Fetal heart rate(FHR)monitoring is one of the central parts of obstetric care.Ultrasound-based technologies such as cardiotocography(CTG)remain the most common method for FHR monitoring.The CTG’s limitations,includin...Fetal heart rate(FHR)monitoring is one of the central parts of obstetric care.Ultrasound-based technologies such as cardiotocography(CTG)remain the most common method for FHR monitoring.The CTG’s limitations,including subjective interpretation,high interobserver variability,and the need for skilled professionals,led to the development of computerized CTG(cCTG).While cCTG demonstrated advantages,its superiority over visual interpretation remains inconclusive.This has prompted the exploration of alternatives like noninvasive fetal electrocardiography(NIFECG).This review explores the landscape of antenatal FHR monitoring and the need for remote FHR monitoring in a patient-centered care model.Additionally,FHR monitoring needs to evolve from the traditional approach to incorporate artificial intelligence and machine learning.The review underscores the importance of aligning fetal monitoring with modern healthcare,leveraging artificial intelligence algorithms for accurate assessments,and enhancing patient engagement.The physiology of FHR variability(FHRV)is explained emphasizing its significance in assessing fetal well-being.Other measures of FHRV and their relevance are described.It delves into the promising realm of NIFECG,detailing its history and recent technological advancements.The potential advantages of NIFECG are objective FHR assessment,beat-to-beat variability,patient comfort,remote prolonged use,and less signal loss with increased maternal body mass index.Despite its promise,challenges such as signal loss must be addressed.The clinical application of NIFECG,its correlation with cCTG measures,and ongoing technological advancements are discussed.In conclusion,this review explores the evolution of antenatal FHR monitoring,emphasizing the potential of NIFECG in providing reliable,home-based monitoring solutions.Future research directions are outlined,urging longitudinal studies and evidence generation to establish NIFECG’s role in enhancing fetal well-being assessments during pregnancy.展开更多
Objective:This study investigates the efficacy of analyzing fetal heart rate(FHR)signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through el...Objective:This study investigates the efficacy of analyzing fetal heart rate(FHR)signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through electronic fetal monitoring during labor.Methods:A total of 43,888 cardiotocograph(CTG)records of female patients in labor from January 2012 to December 2020 were collected from the NanFang Hospital of Southern Medical University.After filtering the data,2341 FHR records were used for the study.The ObVue fetal monitoring system,manufactured by Lian-Med Technology Co.Ltd.,was used to monitor the FHR signals for these pregnant women from the beginning of the first stage of labor to the end of delivery.Two obstetric experts together annotated the FHR signals in the system to determine the baseline as well as accelerations/decelerations of the FHR.Our cardiotocograph network(CTGNet)as well as traditional methods were then used to automatically analyze the baseline and acceleration/deceleration of the FHR signals.The results of calculations were compared with the annotations provided by the obstetric experts,and ten-fold cross-validation was applied to evaluate them.The root-mean-square difference(RMSD)between the baselines,acceleration F-measure(Acc.F-measure),deceleration F-measure(Dec.F-measure),coefficient of synthetic inconsistency(SI)and the morphological analysis discordance index(MADI)were used as evaluation metrics.The data were analyzed by using a pairedt-test.Results:The proposed CTGNet was superior to the best traditional method,proposed by Mantel,in terms of the RMSD.BL(1.7935±0.8099vs.2.0293±0.9267,t=-3.55,P=0.004),Acc.F-measure(86.8562±10.9422vs.72.2367±14.2096,t=12.43,P<0.001),Dec.F-measure(72.1038±33.2592vs.58.5040±38.0276,t=4.10,P<0.001),SI(34.8277±20.9595vs.54.8049±25.0265,t=-9.39,P<0.001),and MADI(3.1741±1.9901vs.3.7289±2.7253,t=-2.74,P=0.012).The proposed CTGNet thus had significant advantages over the best traditional method on all evaluation metrics.Conclusion:The proposed Artificial Intelligence-based method CTGNet delivers good performance in terms of the automatic analysis of FHR based on cardiotocograph data.It promises to be a key component of smart obstetrics systems of the future.展开更多
目的:评价胎心率变异功率谱与胎儿窘迫状态的关系。方法:对301例单胎妊娠孕妇行产前胎儿电子监护,将所得胎心率模拟信号转化为数字信号后再行功率谱分析,分析极低频(very low frequency 1,VLF)、低频1(low frequency,LF1)、LF2、高频1(h...目的:评价胎心率变异功率谱与胎儿窘迫状态的关系。方法:对301例单胎妊娠孕妇行产前胎儿电子监护,将所得胎心率模拟信号转化为数字信号后再行功率谱分析,分析极低频(very low frequency 1,VLF)、低频1(low frequency,LF1)、LF2、高频1(high frequency 1,HF1)、HF2、HF3、HF4功率谱密度与脐动脉血血气分析指标、脐血促红细胞生成素和肾上腺素浓度的关系。结果:LF2与碳酸氢根(HCO3-)、二氧化碳总量(TCO2)呈正相关(P<0.01);HF1与碳酸氢根(HCO3-)、二氧化碳总量(TCO2)呈负相关(P<0.01);VLF与促红细胞生成素浓度呈负相关(P<0.01);LF1与肾上腺素浓度呈正相关(P<0.05)。结论:胎心率变异性的功率谱密度反映了胎儿窘迫不同发展阶段的状态。展开更多
目的分析镇痛分娩后出现胎儿窘迫的相关因素,提出在镇痛分娩时预防胎儿窘迫的措施。方法对2000年10月～2012年11月本院140例镇痛分娩后出现因胎心变异的胎儿窘迫与同期420例镇痛分娩后未出现异常的相关因素进行比较分析。结果两组间在...目的分析镇痛分娩后出现胎儿窘迫的相关因素,提出在镇痛分娩时预防胎儿窘迫的措施。方法对2000年10月～2012年11月本院140例镇痛分娩后出现因胎心变异的胎儿窘迫与同期420例镇痛分娩后未出现异常的相关因素进行比较分析。结果两组间在宫缩间歇期注入麻醉药率分别为14.3%、59.0%,麻醉前空腹>4 h率分别为20.0%、2.4%,低血压期应用缩宫素静脉滴注加强宫缩率分别为21.4%、0.5%,差异有统计学意义(P<0.05)。两组间血压下降>30 mm Hg率分别为30.0%、14.8%,麻醉后采取平卧位控制阻滞平面率分别为30.0%、12.4%,差异有统计学意义(P<0.01)。结论麻醉前4 h内适量进食、宫缩节律的间歇期注入麻醉药物、减少血压下降幅度、低血压时不使用缩宫素、麻醉后采取侧卧位控制阻滞平面可有效预防镇痛分娩后因胎心变异发生的胎儿窘迫。展开更多
文摘Fetal heart rate(FHR)monitoring is one of the central parts of obstetric care.Ultrasound-based technologies such as cardiotocography(CTG)remain the most common method for FHR monitoring.The CTG’s limitations,including subjective interpretation,high interobserver variability,and the need for skilled professionals,led to the development of computerized CTG(cCTG).While cCTG demonstrated advantages,its superiority over visual interpretation remains inconclusive.This has prompted the exploration of alternatives like noninvasive fetal electrocardiography(NIFECG).This review explores the landscape of antenatal FHR monitoring and the need for remote FHR monitoring in a patient-centered care model.Additionally,FHR monitoring needs to evolve from the traditional approach to incorporate artificial intelligence and machine learning.The review underscores the importance of aligning fetal monitoring with modern healthcare,leveraging artificial intelligence algorithms for accurate assessments,and enhancing patient engagement.The physiology of FHR variability(FHRV)is explained emphasizing its significance in assessing fetal well-being.Other measures of FHRV and their relevance are described.It delves into the promising realm of NIFECG,detailing its history and recent technological advancements.The potential advantages of NIFECG are objective FHR assessment,beat-to-beat variability,patient comfort,remote prolonged use,and less signal loss with increased maternal body mass index.Despite its promise,challenges such as signal loss must be addressed.The clinical application of NIFECG,its correlation with cCTG measures,and ongoing technological advancements are discussed.In conclusion,this review explores the evolution of antenatal FHR monitoring,emphasizing the potential of NIFECG in providing reliable,home-based monitoring solutions.Future research directions are outlined,urging longitudinal studies and evidence generation to establish NIFECG’s role in enhancing fetal well-being assessments during pregnancy.
基金supported by National Key Research and Development Project(2019YFC0121907 and 2019YFC0120100)Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization(2021B1212040007)。
文摘Objective:This study investigates the efficacy of analyzing fetal heart rate(FHR)signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through electronic fetal monitoring during labor.Methods:A total of 43,888 cardiotocograph(CTG)records of female patients in labor from January 2012 to December 2020 were collected from the NanFang Hospital of Southern Medical University.After filtering the data,2341 FHR records were used for the study.The ObVue fetal monitoring system,manufactured by Lian-Med Technology Co.Ltd.,was used to monitor the FHR signals for these pregnant women from the beginning of the first stage of labor to the end of delivery.Two obstetric experts together annotated the FHR signals in the system to determine the baseline as well as accelerations/decelerations of the FHR.Our cardiotocograph network(CTGNet)as well as traditional methods were then used to automatically analyze the baseline and acceleration/deceleration of the FHR signals.The results of calculations were compared with the annotations provided by the obstetric experts,and ten-fold cross-validation was applied to evaluate them.The root-mean-square difference(RMSD)between the baselines,acceleration F-measure(Acc.F-measure),deceleration F-measure(Dec.F-measure),coefficient of synthetic inconsistency(SI)and the morphological analysis discordance index(MADI)were used as evaluation metrics.The data were analyzed by using a pairedt-test.Results:The proposed CTGNet was superior to the best traditional method,proposed by Mantel,in terms of the RMSD.BL(1.7935±0.8099vs.2.0293±0.9267,t=-3.55,P=0.004),Acc.F-measure(86.8562±10.9422vs.72.2367±14.2096,t=12.43,P<0.001),Dec.F-measure(72.1038±33.2592vs.58.5040±38.0276,t=4.10,P<0.001),SI(34.8277±20.9595vs.54.8049±25.0265,t=-9.39,P<0.001),and MADI(3.1741±1.9901vs.3.7289±2.7253,t=-2.74,P=0.012).The proposed CTGNet thus had significant advantages over the best traditional method on all evaluation metrics.Conclusion:The proposed Artificial Intelligence-based method CTGNet delivers good performance in terms of the automatic analysis of FHR based on cardiotocograph data.It promises to be a key component of smart obstetrics systems of the future.
文摘目的:评价胎心率变异功率谱与胎儿窘迫状态的关系。方法:对301例单胎妊娠孕妇行产前胎儿电子监护,将所得胎心率模拟信号转化为数字信号后再行功率谱分析,分析极低频(very low frequency 1,VLF)、低频1(low frequency,LF1)、LF2、高频1(high frequency 1,HF1)、HF2、HF3、HF4功率谱密度与脐动脉血血气分析指标、脐血促红细胞生成素和肾上腺素浓度的关系。结果:LF2与碳酸氢根(HCO3-)、二氧化碳总量(TCO2)呈正相关(P<0.01);HF1与碳酸氢根(HCO3-)、二氧化碳总量(TCO2)呈负相关(P<0.01);VLF与促红细胞生成素浓度呈负相关(P<0.01);LF1与肾上腺素浓度呈正相关(P<0.05)。结论:胎心率变异性的功率谱密度反映了胎儿窘迫不同发展阶段的状态。
文摘目的分析镇痛分娩后出现胎儿窘迫的相关因素,提出在镇痛分娩时预防胎儿窘迫的措施。方法对2000年10月～2012年11月本院140例镇痛分娩后出现因胎心变异的胎儿窘迫与同期420例镇痛分娩后未出现异常的相关因素进行比较分析。结果两组间在宫缩间歇期注入麻醉药率分别为14.3%、59.0%,麻醉前空腹>4 h率分别为20.0%、2.4%,低血压期应用缩宫素静脉滴注加强宫缩率分别为21.4%、0.5%,差异有统计学意义(P<0.05)。两组间血压下降>30 mm Hg率分别为30.0%、14.8%,麻醉后采取平卧位控制阻滞平面率分别为30.0%、12.4%,差异有统计学意义(P<0.01)。结论麻醉前4 h内适量进食、宫缩节律的间歇期注入麻醉药物、减少血压下降幅度、低血压时不使用缩宫素、麻醉后采取侧卧位控制阻滞平面可有效预防镇痛分娩后因胎心变异发生的胎儿窘迫。
