FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appro-priate and timely decisions during pregnancy and labor. The extraction and detection of the FECG signa...FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appro-priate and timely decisions during pregnancy and labor. The extraction and detection of the FECG signal from com-posite maternal abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this paper is to illustrate the developed algorithms on FECG signal extraction from the abdominal ECG signal using Neural Network approach to provide efficient and effective ways of separating and understanding the FECG signal and its nature. The FECG signal was isolated from the abdominal signal by neural network approach with different learning constant value and momentum as well so that acceptable signal can be con-sidered. According to the output it can be said that the algorithm is working satisfactory on high learning rate and low momentum value. The method appears to be exceedingly robust, correctly isolate the FECG signal from abdominal ECG.展开更多
The electrocardiogram（ECG） recorded from the abdominal surface of a pregnant woman is a composite of maternal ECG, fetal ECG（f ECG） and other noises, while only the f ECG component is always needed by us. With dif...The electrocardiogram（ECG） recorded from the abdominal surface of a pregnant woman is a composite of maternal ECG, fetal ECG（f ECG） and other noises, while only the f ECG component is always needed by us. With different locations of electrode pairs on the maternal abdominal surface to measure f ECGs, the signal-to-noise ratios（SNRs） of the recorded abdominal ECGs are also correspondingly different. Some regularity on how to locate electrodes to obtain higher f ECG SNRs is needed practically. In this paper, 343 groups of abdominal ECG records were acquired from 78 pregnant women with different electrode pairs locating, and an appropriate extended research database is formed. Then the regularity on f ECG SNRs corresponding to different electrode pairs locating was studied. Based on statistical analysis, it is shown that the f ECG SNRs are significantly higher in certain locations than others. Reasonable explanation is also provided to the statistical result using the theories of the fetal cardiac electrical axis and the signal phase delay.展开更多
Fetal ECG extraction has the vital significance for fetal monitoring.This paper introduces a method of extracting fetal ECG based on adaptive linear neural network.The method can be realized by training a small quanti...Fetal ECG extraction has the vital significance for fetal monitoring.This paper introduces a method of extracting fetal ECG based on adaptive linear neural network.The method can be realized by training a small quantity of data.In addition,a better result can be achieved by improving neural network structure.Thus,more easily identified fetal ECG can be extracted.Experimental results show that the adaptive linear neural network can be used to extract fetal ECG from maternal abdominal signal effectively.What's more,a clearer fetal ECG can be extracted by improving neural network structure.展开更多
We collected 343 groups of abdominal electrocardiogram(ECG) data from 78 pregnant women and deleted the channels unable for experts to determine R-wave peaks from them; then, based on these filtered data, the statisti...We collected 343 groups of abdominal electrocardiogram(ECG) data from 78 pregnant women and deleted the channels unable for experts to determine R-wave peaks from them; then, based on these filtered data, the statistics of position difference of corresponding R-wave peaks for different maternal ECG components from different points were studied. The resultant statistics showed the regularity that the position difference of corresponding maternal R-wave peaks between different abdominal points does not exceed the range of 30 ms. The regularity was also proved using the fECG data from MIT–BIH PhysioBank. Additionally, the paper applied the obtained regularity, the range of position differences of the corresponding maternal R-wave peaks, to accomplish the automatic detection of maternal R-wave peaks in the recorded all initial 343 groups of abdominal signals, including the ones with the largest fetal ECG components, and all 55 groups of ECG data from MIT–BIH PhysioBank, achieving the successful separation of the maternal ECGs.展开更多
文摘FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appro-priate and timely decisions during pregnancy and labor. The extraction and detection of the FECG signal from com-posite maternal abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this paper is to illustrate the developed algorithms on FECG signal extraction from the abdominal ECG signal using Neural Network approach to provide efficient and effective ways of separating and understanding the FECG signal and its nature. The FECG signal was isolated from the abdominal signal by neural network approach with different learning constant value and momentum as well so that acceptable signal can be con-sidered. According to the output it can be said that the algorithm is working satisfactory on high learning rate and low momentum value. The method appears to be exceedingly robust, correctly isolate the FECG signal from abdominal ECG.
基金supported by the National Natural Science Foundation of China(Grant No.61271079)the Supporting Plan Project of Jiangsu Province,China(Grant No.BE2010720)
文摘The electrocardiogram（ECG） recorded from the abdominal surface of a pregnant woman is a composite of maternal ECG, fetal ECG（f ECG） and other noises, while only the f ECG component is always needed by us. With different locations of electrode pairs on the maternal abdominal surface to measure f ECGs, the signal-to-noise ratios（SNRs） of the recorded abdominal ECGs are also correspondingly different. Some regularity on how to locate electrodes to obtain higher f ECG SNRs is needed practically. In this paper, 343 groups of abdominal ECG records were acquired from 78 pregnant women with different electrode pairs locating, and an appropriate extended research database is formed. Then the regularity on f ECG SNRs corresponding to different electrode pairs locating was studied. Based on statistical analysis, it is shown that the f ECG SNRs are significantly higher in certain locations than others. Reasonable explanation is also provided to the statistical result using the theories of the fetal cardiac electrical axis and the signal phase delay.
基金Foundation of Young Backbone Teacher of Beijing Citygrant number:102KB000845
文摘Fetal ECG extraction has the vital significance for fetal monitoring.This paper introduces a method of extracting fetal ECG based on adaptive linear neural network.The method can be realized by training a small quantity of data.In addition,a better result can be achieved by improving neural network structure.Thus,more easily identified fetal ECG can be extracted.Experimental results show that the adaptive linear neural network can be used to extract fetal ECG from maternal abdominal signal effectively.What's more,a clearer fetal ECG can be extracted by improving neural network structure.
基金Project supported by the National Natural Science Foundation of China （Grant No. 61271079） and the Jiangsu Supporting Plan Project, China （Grant No. BE2010720）.
文摘We collected 343 groups of abdominal electrocardiogram(ECG) data from 78 pregnant women and deleted the channels unable for experts to determine R-wave peaks from them; then, based on these filtered data, the statistics of position difference of corresponding R-wave peaks for different maternal ECG components from different points were studied. The resultant statistics showed the regularity that the position difference of corresponding maternal R-wave peaks between different abdominal points does not exceed the range of 30 ms. The regularity was also proved using the fECG data from MIT–BIH PhysioBank. Additionally, the paper applied the obtained regularity, the range of position differences of the corresponding maternal R-wave peaks, to accomplish the automatic detection of maternal R-wave peaks in the recorded all initial 343 groups of abdominal signals, including the ones with the largest fetal ECG components, and all 55 groups of ECG data from MIT–BIH PhysioBank, achieving the successful separation of the maternal ECGs.
