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
Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been ...Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been researched.The simulations were made for the performance of these algorithms.The extraction of fetal electrocardiogram(FECG) is applied to compare the application effect of the above algorithms.The proposed FAP algorithm has obvious advantages in computational complexity,convergence speed and steadystate error.展开更多
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
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
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.
基金the National Key Technologies R&D Program (No. 2006BAI22B01)
文摘Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been researched.The simulations were made for the performance of these algorithms.The extraction of fetal electrocardiogram(FECG) is applied to compare the application effect of the above algorithms.The proposed FAP algorithm has obvious advantages in computational complexity,convergence speed and steadystate error.
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.
