CN110101378A - A kind of algorithm extracting fetal heart frequency by mother's abdomen mixing ECG signal - Google Patents
A kind of algorithm extracting fetal heart frequency by mother's abdomen mixing ECG signal Download PDFInfo
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- CN110101378A CN110101378A CN201910359208.5A CN201910359208A CN110101378A CN 110101378 A CN110101378 A CN 110101378A CN 201910359208 A CN201910359208 A CN 201910359208A CN 110101378 A CN110101378 A CN 110101378A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02411—Detecting, measuring or recording pulse rate or heart rate of foetuses
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
Abstract
The present invention provides a kind of algorithms that fetal heart frequency is extracted by mother's abdomen mixing ECG signal, including the detection of abdomen mixing ECG signal processing, MQRS wave, Fetal ECG signal FECG separation, the detection of FQRS wave, signal quality estimation and Kalman filtering to merge six parts.The algorithm fusion separation algorithm of TS, TS-PCA and TS-ICA, avoid unavailability of the single algorithm when handling certain data, to improve the accuracy and reliability of separation algorithm, multi-lead fetal heart frequency information is carried out using signal quality estimation, Kalman filtering algorithm simultaneously to optimize and merge, obtain optimal fetal heart frequency FHR curve, this method fully utilizes the information in each channel, substantially increases the accuracy and anti-interference ability of fetal heart frequency FHR curve.
Description
Technical field
The present invention relates to fetal heart frequency algorithm fields, and in particular to a kind of to extract fetal heart frequency by mother's abdomen mixed signal
Algorithm.
Background technique
Fetal heart frequency (FHR) is the important indicator for monitoring the growth and development state of fetus, can by the fetal heart frequency of acquisition
Whether to be abnormal situation in determining the growth and development of fetus, so as to take the measure remedied early, ensure fetus and
The health and safety of pregnant woman.Current fetal heart frequency mainly passes through the method detection of doppler ultrasound, such as generallys use various how general
It strangles fetus-voice meter to guard fetal rhythm, the ultrasound according to made of Doppler effect diagnoses the situation of fetal rhythm, so as to obtain
Fetal heart frequency.
Although the above-mentioned method using doppler ultrasound can effectively monitoring fetal heart rate, because of ultrasonic radiation
Energy can enter human body, it is contemplated that the ultrasonic radiation can have an impact the health of human body, therefore using doppler ultrasound
It when method monitoring fetal heart rate, is guarded at it and is subjected to control in number and monitoring method, and cannot frequently and chronically made
With.
In the prior art, it discloses and fetal heart frequency has been obtained based on pregnant woman's electrocardiosignal acquisition Fetal ECG signal, have
Body is to acquire her abdominal electrocardiosignal by electrode approach, separates and calculates from her abdominal electrocardiosignal collected
Fetal heart frequency out.The method is all a kind of noninvasive method for fetus and pregnant woman, can be to avoid the ultrasound being harmful to the human body
Energy etc. enters human body, and the method relative to traditional doppler ultrasound is safer, and can realize long term monitoring.
The algorithm that fetal heart frequency FHR is extracted by mother's abdomen mixing ECG signal (AECG) mainly includes two steps: first is that from
Fetal ECG signal (FECG) is isolated in AECG signal, second is that calculating fetal heart frequency FHR according to FECG signal.It is existing at present
FECG separation algorithm based on AECG signal mainly has adaptive filter algorithm, template subtraction (TS) and the analysis of person ignorant of the law's signal
(principal component analysis (ICA), independent component analysis (PCA) etc.) and the blending algorithms such as method (TS-PCA), (TS-ICA).Its
The necessary synchronous acquisition mother's chest leads of middle adaptive filter algorithm are as reference, using in Adaptive noise cancellation AECG signal
Mother's ECG signal, the effect of algorithm is affected by sef-adapting filter；TS technology is to utilize mother QRS in AECG
The detection of wave (MQRS) wave establishes a MECG template (mother's heart claps the period, is denoted as TsECG) using averaging etc., so
TsECG signal is subtracted from AECG afterwards, remaining signal is as FECG signal, and dependent on the detection of MQRS wave, template matching is calculated
Method, and it is affected by noise larger.Blind Signal Separation algorithm (BSS) mainly using multi-lead AECG signal (lead it is more more more
It is good), using the methods of PCA, ICA, direct estimation goes out FECG from AECG.But ICA method dependent on signal source be it is independent with
And non-Gaussian system it is assumed that when use high-order statistic, ICA by multivariable signal decomposition be its component group form；PCA method
Assuming that the signal from various sources is linear hybrid, big variance represents interested structure, and various composition is just
It hands over.However, MECG and FECG be in observation space, without any orthogonality, so the algorithm of PCA and ICA, in many cases simultaneously
It is unreasonable, and PCA and ICA algorithm are limited by multi-lead signal.Blending algorithm TS-BSS (TS-PCA and TS-ICA) is performance
It is generally preferred over TS, BSS algorithm, but the baseline drift due to being related to environment and parent, Hz noise, motion artifacts, myoelectricity are dry
It disturbs and the factors such as the size of position of the fetus in uterus, the position of electrode and pregnant week, causes FECG can in AECG
It can be submerged in noise, or even completely invisible, TS-BSS algorithm, is not able to satisfy wanting for clinical application equally in many cases
It asks.
Summary of the invention
The present invention is directed to the deficiency of existing Fetal ECG signal extraction and separation algorithm, and providing one kind can be improved the fetus heart
The method for the accuracy that rate is extracted.
The present invention is extracted the algorithm of fetal heart frequency by mother's abdomen mixing ECG signal, including abdomen mixing electrocardiosignal is pre-
Processing, the detection of MQRS wave, Fetal ECG signal FECG separation, the detection of FQRS wave, signal quality estimation and Kalman filtering fusion
Six parts.
(1) abdomen mixing ECG signal processing, comprising:
1Hz IIR high-pass filter removes the baseline drift in AECG signal；
80Hz FIR low pass filter removes the radio-frequency component in AECG signal；
50/60Hz trapper removes Hz noise
(2) MQRS wave detects
The position for determining that mother's heartbeat occurs is detected by MQRS wave, is mentioned for next Fetal ECG signal separation algorithm
Point for reference.
(3) FECG Signal separator
Fetal ECG signal FECG is separated, separation algorithm is on the basis of TS, TS-BSS (TS-PCA and TS-ICA)
On, BSS algorithm is merged, i.e., FECG isolated for TS, TS-PCA and TS-ICA algorithm is inputted as signal, reused
PCA and ICA method carries out secondary FECG separation.
(4) FQRS is detected
FQRS detection algorithm runs simultaneously the FECG in multichannel；
(5) signal quality is estimated
Signal quality estimation is carried out, the degree of reliability for the FQRS wave that each channel obtains is assessed.
(6) Kalman filtering merges
1. the FQRS wave position tentatively obtained from each channel, binding signal quality, to the FHR curve tentatively obtained, benefit
It is optimized with Kalman filter；
2. being weighted, being obtained using the FHR curve in each channel according to signal quality and the residual error of Kalman filtering
To optimal FHR curve.
According to above-mentioned technical solution, compared with the existing technology, the present invention have it is following the utility model has the advantages that
1) the FECG separation algorithm for merging TS, TS-PCA and TS-ICA, avoids single algorithm when handling certain data
Unavailability, to improve the accuracy and reliability of separation algorithm；
2) second is that amplification FECG lead number provides more fully signal input for further BSS separation；
3) multi-lead FHR information is carried out using signal quality estimation, Kalman filtering algorithm to optimize and merge, obtain
Optimal FHR curve, this method fully utilize the information in each channel, substantially increase the standard of fetal heart frequency FHR curve
True property and anti-interference ability.
Detailed description of the invention
Fig. 1 is the whole flow chart of algorithm；
Fig. 2 is the flow chart of fetal signals separation and Kalman filtering optimization；
Fig. 3 is the instance graph of the FECG signal of original AECG signal and extraction；
Fig. 4 (a) is the AECG signal graph of 4 leads of this algorithm input, is (b) the AECG signal of input by algorithm point
The FECG signal graph obtained from method；
Fig. 5 (a) is the preliminary FHR curve graph that lead is obtained by FQRS detection, is handled using Kalman filtering
FHR curve graph afterwards；
Fig. 6 (a) (b) (c) (d) is by the FHR curve comparison figure before and after Kalman filtering, is (e) by blending algorithm
Result figure after obtained optimal FHR curve processing.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description.
The present invention provides a kind of algorithms that fetal heart frequency is extracted by mother's abdomen mixing ECG signal, including abdomen mixing
ECG signal processing, the detection of MQRS wave, Fetal ECG signal FECG separation, the detection of FQRS wave, signal quality estimation and karr
Six parts are merged in graceful filtering, specifically, as shown in Figure 1, comprising:
Step S1 includes the abdomen mixed signal of parent electrocardio signal and Fetal ECG signal from the acquisition of the abdomen of parent
AECG。
Step S2 is pre-processed using filter AECG signal collected to step S1, removes collected AECG
Baseline drift, Hz noise, high-frequency noise, random noise in signal etc.；
Specifically, Preprocessing Algorithm specifically includes that
1Hz IIR high-pass filter removes the baseline drift in AECG signal；
80Hz FIR low pass filter removes the radio-frequency component in AECG signal；
50/60Hz trapper removes Hz noise.
Step S3 detects the position for determining that mother's heartbeat occurs by MQRS wave, for next Fetal ECG signal point
Reference point is provided from algorithm.
The detection of QRS wave, mainly by band logical, difference, square, after the processing such as smooth, weaken making an uproar in ECG signal
Sound and P, T wave etc., the ingredient of prominent QRS complex, finally according to a kind of adaptive threshold, detection is greater than the point of threshold value, as QRS
The position of wave.
, be based on the position of MQRS wave when extracting Fetal ECG signal using TS algorithm, but QRS wave on different channel
Position slightly have difference, so needing to readjust the position of QRS wave before each multichannel analysis.
Step S4 separates Fetal ECG signal FECG, and separation algorithm is at TS, TS-BSS (TS-PCA and TS-ICA)
On the basis of, merging BSS algorithm, i.e., FECG isolated for TS, TS-PCA and TS-ICA algorithm is inputted as signal, then
It is secondary to carry out secondary FECG separation using PCA and ICA method.The purpose for merging a variety of separation algorithms is on the one hand to prevent list
One algorithm is not suitable for certain specific data, and causes algorithm not applicable, second is that amplification FECG lead number, for further
BSS separation provides more fully signal input.
In the present embodiment, if original AECG signal is the data of 4 leads, pass through TS, TS-PCA and TS- respectively
The isolated FECG data of ICA algorithm include that the FECG data in 12 channels (do not differentiate between in the present embodiment by TS- altogether
FECG signal, noise signal or the MECG signal that PCA, TS-ICA, PCA, ICA algorithm export, are collectively referred to as FECG).Such as Fig. 2
Shown, the FECG signal in 36 channels is obtained for the detection of FQRS wave and Signal quality assessment (obtained FECG in last
Signal is denoted as, and fecgSig (lead :), lead=1 ..., L, L are port numbers, here L=36).
Step S5, FQRS detection algorithm runs simultaneously the FECG in multichannel, is on the one hand to for calculating FHR song
Line, on the other hand for assessing the signal quality in the channel.
Specifically, the present embodiment utilizes QRS wave detection algorithm, synchronizes processing to the FECG of each lead, detection is each
The wave of doubtful QRS wave in a lead, is denoted as FQRS wave.Then according to the fluctuation of RR interphase and the matching of FQRS and MQRS wave
Degree is denoted as parameter F1, carries out the estimation of signal quality.If the parameter F1 being calculated in some channel is less than certain threshold value
In the case where, fluctuation is bigger, illustrates that its corresponding FHR curve is better, is indicated with FSQI, be specifically shown in formula (3).
Shown in the formula such as formula (1) of the matching degree F1 of FQRS and MQRS wave:
In formula, TP is the QRS quantity that FQRS and MQRS are mutually matched, FP be MQRS wave be not present and FQRS wave existing for
QRS wave quantity (mismatch), FN be MQRS wave exist and FQRS wave existing for QRS wave quantity (mismatch).In the present embodiment,
The length of match window takes 50ms.
In fecgSig (lead :), lead=1 ... L, to the fecgSig in some channel (lead :), if FQRS and
The matching degree F1 of MQRS is very high, illustrates that the corresponding fecgSig of channel lead (lead :) is likely to separate unsuccessful, leads
Cause MECG signal clearly.
In fecgSig (lead :), lead=1 ... L, FHR extraction is carried out to the fecgSig in each channel (lead :)
Processing, then carries out interpolation to FHR curve using linear interpolation, and the FHR curve after interpolation remembers newFhr, between the time of interpolation
It is divided into 0.25s.If when 20 seconds a length of, total point of the FHR curve newFhr after interpolation processing of fecgSig (lead :)
Number is 80 points, newFhr (n), n=1 ... N, N=80.
Abs(newFhr(n+1)-newFhr(n))<threshold (2)
According to (2) formula, judges that newFhr fluctuation is less than the points of threshold value, be denoted as oCnt.OCnt is bigger, illustrates newFhr wave
It moves smaller.
Step S6 carries out signal quality estimation, assesses the degree of reliability for the FQRS wave that each channel obtains.FQRS wave can
It indicates, can be indicated by formula (3) by degree available signal performance figure FSQI
Formula (3) is used to measure the FHR curve that the fecgSig (lead :) (lead=1 ... L) in the channel lead is obtained
Signal quality.FSQI (lead) is bigger, illustrates that corresponding FHR curve is more reliable, and ecgSig (lead :) closer to true
FECG。
Step S7, the FQRS wave position tentatively obtained from each channel, binding signal quality are bent to the FHR tentatively obtained
Line is optimized using Kalman filter, it is therefore an objective to can greatly improve the anti-dry of algorithm using Kalman Filter Estimation
Disturb ability.It is handled by Kalman filter, can be improved the accuracy of FHR curve, but only see the FHR in some channel,
The important information of other FHR curves can be lost.
Kalman filtering KF is a kind of optimal stochastic signal condition estimation method, discrete time control process is estimated, with survey
Data z, x are measured, wherein x and z is managed by linear stochastic difference equation.
xk=Axk-1+Buk+wk-1
zk=Hxk+vk
Stochastic variable w and v, are independent, and meet Gaussian Profile, P (w)~N (1, Q) and p (v)~N (0, R), A,
B, H are coefficient behavior transfer matrixes, and Q is state-noise covariance, and R is measurement noise covariance, and u is the optional control of state x
Input.
KF algorithm is provided by following equation:
Pay attention to hereWithIt is to give a measurement ZkBefore or after priori and posterior state estimation,
PkIt is the covariance matrix of the error of priori and posteriority state.KKIt is to minimize posteriori error covariance PkUnder conditions of gain.
In order to estimate the weight of update from cleaner data, work as KKWhen update, we go adjustment to survey using FSQI
The covariance matrix R of the error of amount.When FSQI value is low, ZkIt should less be worth believing, therefore KKValue should be more
It is small, therefore we force so that R is bigger.This can be realized by following equation:
R→R·(1+ea(SQI-b))2)
Wherein a and b is a constant, wherein a < 0, and b ∈ (0,1).As FSQI=1, covariance matrix R is almost without change
Change, when FAQI < b is especially close to 0, (1+ea(SQI-b)) R can be made to converge on a very big value.This function influences
KF trusts the degree of current measurement value, ZkWith kalman gain Kk.In low FSQI value, R tendency infinity (but the practical upper limit
Converge on a very big value) and KF is forced to reduce KkValue, and more believe before measured value.
Step S8 is added according to signal quality and the residual error of Kalman filtering using the FHR curve in each channel
Power, obtains optimal FHR curve.
Optimize that (the FHR curve after optimization is denoted as to the FHR curve that FQRS detection algorithm obtains using Kalman filtering
KfHR), to the FHR curve kfHR (lead) (lead=1,2 ..., L) after optimization, place is weighted and averaged according to following formula
Reason.
K is the sequence of fetal heart frequency point, and lead is corresponding lead sequence, and FHR (k) is the fused optimal fetus heart
Rate value.WhereinIt is according to the Kalman's residual error provided when updating every time.
Fig. 3 is one section of AECG signal and extracted FECG signal in the present embodiment, and dotted line is original AECG letter in figure
Number, solid line is that (AECG and FECG is multi-lead signal to the isolated FECG curve of corresponding algorithm in practice, here only
Select one of channel to be shown), "+" ' be the MQRS wave that algorithm detects position, " o " is that algorithm detects
The position of FQRS wave.
Fig. 4 is 12 channels in 36 channels of the 4 lead AECG and separation algorithm acquisition in the present embodiment
FECG signal, including FECG, MECG, noise etc.；Fig. 4 (a) is the AECG signal of 4 leads of this algorithm input；Fig. 4 (b) is 4
The FECG in 12 channels in 36 channels that lead AECG signal is obtained by separation algorithms such as TS, TS-ICA, TS-PCA believes
Number, including FECG, MECG, noise etc., it is the position of FQRS wave that curve, which is signal, " * ", in figure.
Fig. 5 (a) is a lead FECG, the preliminary FHR curve obtained according to FQRS detection；(b) block curve is benefit
The FHR curve tentatively obtained in (a) is handled with Kalman filtering, obtained FHR curve (kfHR (lead)), dotted line is bent
Line is the reference FHR curve that the ECG signal obtained according to fetal scalp electrode obtains.As can be seen that by Kalman filtering
Reason, can further reduce the noise contribution in original FHR curve, approach the FHR curve of reference.
Fig. 6 (a) (b) (c) (d) is the FHR curve of the KF filtering front and back in four channels, and wherein dotted line is examined according to FQRS wave
The original FHR curve obtained is surveyed, solid line is KF algorithm to the result after original FHR curve processing.Fig. 6 (e) block curve is benefit
The FHR obtained with KF algorithm, the final optimal FHR curve merged according to signal quality FSQI value, dotted line are bent
Line is the reference FHR curve that the ECG signal obtained according to fetal scalp electrode obtains.It can be seen that four include noise fluctuations
FHR curve, after being weighted averagely, the optimal FHR curve and reference value of acquisition are very close.
Although the present invention has been described by way of example and in terms of the preferred embodiments, embodiment is not for the purpose of limiting the invention.Not
It is detached from the spirit and scope of the present invention, any equivalent change or retouch done also belongs to the protection scope of the present invention.Cause
This protection scope of the present invention should be based on the content defined in the claims of this application.
Claims (5)
1. a kind of algorithm for extracting fetal heart frequency by mother's abdomen mixing ECG signal, which is characterized in that including abdomen mixing electrocardio
Signal Pretreatment, the detection of MQRS wave, Fetal ECG signal FECG separation, the detection of FQRS wave, signal quality estimation and Kalman's filter
Wave merges six parts.
2. the algorithm according to claim 1 for extracting fetal heart frequency by mother's abdomen mixing ECG signal, which is characterized in that
The abdomen mixing ECG signal processing, comprising:
1Hz IIR high-pass filter removes the baseline drift in AECG signal；
80Hz FIR low pass filter removes the radio-frequency component in AECG signal；
50/60Hz trapper removes Hz noise.
3. the algorithm according to claim 2 for extracting fetal heart frequency by mother's abdomen mixing ECG signal, which is characterized in that
The Fetal ECG signal FECG separation, including TS, TS-PCA, TS-ICA algorithm, for TS, TS-PCA, TS-ICA algorithm
For isolated FECG signal as input, the method for reusing PCA and ICA carries out secondary FECG separation.
4. the algorithm according to claim 3 for extracting fetal heart frequency by mother's abdomen mixing ECG signal, which is characterized in that
FECG of the FQRS detection algorithm synchronous operation in multichannel.
5. the algorithm according to claim 4 for extracting fetal heart frequency by mother's abdomen mixing ECG signal, which is characterized in that
The Kalman filtering merges
1. the FQRS wave position tentatively obtained from each channel, binding signal quality utilize card to the FHR curve tentatively obtained
Thalmann filter optimizes；
2. being weighted, being obtained most using the FHR curve in each channel according to signal quality and the residual error of Kalman filtering
Excellent FHR curve.
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| CN202010267503.0A CN111265204A (en) | 2019-04-30 | 2020-04-08 | Algorithm for extracting fetal heart rate from mother abdomen mixed ECG signal |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110720905A (en) * | 2019-09-20 | 2020-01-24 | 中南大学 | Fetal electrocardiosignal interference suppression system based on array processing |
| CN110974180A (en) * | 2019-12-25 | 2020-04-10 | 索思（苏州）医疗科技有限公司 | Uterine contraction detection device and method based on maternal physiological electric signals |
| CN111317500A (en) * | 2020-02-21 | 2020-06-23 | 孙磊 | Intelligent wearing system based on fetal heart and fetal movement signals |
| CN113907765A (en) * | 2021-10-10 | 2022-01-11 | 北京工业大学 | Noninvasive fetal electrocardiosignal quality evaluation method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112043258A (en) * | 2020-09-30 | 2020-12-08 | 青岛歌尔智能传感器有限公司 | Dynamic heart rate prediction method, device, equipment and readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103610460B (en) * | 2013-12-11 | 2015-10-28 | 哈尔滨工业大学 | A kind of Fetal ECG method for extracting signal based on self adaptation FLANN wave filter |
| CN104027105B (en) * | 2014-04-23 | 2016-08-24 | 河南科技大学 | A kind of novel female fetal electrocardiogram separation method |
| GB2555574A (en) * | 2016-10-25 | 2018-05-09 | Univ Guangdong Technology | Method for blind extraction of fetal electrocardio based on time-frequency conversion |
| CN108013872A (en) * | 2018-01-10 | 2018-05-11 | 北京大学第三医院 | System for maternal fetus rhythm of the heart |
| CN109124622A (en) * | 2018-08-17 | 2019-01-04 | 山东康佑医疗科技有限公司 | A kind of Fetal ECG testing and analysis system and method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110720905A (en) * | 2019-09-20 | 2020-01-24 | 中南大学 | Fetal electrocardiosignal interference suppression system based on array processing |
| CN110720905B (en) * | 2019-09-20 | 2021-10-15 | 中南大学 | Fetal electrocardiosignal interference suppression system based on array processing |
| CN110974180A (en) * | 2019-12-25 | 2020-04-10 | 索思（苏州）医疗科技有限公司 | Uterine contraction detection device and method based on maternal physiological electric signals |
| CN110974180B (en) * | 2019-12-25 | 2023-03-28 | 索思（苏州）医疗科技有限公司 | Uterine contraction detection device and method based on maternal physiological electric signals |
| CN111317500A (en) * | 2020-02-21 | 2020-06-23 | 孙磊 | Intelligent wearing system based on fetal heart and fetal movement signals |
| CN113907765A (en) * | 2021-10-10 | 2022-01-11 | 北京工业大学 | Noninvasive fetal electrocardiosignal quality evaluation method |
| CN113907765B (en) * | 2021-10-10 | 2024-02-23 | 北京工业大学 | Noninvasive fetal electrocardiosignal quality assessment method |
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