# Fetal Heart Rate Extraction Based on Wavelet Transform to Prevent Fetal Distress In Utero

Authors: Mengni Zhu; Liping Liu
Journal: Journal of Healthcare Engineering
 (2021)

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## Abstract

In order to improve the effective extraction of fetal heart rate and prevent fetal distress in utero, a study of fetal heart rate feature extraction based on wavelet transform to prevent fetal distress in utero was proposed. This paper adopts a fetal heart rate detection method based on the maximum value of the binary wavelet transform modulus. The method is simulated by the Doppler fetal heart signal obtained from the clinic. Compared with the original curve, the transformed curve can roughly see the change rule of the original signal and identify the peak point of the signal, but due to the large disturbance of the peak point, the influence on the computer processing is also great. The periodicity of the transformed signal is greatly enhanced, making it easier to deal with the computation. A total of 300 pregnant women with full-term fetal heart monitoring from January 2018 to January 2020 were selected as the research subjects and divided into the observation group and the control group. The observation group consisted of 100 patients with abnormal fetal heart monitoring, and the control group consisted of 200 patients with normal fetal heart monitoring. The uterine contractions and fetal heart rate were recorded, and the incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid and fecal contamination were observed. The incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid fecal stain in the observation group were significantly higher than those in the control group. Fetal heart monitoring can accurately judge the situation of the fetus in pregnant women and timely diagnose the abnormal fetal heart rate, which has a better effect on the prognosis of perinatal infants and can reduce their mortality. It can effectively solve the problems existing in the autocorrelation algorithm and extract the fetal heart rate more accurately. It is an effective improved scheme of fetal heart rate extraction. It is very helpful in preventing fetal distress in utero.


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## Body

## 1. Introduction

Intrauterine distress is common in clinical practice, which often endangers the life of the fetus if not treated. With the wide application of electronic monitors in clinic in the past 20 years, the changes of fetal heart baseline rate, variability of baseline rate, and periodic fetal heart rate were recorded through the monitor description curve, which can be used as the basis for predicting or judging fetal intrauterine conditions [1]. Fetal heart rate patterns associated with hypoxia include late deceleration, decreased baseline short-term variability, prolonged deceleration, sinusoidal graph, and baseline heart rate changes [2]. The method of fetal heart rate extraction based on wavelet transform is studied. It can effectively solve the problems of autocorrelation algorithm and extract the fetal heart rate more accurately. It is an effective improved scheme of fetal heart rate extraction. At present, with the rapid development and improvement of fetal heart rate electronic monitoring technology, the application of fetal heart rate electronic monitoring technology in China has been quite popular, and it is currently the most commonly used and most sensitive method for monitoring the fetal intrauterine condition. The wide application of this technology has become an important means to improve the level of perinatal healthcare in China. It is difficult to extract fetal ECG signals from the abdominal surface ECG signals. The main reason is that the fetal ECG signal itself is a very weak signal, which is often interfered by noises such as maternal ECG signals and submerged EMG interference; in particular maternal ECG signal amplitude is greater than the amplitude of fetal ECG signal. In addition, the fetal electrocardiogram and the maternal electrocardiogram overlap in the time domain and frequency domain. The location of the fetal heart is difficult to determine, and the electrical conductivity from the fetus to the abdomen frequently changes during pregnancy. These characteristics make it very difficult to collect and observe fetal ECG signals, and it is difficult to extract clear and stable FECG by conventional methods. Independent component analysis (ICA) is a new technique of Blind Source Separation which has developed in recent years. This method is to find a linear coordinate system and make the generated signals statistically independent of each other as much as possible. Therefore, under certain conditions, the hidden independent source signals can be separated only by observing the signals. ICA technology has been applied to the extraction of fetal electrocardiogram and has achieved certain success. However, traditional ICA methods mostly use gradient method or Newton iteration method to optimize the separation matrix, which is easy to fall into the local optimal value, which affects the separation ability of ICA and cannot guarantee the orderly separation of signals. Intrauterine distress during delivery can lead to perinatal death, neonatal asphyxia, and permanent neurological dysfunction due to intracranial hypoxia. In order to avoid and timely detect fetal distress in utero and improve the quality of the population in China, fetal electronic monitor has been widely used. A normal baseline fetal heart rate can rule out the presence of fetal hypoxia [3]. An abnormal fetal heart rate, and even some types of fetal heart rate maps, can highly suspect that the fetus is at risk but do not always indicate the presence of fetal hypoxemia. The patterns of fetal heart rate associated with hypoxia were late deceleration, decreased baseline short-term variability, prolonged deceleration, and sinusoidal and baseline changes in heart rate. The change of fetal heart rate is the most obvious clinical manifestation of fetal distress in uterus, and it is also one of the main bases for clinical diagnosis. Prenatal fetal heart monitoring is a method to ensure the health of the fetus. This method is widely used in clinical medicine with very high accuracy which is used for fetal distress. Warmerdam et al. began to listen to the fetal heart with the ear on the pregnant woman’s abdominal wall [4]. Kuberan introduced the stethoscope for fetal heart rate auscultation [5]. It was not until Herry et al. reported the electronic monitoring technology of fetal heart rate for the first time that the monitoring of fetal heart rate entered a new era [6]. At present, fetal monitors on the market have more or less broken wires, distortion, doubling and halving, and other problems. When the fetal heart rate changes greatly, the calculation results are not timely or even accurate [7]. Therefore, the method of fetal heart rate extraction based on wavelet transform was studied. It can effectively solve the problems existing in the autocorrelation algorithm and extract the fetal heart rate more accurately, which is an effective improved scheme of fetal heart rate extraction [8]. On the basis of the current research, this paper, based on the wavelet transform of fetal heart rate extraction method, can effectively solve the problems existing in the autocorrelation algorithm; more accurate extraction of fetal heart rate of heart rate is an effective improvement of the extraction of fetal heart rate. It is helpful for the prevention of fetal distress [9].


## 2. Methods

### 2.1. Doppler Principle

When the position of the source and the receiver move relative to each other in a uniform medium, the frequency of the sound wave received by the receiver will change. When the sound source moves toward the receiver, the frequency of the sound wave increases, and when the sound source moves toward the receiver, the frequency of the sound wave decreases [10]. When the sound source is not moving and the object is moving toward the sound source with velocity v, if the sound source continuously emits ultrasonic wave of frequency f1, then the ultrasonic wave of frequency transformation is reflected back from the object, and the following relationship is satisfied:(1)f2=fcc+v1=1+fcv1.If the direction of motion of the sound beam is not consistent with that of the object and the included angle isθ, then only the effective quantity Vcos (θ) of V along the direction of sound velocity is taken. When both the sound source and the object are in motion, it is shown in formulas (2) and (3).(2)f2=,c−vcosθf1c+vcosθ(3)fd=f2−f1=cf12vcosθ,where fd is the Doppler frequency shift. The received ultrasound frequency shift fd reflects the fetal heart beating condition. Since the beating of the fetal heart is periodic, the frequency shift signal also has a certain periodicity. By processing the frequency shift signal, the fetal heart rate can be calculated, which is the basic principle of extracting the fetal heart rate using ultrasonic Doppler technology [11].


### 2.2. Wavelet Transform

If the functionψt∈L2R satisfies ∫−∞+∞ψtdt=0; it is called the fundamental wavelet, the wavelet function ψabt is obtained from the basic wavelet ψt through translation and stretching, as shown in the following equation:(4)ψabt=ψa1t−ba,a>0,b∈R.The wavelet transform definition of the functionft with finite energy is shown in the following equation:(5)WTfa,b=∫−∞+∞fta1ψ∗t−badt,a>0.In use, the wavelet function needs to be discretized, and binary discrete wavelet transform is generally adopted. The corresponding binary discrete wavelet transform is shown in the followingformula:(6)Wf2jk≤ft,ψ2jk≥2−j/2∫−∞+∞ftψ2−jt−kdt.Wavelet transform has the two following properties: (1) linear time-shift invariance; (2) transmissibility of signal discontinuity under different resolutions of wavelet transform, that is, if the signal or its derivative is discontinuous or has sharp changes in some place, then the absolute value of the subsignal obtained by wavelet decomposition will also have local maximum value here. Therefore, the abscess point where the wavelet modulus maximum converges at a fine scale is used to detect the periodicity of the signal [12].


### 2.3. Extraction of Wavelet Transform

The principle of fetal heart rate for meeting the conditions:∫−∞∞θxdx=1, and the smooth real function of, if θsx=s1θsx is recorded, the wavelet function is defined as shown in equations (7) and (8):(7)ψ1x=dxdθx(8)ψ2x=dx2d2θxThen, for a real functionfx∈L2R, the wavelet transform is shown in equations (9) and (10).(9)WS1fx=f×ψs2x=f×sdxdθsx=sdxdf×θsx,WS2fx=f×ψs2x=,(10)f×s2dx2d2θsx=s2dx2d2f×θsx.It can be seen from the basic properties of the function that the extreme point of the first derivative of the function corresponds to the zero of its second derivative, which is also the inflection point of the function. The maximum absolute value of the first derivative corresponds to the mutation, so the maximum amplitude ofWf1x corresponds to the mutation point of fx, and the zero point of Wf2x corresponds to the inflection point of fx×θsx. If the wavelet transform has no modulus maximum on a fine scale, then the function is anisotropic in any domain here [13]. From the point of view of the generation process of fetal heart Doppler echo, fetal heart Doppler signal presents periodic changes due to the contraction of the fetal heart and will be affected by the maternal heart and blood flow. When the Doppler signal sent by the probe encounters the beating of the heart, a periodic frequency shift difference will be generated. When the direction of Doppler signal movement is consistent with the direction of the heart movement, the frequency shift signal will reach the peak point. Therefore, the peak point of the echo signal is detected from the signal by using an appropriate scaling factor and wavelet transform. The time interval between the two peak points is the beating cycle of the fetal heart, and the derivative of the cycle is the fetal heartbeat frequency required in this paper [14].


### 2.4. Extraction Process of Fetal Heart Rate by Wavelet Transform

The main process of fetal heart rate extraction method based on wavelet transform includes prefiltering, averaging, wavelet transform, and detection of wavelet transform modulus maxima at various scales. The purpose of prefiltering is to eliminate the small fluctuation between random points inA/D signal and keep the trend of original signal as far as possible; if the fetal movement cannot be removed, it should be used in the detection of fetal movement, which can be analyzed in the fetal heart rate detection. Due to various reasons, the signal amplitude of ultrasonic Doppler signal is time-varying. In order to measure the intensity of the signal, an average level is required as the basis. Meanwhile, in order to make the signal amplitude reflect the immediate change as far as possible, the method of dynamic exponential weighted average is used as shown in the following formula:(11)Snewavg=M−1×SoldavgM+SnewsigM.Type:Snewavg is the new revised average; Soldavg is the old mean value; and Snewsig is the new sample point. This treatment makes the closer samples have more weight in the mean value. M−1/M is the forgetting factor. Let’s say that the average at time T is Saveg and after n samples, it is Snewsig at the moment T + n; then the weight of Saveg in Snewsig changes to M−1/Mn. The forgetting factor determines how fast the average changes with the signal. The smaller m is, the faster it changes, and m is usually an integer power of 2. By experiment, M = 8 is appropriate. In order to detect the periodicity of the signal, the first derivative of a smooth function should be selected as the wavelet function. Here, the quadratic spline function ψx is selected in this paper; it is the first derivative of the smooth function θx. The Fourier transforms of the wavelet function ψx and smooth function θx are shown in formulas (12) and (13), respectively.(12)ψ⌢ω=iωω/4sinω/44,(13)θ⌢ω=ω/4sinω/44.The transfer function of the corresponding filter is as shown in equations (14) and (15):(14)Hω=eiω/2cosω/23,(15)Gω=4ieiω/2sinω/2.Construct a wavelet with vanishing distance, and its Fourier transform is as shown in the following equation:(16)ψω=4−iωω/4sinω/44exp2−iω.The required filter coefficients can be obtained from the formula of the transfer function, as shown in Table1.Table 1
The filter coefficients.
RejectorDataN−1012hn20.1250.3750.3750.125gn20−0.50.50The quadratic spline waveletψω is taken as the basic wavelet, and the Mallat fast algorithm is adopted to carry out discrete binary wavelet transform for the input windowed signal, as shown in the following equation:(17)S2jf=S2J−1×hj−1,Wf2j=S2J−1×gj−1.Theoretically, it is necessary to calculate the wavelet transform at each scale, but with the increase of the scale, the amount of calculation will also increase. The frequency of fetal heart signal is mainly concentrated in the range of 1.2–15 Hz, and the frequency of input center is set asfci, the required output center frequency is f∞, the scale J takes the largest integer close to log2fci/f∞, the sampling frequency fci=500Hz during fetal heart rate detection, select f∞=8Hz, and then the output pass band is 2f∞=16Hz to include all fetal heart rate frequency information. Therefore, the maximum scale J=log2500/8=6 of multiresolution analysis can be determined. After many experiments, it is proved that only the wavelet transform under the three scale functions of I = 4,5, and 6 can be detected. When the scale parameter is 6, all the positive local peaks detected between adjacent zero crossings of the signal are the positive maximum values, and the interval between adjacent peaks can be calculated to calculate the fetal heart rate.


### 2.5. Basic Information

A total of 300 full-term pregnant women with fetal heart monitoring from January 2018 to January 2020 in the department of obstetrics and gynecology were selected. The pregnant women ranged in age from 18 to 37 years, with an average of 28.9 years with gestation of 36 to 43 weeks and an average of 40.1 weeks. Physical condition of the pregnant women: 300 pregnant women had acephalous pelvis asymmetry and pelvic abnormalities, with no complications of internal medicine and surgery or related complications, and all the fetuses were single fetal head position. Fetal heart rate monitoring: All the above pregnant women started from 32 weeks, and the period was once/2 weeks before 36 weeks, and once/week after 36 weeks. Among them, 100 cases had abnormal fetal heart rate monitoring, and 200 cases had normal fetal heart rate monitoring. The basic data are shown in Table2.Table 2
Basic data of fetal heart rate monitoring.
CycleAverage ageFetal heart rate monitoringAbnormal fetal heart rate monitoringThe fetal heart rate was not abnormal32 weeks28.91 time/2 weeks105034 weeks1 time/2 weeks253036 weeks1 time/2 weeks202038 weeks1 time/week155040 weeks1 time/week102042 weeks1 time/week101544 weeks1 time/week1015Among them, 100 cases of abnormal fetal heart monitoring were taken as the observation group, and the other 200 cases of pregnant women with normal fetal heart monitoring were taken as the control group. An electronic fetal heart monitor was used to monitor the pregnant women after routine blood pressure and pulse measurement. Before monitoring, pregnant women should eat properly without medication, and the indoor environment should be quiet. When the physician monitors the fetal heart, the ultrasonic probe to monitor the fetal heartbeat sound is placed in the loudest place, and the contractions sensor to sense the uterine tension is placed under the uterus with 3 horizontal fingers, and the fetal heart rate is recorded. During monitoring, the pregnant woman should lie on her side or supine after emptying the bladder, and the physician should fix the belt to the pregnant woman’s abdominal wall. The monitoring speed is generally 3en/min, and the monitoring time is about 20 min. During monitoring, if the fetal movement is not obvious or there is no fetal movement, it can be continued to monitor for 20 min according to the wake-sleep cycle, and the fetal movement should be recorded properly. The incidence of fetal distress, neonatal asphyxia during cesarean section, and amniotic fluid fecal contamination was observed. In order to understand the reserve capacity of the fetus, fetal contractions and fetal heart rate should be observed and recorded. In the absence of external load stimulation and contractions, this is a stress-free test. If the fetal heart rate accelerates more than 15/min and the fetus moves at least 3 times during the 20 minutes of fetal heart rate monitoring, it is considered as reactive fetal movement. If the fetal heart rate accelerates less than 15/min and the fetal movement is less than 3 times during the 40 min of fetal heart rate monitoring, or if the fetal heart rate does not accelerate during the fetal movement, it is considered as nonreactive fetal movement. The uterine stress test monitors the load changes of the placenta during transient hypoxia during uterine contraction and records the changes of fetal heart rate at the same time. This is the uterine stress test, and its judgment criteria are as follows: in the case of no obvious variation deceleration or late deceleration, those suggesting good placental function are negative [15]. Half of all contractions with late deceleration or three contractions every 10 minutes were positive. If there is obvious variation deceleration or intermittent late deceleration, it is judged to be suspicious positive. The incidence of fetal distress, cesarean section, neonatal asphyxia, and fecal contamination in the observation group of 100 patients was significantly higher than that in the control group of 200 patients, and the difference was statistically significant (P < 0.01), as shown in Table 3.Table 3
Comparison of pregnancy results between the control group and experimental group.
GroupThe number of casesCesarean sectionAmniotic fluid dung to dyeFetal distress in uteroNeonatal asphyxiaObservation group10084928318Control group2001019257P value＜0.01＜0.01＜0.01＜0.01


## 2.1. Doppler Principle

When the position of the source and the receiver move relative to each other in a uniform medium, the frequency of the sound wave received by the receiver will change. When the sound source moves toward the receiver, the frequency of the sound wave increases, and when the sound source moves toward the receiver, the frequency of the sound wave decreases [10]. When the sound source is not moving and the object is moving toward the sound source with velocity v, if the sound source continuously emits ultrasonic wave of frequency f1, then the ultrasonic wave of frequency transformation is reflected back from the object, and the following relationship is satisfied:(1)f2=fcc+v1=1+fcv1.If the direction of motion of the sound beam is not consistent with that of the object and the included angle isθ, then only the effective quantity Vcos (θ) of V along the direction of sound velocity is taken. When both the sound source and the object are in motion, it is shown in formulas (2) and (3).(2)f2=,c−vcosθf1c+vcosθ(3)fd=f2−f1=cf12vcosθ,where fd is the Doppler frequency shift. The received ultrasound frequency shift fd reflects the fetal heart beating condition. Since the beating of the fetal heart is periodic, the frequency shift signal also has a certain periodicity. By processing the frequency shift signal, the fetal heart rate can be calculated, which is the basic principle of extracting the fetal heart rate using ultrasonic Doppler technology [11].


## 2.2. Wavelet Transform

If the functionψt∈L2R satisfies ∫−∞+∞ψtdt=0; it is called the fundamental wavelet, the wavelet function ψabt is obtained from the basic wavelet ψt through translation and stretching, as shown in the following equation:(4)ψabt=ψa1t−ba,a>0,b∈R.The wavelet transform definition of the functionft with finite energy is shown in the following equation:(5)WTfa,b=∫−∞+∞fta1ψ∗t−badt,a>0.In use, the wavelet function needs to be discretized, and binary discrete wavelet transform is generally adopted. The corresponding binary discrete wavelet transform is shown in the followingformula:(6)Wf2jk≤ft,ψ2jk≥2−j/2∫−∞+∞ftψ2−jt−kdt.Wavelet transform has the two following properties: (1) linear time-shift invariance; (2) transmissibility of signal discontinuity under different resolutions of wavelet transform, that is, if the signal or its derivative is discontinuous or has sharp changes in some place, then the absolute value of the subsignal obtained by wavelet decomposition will also have local maximum value here. Therefore, the abscess point where the wavelet modulus maximum converges at a fine scale is used to detect the periodicity of the signal [12].


## 2.3. Extraction of Wavelet Transform

The principle of fetal heart rate for meeting the conditions:∫−∞∞θxdx=1, and the smooth real function of, if θsx=s1θsx is recorded, the wavelet function is defined as shown in equations (7) and (8):(7)ψ1x=dxdθx(8)ψ2x=dx2d2θxThen, for a real functionfx∈L2R, the wavelet transform is shown in equations (9) and (10).(9)WS1fx=f×ψs2x=f×sdxdθsx=sdxdf×θsx,WS2fx=f×ψs2x=,(10)f×s2dx2d2θsx=s2dx2d2f×θsx.It can be seen from the basic properties of the function that the extreme point of the first derivative of the function corresponds to the zero of its second derivative, which is also the inflection point of the function. The maximum absolute value of the first derivative corresponds to the mutation, so the maximum amplitude ofWf1x corresponds to the mutation point of fx, and the zero point of Wf2x corresponds to the inflection point of fx×θsx. If the wavelet transform has no modulus maximum on a fine scale, then the function is anisotropic in any domain here [13]. From the point of view of the generation process of fetal heart Doppler echo, fetal heart Doppler signal presents periodic changes due to the contraction of the fetal heart and will be affected by the maternal heart and blood flow. When the Doppler signal sent by the probe encounters the beating of the heart, a periodic frequency shift difference will be generated. When the direction of Doppler signal movement is consistent with the direction of the heart movement, the frequency shift signal will reach the peak point. Therefore, the peak point of the echo signal is detected from the signal by using an appropriate scaling factor and wavelet transform. The time interval between the two peak points is the beating cycle of the fetal heart, and the derivative of the cycle is the fetal heartbeat frequency required in this paper [14].


## 2.4. Extraction Process of Fetal Heart Rate by Wavelet Transform

The main process of fetal heart rate extraction method based on wavelet transform includes prefiltering, averaging, wavelet transform, and detection of wavelet transform modulus maxima at various scales. The purpose of prefiltering is to eliminate the small fluctuation between random points inA/D signal and keep the trend of original signal as far as possible; if the fetal movement cannot be removed, it should be used in the detection of fetal movement, which can be analyzed in the fetal heart rate detection. Due to various reasons, the signal amplitude of ultrasonic Doppler signal is time-varying. In order to measure the intensity of the signal, an average level is required as the basis. Meanwhile, in order to make the signal amplitude reflect the immediate change as far as possible, the method of dynamic exponential weighted average is used as shown in the following formula:(11)Snewavg=M−1×SoldavgM+SnewsigM.Type:Snewavg is the new revised average; Soldavg is the old mean value; and Snewsig is the new sample point. This treatment makes the closer samples have more weight in the mean value. M−1/M is the forgetting factor. Let’s say that the average at time T is Saveg and after n samples, it is Snewsig at the moment T + n; then the weight of Saveg in Snewsig changes to M−1/Mn. The forgetting factor determines how fast the average changes with the signal. The smaller m is, the faster it changes, and m is usually an integer power of 2. By experiment, M = 8 is appropriate. In order to detect the periodicity of the signal, the first derivative of a smooth function should be selected as the wavelet function. Here, the quadratic spline function ψx is selected in this paper; it is the first derivative of the smooth function θx. The Fourier transforms of the wavelet function ψx and smooth function θx are shown in formulas (12) and (13), respectively.(12)ψ⌢ω=iωω/4sinω/44,(13)θ⌢ω=ω/4sinω/44.The transfer function of the corresponding filter is as shown in equations (14) and (15):(14)Hω=eiω/2cosω/23,(15)Gω=4ieiω/2sinω/2.Construct a wavelet with vanishing distance, and its Fourier transform is as shown in the following equation:(16)ψω=4−iωω/4sinω/44exp2−iω.The required filter coefficients can be obtained from the formula of the transfer function, as shown in Table1.Table 1
The filter coefficients.
RejectorDataN−1012hn20.1250.3750.3750.125gn20−0.50.50The quadratic spline waveletψω is taken as the basic wavelet, and the Mallat fast algorithm is adopted to carry out discrete binary wavelet transform for the input windowed signal, as shown in the following equation:(17)S2jf=S2J−1×hj−1,Wf2j=S2J−1×gj−1.Theoretically, it is necessary to calculate the wavelet transform at each scale, but with the increase of the scale, the amount of calculation will also increase. The frequency of fetal heart signal is mainly concentrated in the range of 1.2–15 Hz, and the frequency of input center is set asfci, the required output center frequency is f∞, the scale J takes the largest integer close to log2fci/f∞, the sampling frequency fci=500Hz during fetal heart rate detection, select f∞=8Hz, and then the output pass band is 2f∞=16Hz to include all fetal heart rate frequency information. Therefore, the maximum scale J=log2500/8=6 of multiresolution analysis can be determined. After many experiments, it is proved that only the wavelet transform under the three scale functions of I = 4,5, and 6 can be detected. When the scale parameter is 6, all the positive local peaks detected between adjacent zero crossings of the signal are the positive maximum values, and the interval between adjacent peaks can be calculated to calculate the fetal heart rate.


## 2.5. Basic Information

A total of 300 full-term pregnant women with fetal heart monitoring from January 2018 to January 2020 in the department of obstetrics and gynecology were selected. The pregnant women ranged in age from 18 to 37 years, with an average of 28.9 years with gestation of 36 to 43 weeks and an average of 40.1 weeks. Physical condition of the pregnant women: 300 pregnant women had acephalous pelvis asymmetry and pelvic abnormalities, with no complications of internal medicine and surgery or related complications, and all the fetuses were single fetal head position. Fetal heart rate monitoring: All the above pregnant women started from 32 weeks, and the period was once/2 weeks before 36 weeks, and once/week after 36 weeks. Among them, 100 cases had abnormal fetal heart rate monitoring, and 200 cases had normal fetal heart rate monitoring. The basic data are shown in Table2.Table 2
Basic data of fetal heart rate monitoring.
CycleAverage ageFetal heart rate monitoringAbnormal fetal heart rate monitoringThe fetal heart rate was not abnormal32 weeks28.91 time/2 weeks105034 weeks1 time/2 weeks253036 weeks1 time/2 weeks202038 weeks1 time/week155040 weeks1 time/week102042 weeks1 time/week101544 weeks1 time/week1015Among them, 100 cases of abnormal fetal heart monitoring were taken as the observation group, and the other 200 cases of pregnant women with normal fetal heart monitoring were taken as the control group. An electronic fetal heart monitor was used to monitor the pregnant women after routine blood pressure and pulse measurement. Before monitoring, pregnant women should eat properly without medication, and the indoor environment should be quiet. When the physician monitors the fetal heart, the ultrasonic probe to monitor the fetal heartbeat sound is placed in the loudest place, and the contractions sensor to sense the uterine tension is placed under the uterus with 3 horizontal fingers, and the fetal heart rate is recorded. During monitoring, the pregnant woman should lie on her side or supine after emptying the bladder, and the physician should fix the belt to the pregnant woman’s abdominal wall. The monitoring speed is generally 3en/min, and the monitoring time is about 20 min. During monitoring, if the fetal movement is not obvious or there is no fetal movement, it can be continued to monitor for 20 min according to the wake-sleep cycle, and the fetal movement should be recorded properly. The incidence of fetal distress, neonatal asphyxia during cesarean section, and amniotic fluid fecal contamination was observed. In order to understand the reserve capacity of the fetus, fetal contractions and fetal heart rate should be observed and recorded. In the absence of external load stimulation and contractions, this is a stress-free test. If the fetal heart rate accelerates more than 15/min and the fetus moves at least 3 times during the 20 minutes of fetal heart rate monitoring, it is considered as reactive fetal movement. If the fetal heart rate accelerates less than 15/min and the fetal movement is less than 3 times during the 40 min of fetal heart rate monitoring, or if the fetal heart rate does not accelerate during the fetal movement, it is considered as nonreactive fetal movement. The uterine stress test monitors the load changes of the placenta during transient hypoxia during uterine contraction and records the changes of fetal heart rate at the same time. This is the uterine stress test, and its judgment criteria are as follows: in the case of no obvious variation deceleration or late deceleration, those suggesting good placental function are negative [15]. Half of all contractions with late deceleration or three contractions every 10 minutes were positive. If there is obvious variation deceleration or intermittent late deceleration, it is judged to be suspicious positive. The incidence of fetal distress, cesarean section, neonatal asphyxia, and fecal contamination in the observation group of 100 patients was significantly higher than that in the control group of 200 patients, and the difference was statistically significant (P < 0.01), as shown in Table 3.Table 3
Comparison of pregnancy results between the control group and experimental group.
GroupThe number of casesCesarean sectionAmniotic fluid dung to dyeFetal distress in uteroNeonatal asphyxiaObservation group10084928318Control group2001019257P value＜0.01＜0.01＜0.01＜0.01


## 3. Results and Analysis

Fetal heart monitoring is one of the main methods to check fetal survival in utero, and it mainly predicts fetal health by observing the amplitude and frequency of baseline variation of fetal heart rate, the change of fetal heart rate after fetal movement, and the duration of fetal heart rate [16]. Early changes in fetal heart rate can reflect the fetal hypoxia and oxygen supply in utero: Physiological accelerated fetal heart rate can reflect the fetal movement of the central nervous system excitement. If the fetus is hypoxic, the response caused by central suppression is weakened to an unresponsive type. By extending the monitoring time, the positive rate can be reduced, the abnormal condition of the fetus in utero can be found in time, and it is convenient to adopt appropriate measures to terminate pregnancy earlier so as to reduce the fetal asphyxia rate and the neonatal mortality to the greatest extent [17]. Fetal heart monitoring is now the primary monitoring method for prenatal screening and fetal intrauterine abnormalities. Fetal heart monitor can display the changes of fetal heart rate in real time. This continuous monitoring provides a dynamic method for clinical observation of fetal heart work. According to relevant data, most fetuses are accompanied by the phenomenon of accelerated fetal heart rate during fetal movement, which reflects that the fetus is in good condition in utero. If the fetal heart rate is not accelerated when the fetus is moving, it indicates that the fetus has the precursor of intrauterine distress, but the possibility of fetal intrauterine distress in a short time is not great. This study showed that the incidence of fetal distress, neonatal asphyxia, and cesarean section in the normal fetal heart monitoring group was significantly lower than that in the abnormal fetal heart monitoring group, and the difference was statistically significant (P < 0.01). When the fetus shows hypoxia, the blood supply in the body also changes to ensure the blood supply of the heart, in addition to the expansion of the heart and cerebrovascular blood changes to ensure the blood supply of the heart and the expansion of the heart and contraction of other cerebrovascular parts, intestinal peristalsis is accelerated, and the fetus and anus include amniotic fluid feces.Through the simulation of Doppler fetal heart signal obtained from the clinic, the transformed curve is compared with the original curve. Although the original signal can be roughly seen by human eyes and can identify the peak point of the signal, it has a great influence on computer processing due to the large disturbance of the peak point. The periodicity of the transformed signal is greatly enhanced, making it easier to carry out computationally related processing [18]. Therefore, this method can solve the problems of fetal heart rate curve breaking, distortion, and inaccurate detection. After calculating the period of the signal after wavelet transform, the fetal heart rate curve as shown in Figure 1 can be obtained. After analyzing and comparing with the actual fetal heart rate of the original data, it can completely meet the design requirements. In addition, through the processing of multiple actual sample signals, the fetal heart rate obtained conforms to the normal physiological value, and the experimental results prove the effectiveness of the algorithm [19]. This is shown in Figure 1.Figure 1
Fetal heart rate curve.The data acquisition and processing method of the fetal heart rate detection method is based on binary wavelet transform. According to the simulation results, the extraction of fetal heart rate by wavelet transform has the following advantages: the fetal heart rate detected is more accurate, which solves the problems of broken fetal heart rate curve, distortion, and inaccurate detection in the past. The phenomenon of doubling and halving will not occur [20]. Wavelet transform was used to extract the accuracy of heart rate and the wavelet transform. Using a Gaussian function experiment, it is found that the experimental effect of binary wavelet is the best but there is a limitation of a better wavelet as well as how to construct the wavelet function, so the current method based on binary wavelet transform is limited to this, and further research and experiment are needed.


## 4. Conclusions

Putting forward a kind of modulus maxima of binary wavelet transform of heart rate detection method, this method is specific by Doppler from clinical cardiac signal simulation, compared with the original signal curve and original curve after the transformation while employing eye can see the change law and roughly identify the signal peaks, but because of its high peaks disturbance, it also influences computer processing. The periodicity of the transformed signal is greatly enhanced, and it is easier to deal with the computation. A total of 300 pregnant women with full-term fetal heart monitoring from January 2018 to January 2020 were selected as the research subjects and divided into the observation group and the control group. The observation group consisted of 100 patients with abnormal fetal heart monitoring and the control group consisted of 200 patients with normal fetal heart monitoring. The uterine contractions and fetal heart rate were recorded, and the incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid and fecal contamination was observed. The incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid fecal stain in the observation group was significantly higher than that in the control group. Fetal heart rate monitoring can accurately judge the condition of the fetus in the pregnant woman; at the same time, it can diagnose the abnormal fetal heart rate in time, which can improve the prognosis of perinatal children and reduce the mortality rate. The purpose of fetal heart monitoring is to understand the fetal hypoxia in utero and to provide correct help to improve the diagnosis of fetal related diseases. It provides a reliable basis for clinicians to understand the fetal hypoxia in utero and to take timely corrective measures. Because the change of position can compress the umbilical cord at any time, and amniotic fluid effusion after membrane rupture and fetal decline almost all have effects on the umbilical cord, the degree is different. At this time, fetal heart monitoring results will be affected and cannot choose cesarean section too early. Therefore, the results of fetal heart monitoring should be comprehensively analyzed in the future to improve the accuracy of fetal intrauterine conditions and postnatal prediction.



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*Source: 7608785-2021-10-01.xml*