的]通过PPG监测跟骨骨折传统L型与改良外侧切口的血运变化及术后切口愈合情况,探讨改良切口的优越性,为下一步临床治疗提供理论支持。[方法]比较2012年1月~2014年10月间单侧跟骨骨折患者行外侧传统L型切口及改良L型切口手术内固定80例患者的临床资料,分别于术前及术后第2 d选择切口的6个对应点（A、A1,B、B1,C、C1）作为PPG监测点,并与健侧比较,了解皮瓣血运情况。同时,对术后切口愈合情况进行比较。[结果]传统组在B点及B1点的PPG监测要较改良组差,差异具有统计学意义（P〈0.05）。术后20 d切口愈合传统组比改良组差,差异有统计学意义（P〈0.05）,切口不愈合情况与术后2 d PPG监测呈正相关。[结论]经PPG监测,改良切口组对皮瓣的血运影响小于传统切口组,切口愈合率高于传统切口组,PPG监测结果接近正常者,切口均完全愈合,切口不愈合率与PPG的早期监测结果呈正相关,PPG监测可早期预测切口的不愈合情况。展开更多
针对当前光电容积描记法测量血压方法复杂,不适用于低功耗可穿戴设备的问题。文中在脉搏波时域特征参数法的基础上,提出了一种基于单路光电容积脉搏波(Photoplethysmography,简称PPG)的连续血压检测算法。选用MAX30102脉搏波传感器采集...针对当前光电容积描记法测量血压方法复杂,不适用于低功耗可穿戴设备的问题。文中在脉搏波时域特征参数法的基础上,提出了一种基于单路光电容积脉搏波(Photoplethysmography,简称PPG)的连续血压检测算法。选用MAX30102脉搏波传感器采集PPG信号,对脉搏波依次进行均匀滤波、周期分割、基线校准和归一化后,识别脉搏波的特征点并计算特征值。进而以特征值探究与血压之间的关系,建立血压回归模型。试验结果表明,该方法与充气式电子血压计的一致性较好,测量误差符合美国医疗器械促进学会(AAMI:Association for the Advancement of Medical Instrumentation)标准差不大于8 mmHg的范围。展开更多
应用反相高效液相法研究不同月份中棒柄花（Cleidion brevipetiolatum Pax et Hoffm.）植物枝叶中反式-4-（1-丙烯基）苯酚-β-D吡喃葡萄糖苷（PPG）含量的动态积累,确定棒柄花枝叶的最佳采收时期.结果表明,当年11月至次年2月份样品中PP...应用反相高效液相法研究不同月份中棒柄花（Cleidion brevipetiolatum Pax et Hoffm.）植物枝叶中反式-4-（1-丙烯基）苯酚-β-D吡喃葡萄糖苷（PPG）含量的动态积累,确定棒柄花枝叶的最佳采收时期.结果表明,当年11月至次年2月份样品中PPG的含量相对较高,其中2月份的含量最高,为0.14%;当年3～10月份样品中PPG的含量相对较低,其中6月份含量最低,为0.0329%.当年11月至次年2月份为棒柄花枝叶的最佳采收时期.展开更多
Temporary plugging agent(TPA)is widely used in many fields of petroleum reservoir drilling and production,such as temporary plugging while drilling and petroleum well stimulation by diverting in acidizing or fracturin...Temporary plugging agent(TPA)is widely used in many fields of petroleum reservoir drilling and production,such as temporary plugging while drilling and petroleum well stimulation by diverting in acidizing or fracturing operations.The commonly used TPA mainly includes hard particles,fibers,gels,and composite systems.However,current particles have many limitations in applications,such as insufficient plugging strength and slow degradation rate.In this paper,a degradable pre-formed particle gel(DPPG)was developed.Experimental results show that the DPPG has an excellent static swelling effect and self-degradation performance.With a decrease in the concentration of total monomers or cross-linker,the swelling volume of the synthesized DPPG gradually increases.However,the entire self-degradation time gradually decreases.The increase in 2-acrylamide-2-methylpropanesulfonic acid(AMPS)in the DPPG composition can significantly increase its swelling ratio and shorten the self-degradation time.Moreover,DPPG has excellent high-temperature resistance(150°C)and high-salinity resistance(200,000 mg/L NaCl).Core displacement results show that the DPPG has a perfect plugging effect in the porous media(the plugging pressure gradient was as high as 21.12 MPa),and the damage to the formation after degradation is incredibly minor.Therefore,the DPPG can be used as an up-and-coming TPA in oil fields.展开更多
基金funded by the National Science and Technology Major Project under Grant No.2024ZD0532000 and Grant No.2024ZD0532002the National Natural Science Foundation of China under Grant No.62173318+2 种基金the Shenzhen Basic Research Program under Grant No.JCYJ20250604182831042the Key Laboratory of Biomedical Imaging Science and System,Chinese Academy of Sciencesthe Alliance of International Science Organization(ANSO)under Grant No.2021A8017729010.
文摘Diabetes imposes a substantial burden on global healthcare systems.Worldwide,nearly half of individuals with diabetes remain undiagnosed,while conventional diagnostic techniques are often invasive,painful,and expensive.In this study,we propose a noninvasive approach for diabetes detection using photoplethysmography(PPG),which is widely integrated into modern wearable devices.First,we derived velocity plethysmography(VPG)and acceleration plethysmography(APG)signals from PPG to construct multi-channel waveform representations.Second,we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal,statistical,and nonlinear features with recursive feature elimination and deep feature extraction using a one-dimensional statistical convolutional neural network(1DSCNN).Finally,we developed an interpretable diabetes detection method based on XGBoost,with explainable artificial intelligence(XAI)techniques.Specifically,SHapley Additive exPlanations(SHAP)and Local InterpretableModel-agnostic Explanations(LIME)were employed to identify and interpret potential digital biomarkers associated with diabetes.To validate the proposed method,we extended the publicly available Guilin People’s Hospital dataset by incorporating in-house clinical data from ten subjects,thereby enhancing data diversity.A subject-independent cross-validation strategy was applied to ensure that the testing subjects remained independent of the training data for robust generalization.Compared with existing state-of-the-art methods,our approach achieved superior performance,with an area under the curve(AUC)of 80.5±15.9%,sensitivity of 77.2±7.5%,and specificity of 64.3±18.2%.These results demonstrate that the proposed approach provides a noninvasive,interpretable,and accessible solution for diabetes detection using PPG signals.
基金funded by the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body.Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes,where abnormal hemoglobin levels can indicate significant health issues.Traditional methods for hemoglobin measurement are invasive,causing pain,risk of infection,and are less convenient for frequent monitoring.PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure,sleep,blood glucose,and stress analysis.In this work,we propose a hemoglobin estimation method using an adaptive lightweight convolutional neural network(HMALCNN)from PPG.The HMALCNN is designed to capture both fine-grained local waveform characteristics and global contextual patterns,ensuring robust performance across acquisition settings.We validated our approach on two multi-regional datasets containing 152 and 68 subjects,respectively,employing a subjectindependent 5-fold cross-validation strategy.The proposed method achieved root mean square errors(RMSE)of 0.90 and 1.20 g/dL for the two datasets,with strong Pearson correlations of 0.82 and 0.72.We conducted extensive posthoc analyses to assess clinical utility and interpretability.A±1 g/dL clinical error tolerance evaluation revealed that 91.3%and 86.7%of predictions for the two datasets fell within the acceptable clinical range.Hemoglobin range-wise analysis demonstrated consistently high accuracy in the normal and low hemoglobin categories.Statistical significance testing using the Wilcoxon signed-rank test confirmed the stability of performance across validation folds(p>0.05 for both RMSE and correlation).Furthermore,model interpretability was enhanced using Gradient-weighted Class Activation Mapping(Grad-CAM),supporting the model’s clinical trustworthiness.The proposed HMALCNN offers a computationally efficient,clinically interpretable,and generalizable framework for noninvasive hemoglobin monitoring,with strong potential for integration into wearable healthcare systems as a practical alternative to invasive measurement techniques.
文摘[目的]通过PPG监测跟骨骨折传统L型与改良外侧切口的血运变化及术后切口愈合情况,探讨改良切口的优越性,为下一步临床治疗提供理论支持。[方法]比较2012年1月~2014年10月间单侧跟骨骨折患者行外侧传统L型切口及改良L型切口手术内固定80例患者的临床资料,分别于术前及术后第2 d选择切口的6个对应点（A、A1,B、B1,C、C1）作为PPG监测点,并与健侧比较,了解皮瓣血运情况。同时,对术后切口愈合情况进行比较。[结果]传统组在B点及B1点的PPG监测要较改良组差,差异具有统计学意义（P〈0.05）。术后20 d切口愈合传统组比改良组差,差异有统计学意义（P〈0.05）,切口不愈合情况与术后2 d PPG监测呈正相关。[结论]经PPG监测,改良切口组对皮瓣的血运影响小于传统切口组,切口愈合率高于传统切口组,PPG监测结果接近正常者,切口均完全愈合,切口不愈合率与PPG的早期监测结果呈正相关,PPG监测可早期预测切口的不愈合情况。
文摘针对当前光电容积描记法测量血压方法复杂,不适用于低功耗可穿戴设备的问题。文中在脉搏波时域特征参数法的基础上,提出了一种基于单路光电容积脉搏波(Photoplethysmography,简称PPG)的连续血压检测算法。选用MAX30102脉搏波传感器采集PPG信号,对脉搏波依次进行均匀滤波、周期分割、基线校准和归一化后,识别脉搏波的特征点并计算特征值。进而以特征值探究与血压之间的关系,建立血压回归模型。试验结果表明,该方法与充气式电子血压计的一致性较好,测量误差符合美国医疗器械促进学会(AAMI:Association for the Advancement of Medical Instrumentation)标准差不大于8 mmHg的范围。
文摘应用反相高效液相法研究不同月份中棒柄花（Cleidion brevipetiolatum Pax et Hoffm.）植物枝叶中反式-4-（1-丙烯基）苯酚-β-D吡喃葡萄糖苷（PPG）含量的动态积累,确定棒柄花枝叶的最佳采收时期.结果表明,当年11月至次年2月份样品中PPG的含量相对较高,其中2月份的含量最高,为0.14%;当年3～10月份样品中PPG的含量相对较低,其中6月份含量最低,为0.0329%.当年11月至次年2月份为棒柄花枝叶的最佳采收时期.
基金This work was supported by the Research Foundation of China University of Petroleum-Beijing at Karamay(No.XQZX20200010)the Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2019D01B57)+3 种基金the University Scientific Research Project of Xinjiang Uygur Autonomous Region(No.XJEDU2019Y067)the Xinjiang Uygur Autonomous Region Innovation Environment Construction Project(No.2019Q025)the Sichuan Province Regional Innovation Cooperation Project(No.2020YFQ0036)the CNPC Strategic Cooperation Science and Technology Project(ZLZX2020-01-04-04)。
文摘Temporary plugging agent(TPA)is widely used in many fields of petroleum reservoir drilling and production,such as temporary plugging while drilling and petroleum well stimulation by diverting in acidizing or fracturing operations.The commonly used TPA mainly includes hard particles,fibers,gels,and composite systems.However,current particles have many limitations in applications,such as insufficient plugging strength and slow degradation rate.In this paper,a degradable pre-formed particle gel(DPPG)was developed.Experimental results show that the DPPG has an excellent static swelling effect and self-degradation performance.With a decrease in the concentration of total monomers or cross-linker,the swelling volume of the synthesized DPPG gradually increases.However,the entire self-degradation time gradually decreases.The increase in 2-acrylamide-2-methylpropanesulfonic acid(AMPS)in the DPPG composition can significantly increase its swelling ratio and shorten the self-degradation time.Moreover,DPPG has excellent high-temperature resistance(150°C)and high-salinity resistance(200,000 mg/L NaCl).Core displacement results show that the DPPG has a perfect plugging effect in the porous media(the plugging pressure gradient was as high as 21.12 MPa),and the damage to the formation after degradation is incredibly minor.Therefore,the DPPG can be used as an up-and-coming TPA in oil fields.
