目的:为实现从母体腹壁混合信号中提取高信噪比和波形清晰的胎儿心电信号,提出一种融合核主成分分析(kernel principal component analysis,KPCA)、快速独立成分分析(fast independent component analysis,FastICA)及奇异值分解(singula...目的:为实现从母体腹壁混合信号中提取高信噪比和波形清晰的胎儿心电信号,提出一种融合核主成分分析(kernel principal component analysis,KPCA)、快速独立成分分析(fast independent component analysis,FastICA)及奇异值分解(singular value decomposition,SVD)的胎儿心电信号提取算法。方法:首先,采用KPCA对母体心电信号进行降维,再利用改进的基于负熵的FastICA处理降维后的数据,得到独立成分。随后,引入样本熵进行信号通道选择,挑选出包含最多母体信息的信号通道。在选中的母体通道上进行SVD,得到母体心电信号的近似估计,再用腹壁源信号减去该信号得到胎儿心电的初步估计。最后,采用改进的基于负熵的FastICA成功分离出纯净的胎儿心电信号。在腹部和直接胎儿心电图数据库(Abdominal and Direct Fetal Electrocardiogram Database,ADFECGDB)和PhysioNet 2013挑战赛数据库中对提出的算法进行验证。结果:提出的算法在主观视觉效果和客观评价指标上都表现出优越的性能。在ADFECGDB数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.74%、98.85%和99.30%;在PhysioNet 2013挑战赛数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.10%、97.87%和98.48%。结论:融合KPCA、FastICA及SVD的胎儿心电信号提取算法在提取胎儿心电信号的同时有效处理了附加噪声,为胎儿疾病的早期诊断提供了有力支持。展开更多
本文应用RLS-ANC(recursive least squares adaptive noise cancellation)自适应滤波方法提取胎儿心电(FECG)信号。该方法采用RLS-ANC自适应滤波消除母亲心电,提取胎儿心电信号。实验结果表明,本方法适应非平稳信号的能力强,收敛速度快...本文应用RLS-ANC(recursive least squares adaptive noise cancellation)自适应滤波方法提取胎儿心电(FECG)信号。该方法采用RLS-ANC自适应滤波消除母亲心电,提取胎儿心电信号。实验结果表明,本方法适应非平稳信号的能力强,收敛速度快,提取效果好于NLMS(normalizedleast mean squares)算法。展开更多
Effective fetal monitoring is an important guarantee for fetal health and early treatment. Fetal movement is one of critical indicators of fetal monitoring, which plays an important role in fetal health. Counting the ...Effective fetal monitoring is an important guarantee for fetal health and early treatment. Fetal movement is one of critical indicators of fetal monitoring, which plays an important role in fetal health. Counting the number of fetal movement by pregnant women is a traditional method for long-term monitoring. However, there are many defects in pregnant women’s feeling count, which cannot meet the accurate requirements of modern perinatal medicine. With the rapid development of biological and electronic technology, various sensors are used to probe the fetal dynamic monitoring, but not on fetal movement. This research proposes a monitoring method for fetal movement via three electrodes. Briefly: first, three electrodes are used to extract electrical signals in the abdomen of pregnant women;second, these signals are amplified and filtered;third, A/D converter with microprocessor is used to make analog digital conversion, which can be stored in the SD card under the control of the microprocessor;finally, the SD card data are processed by computer software and the fetal movement information is analyzed.展开更多
目的:探讨分析胎儿心电图(FECG)在子痫前期诊断中的应用价值及对新生儿结局的影响。方法:对江油市人民医院2019年8月至2021年1月期间收治的58例子痫前期患者(孕周在32周至42周之间)和同期在该院接受孕期产检的52例健康孕妇(孕周在32周...目的:探讨分析胎儿心电图(FECG)在子痫前期诊断中的应用价值及对新生儿结局的影响。方法:对江油市人民医院2019年8月至2021年1月期间收治的58例子痫前期患者(孕周在32周至42周之间)和同期在该院接受孕期产检的52例健康孕妇(孕周在32周至42周之间)的临床资料进行回顾性分析。将58例子痫前期患者作为子痫前期组,将52例健康孕妇作为对照组。对两组人员均进行FECG检查,然后对比检查的结果。在完成检查后,对检查结果异常的受检者进行相应的处理。在两组人员完成分娩后,对比其新生儿结局。结果:1)子痫前期组患者中存在FECG检查结果异常患者的占比高于对照组孕妇,差异有统计学意义(P<0.05)。2)两组人员新生儿的出生体质量、出生身长、1 min Apgar评分、5 min Apgar评分相比,差异无统计学意义(P>0.05)。结论:对子痫前期患者进行FECG检查有助于发现其胎儿宫内窘迫及缺氧等情况。及时对出现上述情况的患者进行相应的处理可改善其新生儿的结局。展开更多
This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network(CNN)combined with advanced mathematical methods,such as Independe...This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network(CNN)combined with advanced mathematical methods,such as Independent Component Analysis(ICA),Singular Value Decomposition(SVD),and a dimension-reduction technique like Nonnegative Matrix Factorization(NMF).Due to the highly disproportionate frequency of the fetus’s heart rate compared to the mother’s,the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy.Furthermore,we can disentangle the various components of fetal ECG,which serve as inputs to the CNN model to optimize the actual FECG signal,denoted by FECGr,which is recovered using the SVD-ICA process.The findings demonstrate the efficiency of this innovative approach,which may be deployed in real-time.展开更多
文摘目的:为实现从母体腹壁混合信号中提取高信噪比和波形清晰的胎儿心电信号,提出一种融合核主成分分析(kernel principal component analysis,KPCA)、快速独立成分分析(fast independent component analysis,FastICA)及奇异值分解(singular value decomposition,SVD)的胎儿心电信号提取算法。方法:首先,采用KPCA对母体心电信号进行降维,再利用改进的基于负熵的FastICA处理降维后的数据,得到独立成分。随后,引入样本熵进行信号通道选择,挑选出包含最多母体信息的信号通道。在选中的母体通道上进行SVD,得到母体心电信号的近似估计,再用腹壁源信号减去该信号得到胎儿心电的初步估计。最后,采用改进的基于负熵的FastICA成功分离出纯净的胎儿心电信号。在腹部和直接胎儿心电图数据库(Abdominal and Direct Fetal Electrocardiogram Database,ADFECGDB)和PhysioNet 2013挑战赛数据库中对提出的算法进行验证。结果:提出的算法在主观视觉效果和客观评价指标上都表现出优越的性能。在ADFECGDB数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.74%、98.85%和99.30%;在PhysioNet 2013挑战赛数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.10%、97.87%和98.48%。结论:融合KPCA、FastICA及SVD的胎儿心电信号提取算法在提取胎儿心电信号的同时有效处理了附加噪声,为胎儿疾病的早期诊断提供了有力支持。
文摘本文应用RLS-ANC(recursive least squares adaptive noise cancellation)自适应滤波方法提取胎儿心电(FECG)信号。该方法采用RLS-ANC自适应滤波消除母亲心电,提取胎儿心电信号。实验结果表明,本方法适应非平稳信号的能力强,收敛速度快,提取效果好于NLMS(normalizedleast mean squares)算法。
文摘Effective fetal monitoring is an important guarantee for fetal health and early treatment. Fetal movement is one of critical indicators of fetal monitoring, which plays an important role in fetal health. Counting the number of fetal movement by pregnant women is a traditional method for long-term monitoring. However, there are many defects in pregnant women’s feeling count, which cannot meet the accurate requirements of modern perinatal medicine. With the rapid development of biological and electronic technology, various sensors are used to probe the fetal dynamic monitoring, but not on fetal movement. This research proposes a monitoring method for fetal movement via three electrodes. Briefly: first, three electrodes are used to extract electrical signals in the abdomen of pregnant women;second, these signals are amplified and filtered;third, A/D converter with microprocessor is used to make analog digital conversion, which can be stored in the SD card under the control of the microprocessor;finally, the SD card data are processed by computer software and the fetal movement information is analyzed.
文摘目的:探讨分析胎儿心电图(FECG)在子痫前期诊断中的应用价值及对新生儿结局的影响。方法:对江油市人民医院2019年8月至2021年1月期间收治的58例子痫前期患者(孕周在32周至42周之间)和同期在该院接受孕期产检的52例健康孕妇(孕周在32周至42周之间)的临床资料进行回顾性分析。将58例子痫前期患者作为子痫前期组,将52例健康孕妇作为对照组。对两组人员均进行FECG检查,然后对比检查的结果。在完成检查后,对检查结果异常的受检者进行相应的处理。在两组人员完成分娩后,对比其新生儿结局。结果:1)子痫前期组患者中存在FECG检查结果异常患者的占比高于对照组孕妇,差异有统计学意义(P<0.05)。2)两组人员新生儿的出生体质量、出生身长、1 min Apgar评分、5 min Apgar评分相比,差异无统计学意义(P>0.05)。结论:对子痫前期患者进行FECG检查有助于发现其胎儿宫内窘迫及缺氧等情况。及时对出现上述情况的患者进行相应的处理可改善其新生儿的结局。
文摘This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network(CNN)combined with advanced mathematical methods,such as Independent Component Analysis(ICA),Singular Value Decomposition(SVD),and a dimension-reduction technique like Nonnegative Matrix Factorization(NMF).Due to the highly disproportionate frequency of the fetus’s heart rate compared to the mother’s,the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy.Furthermore,we can disentangle the various components of fetal ECG,which serve as inputs to the CNN model to optimize the actual FECG signal,denoted by FECGr,which is recovered using the SVD-ICA process.The findings demonstrate the efficiency of this innovative approach,which may be deployed in real-time.
