Data processing method and system based on uterine electromyogram

Abstract

The invention relates to the technical field of genetic engineering, and particularly discloses a data processing method and system based on a uterine electromyogram. The method comprises the following steps: monitoring myoelectricity acquisition environment data, myoelectricity acquisition equipment data and signal data of each myoelectricity acquisition area; processing the signal data of each myoelectricity acquisition area to obtain a myoelectricity signal stability evaluation index of each time node, processing the myoelectricity acquisition environment data to obtain a myoelectricity acquisition environment interference value of each time node, and processing the myoelectricity acquisition equipment data to obtain a myoelectricity acquisition equipment precision evaluation index of each time node; performing comprehensive analysis to obtain the effectiveness evaluation value of the uterine myoelectricity data of each time node; and screening to obtain each piece of effective myoelectricity data. According to the data processing method and system based on the uterus electromyogram and the storage medium, effectiveness evaluation is carried out on the uterus electromyogram data, and the accuracy of premature delivery prediction can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of genetic engineering, and specifically to a data processing method and system based on uterine electromyogram. Background Art

[0002] Currently, preterm birth prediction is a very important part of the technical field of genetic engineering. With the progress of technology and the growth of demand, improving more user-friendly preterm birth prediction technology has become the norm. Efficient and accurate preterm birth prediction technology is of great significance for reducing the hospitalization rate of preterm birth patients and ensuring the safety of the mother and fetus.

[0003] For example, the invention patent with the publication number CN109891239B is a method and kit for providing preeclampsia assessment and preterm birth prediction, including: developing and training a random forest model to obtain a score metric to distinguish normal subjects, preeclampsia subjects, and preterm birth subjects; evaluating a biomarker panel from a subject to determine the level of each in the biomarker panel; and providing the level of each in the biomarker panel into the random forest model to provide preeclampsia assessment and preterm birth prediction. The biomarker panel includes inhibin βA (activin A) and at least one selected from ADAM metallopeptidase domain 12 (Adam12), body mass index (BMI), and white blood cell count (WBC).

[0004] For example, the invention patent with the publication number CN112213500A is a biomarker and method for predicting preterm birth, including: certain proteins and peptides in a biological sample obtained from a pregnant woman are differentially expressed in pregnant women at high risk of developing or currently experiencing preterm birth relative to a matched control, and a group mixing one or more of these proteins and peptides can be used with relatively high sensitivity and high specificity in a method for determining the probability of preterm birth in a pregnant woman. These disclosed proteins and peptides are used alone or as a group of biomarkers for classifying a test sample, predicting the probability of preterm birth, and monitoring the development of preterm birth in a pregnant woman.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of the present application, it is found that the above technologies have at least the following technical problems: Currently, preterm birth prediction technology pays more attention to predicting preterm birth risk through past medical history, TOCO, ultrasound monitoring of cervical length, and fetal fibronectin detection. However, for asymptomatic pregnant women and pregnant women with high-risk factors, the accuracy of preterm birth prediction is difficult to guarantee. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a data processing method, system, and storage medium based on uterine electromyogram, which can effectively solve the problems involved in the above background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0008] In the first aspect of the present invention, a data processing method based on electrohysterogram is provided, including: dividing the electrohysterogram acquisition area, marking each electrohysterogram acquisition area, and simultaneously monitoring the electrohysterogram acquisition environment data, electrohysterogram acquisition device data, and signal data of each electrohysterogram acquisition area.

[0009] Processing the signal data of each electrohysterogram acquisition area to obtain the electrohysterogram signal stability evaluation index at each time node, processing the electrohysterogram acquisition environment data to obtain the electrohysterogram acquisition environment interference value at each time node, processing the electrohysterogram acquisition device data to obtain the electrohysterogram acquisition device accuracy evaluation index at each time node, and comprehensively analyzing to obtain the electrohysterogram data validity evaluation value at each time node.

[0010] Screening out the valid electrohysterogram data according to the electrohysterogram data validity evaluation value at each time node, and giving feedback according to each valid electrohysterogram data.

[0011] Optionally, the process of processing the signal data of each electrohysterogram acquisition area to obtain the electrohysterogram signal stability evaluation index at each time node is as follows: the signal data includes the signal amplitude during the monitoring period, the signal-to-noise ratio and signal frequency at each time node.

[0012] Extracting the critical signal amplitude and critical signal-to-noise ratio from the electrohysterogram database, and comprehensively analyzing to obtain the electrohysterogram signal stability evaluation index at each time node.

[0013] Optionally, the process of processing the electrohysterogram acquisition environment data to obtain the electrohysterogram acquisition environment interference value at each time node is as follows: the electrohysterogram acquisition environment data includes the temperature, humidity, electromagnetic interference value, and light intensity at each time node.

[0014] Extracting the reference standard temperature, allowable deviation temperature, reference standard humidity, allowable deviation humidity, critical electromagnetic interference value, reference light intensity, and allowable deviation light intensity from the electrohysterogram database, and comprehensively analyzing to obtain the electrohysterogram acquisition environment interference value.

[0015] Optionally, the process of processing the electrohysterogram acquisition device data to obtain the electrohysterogram acquisition device accuracy evaluation index at each time node is as follows: the electrohysterogram acquisition device data includes the amplifier gain, storage capacity, sampling rate, and delay time at each time node.

[0016] Extracting the reference standard amplifier gain, allowable deviation amplifier gain, critical storage capacity, critical sampling rate, and critical delay time from the electrohysterogram database, and comprehensively analyzing to obtain the electrohysterogram acquisition device accuracy evaluation index at each time node.

[0017] Optionally, the comprehensive analysis obtains the evaluation values of the validity of uterine electromyogram data at each time node. The specific analysis process is as follows: According to the electromyogram signal stability evaluation index, the electromyogram acquisition environment interference value, and the electromyogram acquisition device accuracy evaluation index at each time node, the comprehensive analysis obtains the evaluation values of the validity of uterine electromyogram data at each time node. The evaluation values of the validity of uterine electromyogram data are used to quantify the effective degree of uterine electromyogram data.

[0018] Optionally, the effective electromyogram data are screened according to the evaluation values of the validity of uterine electromyogram data at each time node. The specific process is as follows: The evaluation threshold of the validity of uterine electromyogram data is extracted from the uterine electromyogram database. The evaluation values of the validity of uterine electromyogram data at each time node are compared with the evaluation threshold of the validity of uterine electromyogram data. If the evaluation value of the validity of uterine electromyogram data at a certain time node is greater than or equal to the evaluation threshold of the validity of uterine electromyogram data, the uterine electromyogram data at that time node are marked as effective electromyogram data. If the evaluation value of the validity of uterine electromyogram data at a certain time node is less than the evaluation threshold of the validity of uterine electromyogram data, the uterine electromyogram data at that time node are marked as invalid electromyogram data.

[0019] The effective electromyogram data are statistically obtained.

[0020] Optionally, the feedback is performed according to the effective electromyogram data. The specific process is as follows: The effective electromyogram data are constructed into an effective electromyogram data set, and the effective electromyogram data set is stored in the uterine electromyogram database for premature birth judgment.

[0021] Optionally, the evaluation values of the validity of uterine electromyogram data at each time node have the following specific numerical expression:

[0022]

[0023] Among them, Ue j represents the evaluation value of the validity of uterine electromyogram data at the j-th time node, Ps j represents the electromyogram signal stability evaluation index at the j-th time node, En j represents the electromyogram acquisition environment interference value at the j-th time node, Mp j represents the electromyogram device accuracy evaluation index at the j-th time node, θ1 represents the influence factor of the evaluation value of the validity of uterine electromyogram data corresponding to the set electromyogram signal stability evaluation index, θ2 represents the influence factor of the evaluation value of the validity of uterine electromyogram data corresponding to the set electromyogram acquisition environment interference value, θ3 represents the influence factor of the evaluation value of the validity of uterine electromyogram data corresponding to the set electromyogram device accuracy evaluation index, j represents the number of each time node, j = 1, 2, 3,..., n, and n represents the total number of time nodes.

[0024] In a second aspect of the present invention, a data processing system based on electrohysterogram is provided, including: a data acquisition module, which is used to divide the electrohysterogram acquisition area, label each electrohysterogram acquisition area, and simultaneously obtain electrohysterogram acquisition environment data, electrohysterogram acquisition device data, and signal data of each electrohysterogram acquisition area.

[0025] An electrohysterogram data validity analysis module, which is used to process the signal data of each electrohysterogram acquisition area to obtain an electrohysterogram signal stability evaluation index, process the electrohysterogram acquisition environment data to obtain an electrohysterogram acquisition environment interference value, process the electrohysterogram acquisition device data to obtain an electrohysterogram device accuracy evaluation index, and comprehensively analyze to obtain an electrohysterogram data validity evaluation value.

[0026] An electrohysterogram data validity evaluation module, which is used to give feedback according to the electrohysterogram data validity evaluation value.

[0027] Compared with the prior art, the embodiments of the present invention at least have the following advantages or beneficial effects:

[0028] (1) By providing a data processing method, system, and storage medium based on electrohysterogram, the present invention can objectively reflect the authenticity and reliability of electrohysterogram signals, thereby providing more accurate diagnostic basis for doctors, improving the accuracy and reliability of clinical decisions, reducing medical risks, identifying and eliminating invalid or abnormal data, ensuring the quality and accuracy of data, helping to optimize the data processing process, reducing the complexity and workload of data processing, and improving data processing efficiency.

[0029] (2) By evaluating the electrohysterogram signal stability evaluation index at each time node, the present invention can ensure the accuracy and reliability of the signal, ensure the consistency and repeatability of each evaluation, make the electrohysterogram signal stability evaluation results comparable between different time nodes and different individuals, and help researchers objectively compare and analyze the changes in electrohysterogram signals at different research or treatment stages.

[0030] (3) By evaluating the electrohysterogram acquisition environment interference value at each time node, the present invention can identify which time periods or environmental factors have a significant interference on electrohysterogram signals, and thus take targeted measures to reduce these interferences, which helps to improve the signal-to-noise ratio of electrohysterogram signals and make the collected data clearer and more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention is further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the following drawings without creative efforts.

[0032] Figure 1Schematic diagram of the method flow of the present invention.

[0033] Figure 2 Schematic diagram of the connection of system modules of the present invention.

[0034] Figure 3 Schematic diagram of the functional relationship between the evaluation value of the validity of uterine electromyogram data at each time node of the present invention and the evaluation index of the stability of electromyogram signals at each time node. Specific embodiments

[0035] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] As Figure 1 shown, the first aspect of the present invention provides a data processing method based on uterine electromyogram, including: dividing the uterine electromyogram acquisition area, marking it as each electromyogram acquisition area, and simultaneously monitoring the electromyogram acquisition environment data, electromyogram acquisition device data, and signal data of each electromyogram acquisition area.

[0037] Processing the signal data of each electromyogram acquisition area to obtain the evaluation index of the stability of electromyogram signals at each time node, processing the electromyogram acquisition environment data to obtain the interference value of the electromyogram acquisition environment at each time node, processing the electromyogram acquisition device data to obtain the evaluation index of the accuracy of the electromyogram acquisition device at each time node, and comprehensively analyzing to obtain the evaluation value of the validity of uterine electromyogram data at each time node.

[0038] Screening out each valid electromyogram data according to the evaluation value of the validity of uterine electromyogram data at each time node, and giving feedback according to each valid electromyogram data.

[0039] Specifically, processing the signal data of each electromyogram acquisition area to obtain the evaluation index of the stability of electromyogram signals at each time node, the specific processing process is: the signal data includes the signal amplitude within the monitoring period, as well as the signal-to-noise ratio and signal frequency at each time node.

[0040] Extracting the critical signal amplitude and critical signal-to-noise ratio from the uterine electromyogram database, and comprehensively analyzing to obtain the evaluation index of the stability of electromyogram signals at each time node.

[0041] In a specific embodiment, the signal amplitude refers to the difference between the maximum and minimum values of the voltage of the electrical signal in the electrohysterogram. By monitoring the signal amplitude at different time nodes and acquisition regions, the strength and stability of the electrohysterogram signal can be evaluated. A higher signal amplitude usually means better signal quality and stronger electrohysterogram activity, which can be collected and calculated by an electromyogram device; the signal-to-noise ratio is the ratio of the signal power to the noise power. The signal power can be calculated by measuring the peak value of the signal, while the noise power can be calculated by measuring the peak value of the background noise. The signal-to-noise ratio is an index measuring the ratio between the signal and the noise. A high signal-to-noise ratio means that the useful information in the signal is more prominent and the noise interference is smaller, thus improving the clarity and accuracy of the signal, which can be collected and calculated by an electromyogram device; the signal frequency refers to the frequency characteristics of the electrical signal generated during the excitation and contraction of the uterine smooth muscle. The frequency characteristics of the electrohysterogram signal reflect the excitation and contraction state of the uterine smooth muscle. By monitoring the signal components at different frequencies, different aspects of the electrohysterogram activity can be understood, such as the frequency, intensity, and duration of contraction, etc., which can be collected by an electromyogram device.

[0042] Among them, the evaluation index of the stability of the electromyogram signal at each time node has a specific numerical expression as follows:

[0043]

[0044] Among them, Ps j represents the evaluation index of the stability of the electromyogram signal at the j-th time node, F i represents the signal amplitude of the i-th electromyogram acquisition region, F0 represents the critical signal amplitude, e represents the natural constant, Z ij represents the signal-to-noise ratio of the i-th electromyogram acquisition region at the j-th time node, Z0 represents the critical signal-to-noise ratio, Q ij represents the signal frequency of the i-th electromyogram acquisition region at the j-th time node, ω1 represents the influence factor of the electromyogram signal stability evaluation corresponding to the set signal amplitude, ω2 represents the influence factor of the electromyogram signal stability evaluation corresponding to the set signal-to-noise ratio, ω3 represents the influence factor of the electromyogram signal stability evaluation corresponding to the set signal frequency, i represents the number of each electromyogram acquisition region, i = 1, 2, 3,..., m, m represents the total number of electromyogram acquisition regions, j represents the number of each time node, j = 1, 2, 3,..., n, and n represents the total number of time nodes.

[0045] The algorithm in this embodiment combines signal amplitude, signal-to-noise ratio, and signal frequency to comprehensively analyze and obtain the myoelectric signal stability evaluation index at each time node. The enhancement of signal amplitude usually means an increase in signal strength, which helps to improve the signal-to-noise ratio because the increase in signal strength is more significant relative to the noise. In some cases, as the labor process progresses, the amplitude of the uterine myoelectric signal may increase, and the frequency may also increase, which reflects the enhancement of uterine smooth muscle activity. When collecting and analyzing uterine myoelectric signals, it is necessary to select an appropriate frequency range to extract the signals to exclude noise interference. If the selected frequency range is too wide, it may contain too many noise components, resulting in a decrease in the signal-to-noise ratio. Comprehensive analysis can obtain a more comprehensive myoelectric signal stability evaluation index.

[0046] Table 1 Example data of the myoelectric signal stability evaluation index at each time node

[0047]

[0048]

[0049] As shown in Table 1, the myoelectric signal stability evaluation index at each time node is jointly determined by the signal amplitude, signal-to-noise ratio, and signal frequency. In a specific embodiment, m = 1, n = 5, the critical signal amplitude is 100 μV, the critical signal-to-noise ratio is 60 dB, the influence factor of the myoelectric signal stability evaluation corresponding to the set signal amplitude is 0.3, the influence factor of the myoelectric signal stability evaluation corresponding to the set signal-to-noise ratio is 0.4, and the influence factor of the myoelectric signal stability evaluation corresponding to the set signal frequency is 0.3. This formula takes into account three key factors, namely the signal amplitude, signal-to-noise ratio, and signal frequency of each myoelectric acquisition area at each time node, and can select the optimal acquisition area to obtain more accurate and reliable signals. It can also adjust the parameters of the acquisition device, such as gain, filter, etc., to optimize the signal quality, can detect preterm labor signs earlier, provide strong support for clinical intervention, can also reflect the progress of labor and more accurately predict the time and mode of delivery, and at the same time helps to reflect the fetal state. For example, a decrease in the signal-to-noise ratio may mean that the fetus is in a state of hypoxia or distress and timely measures need to be taken for intervention. By standardizing the signal amplitude, signal-to-noise ratio, and signal frequency of each myoelectric acquisition area at each time node, ensuring that they are compared on the same magnitude, the fairness and comparability of the evaluation are improved. At the same time, the setting of F0 and Z0 helps to avoid inaccurate signal monitoring caused by too large signal amplitude and too low signal-to-noise ratio. By weighting the influences of the signal amplitude, signal-to-noise ratio, and signal frequency of each myoelectric acquisition area at each time node, their relative importance in the evaluation index is reflected, and the weights of different factors can be adjusted according to different needs, making the model have good adaptability. It is not difficult to see that the larger the signal amplitude or the signal-to-noise ratio or the smaller the signal frequency deviation, the larger the myoelectric signal stability evaluation index. By evaluating the myoelectric signal stability evaluation index at each time node, the accuracy and reliability of the signal can be ensured, and a set of standardized evaluation processes can be established, including steps such as electrode placement, signal acquisition, processing, and analysis, to ensure the consistency and repeatability of each evaluation, making the myoelectric signal stability evaluation results comparable between different time nodes and different individuals, and helping researchers or clinicians to objectively compare and analyze the changes in myoelectric signals at different research or treatment stages.

[0050] In a specific embodiment, the value ranges of the influencing factors for evaluating the stability of the EMG signal corresponding to the signal amplitude, signal-to-noise ratio, and signal frequency are between 0 and 1, which represent the numerical values of the influence degrees of the signal amplitude, signal-to-noise ratio, and signal frequency on the EMG signal stability evaluation index. Each influencing factor for evaluating the stability of the EMG signal can be obtained from the uterine EMG database. By adjusting the values of the influencing factors, the influence degrees of different factors on the final EMG signal stability evaluation index can be flexibly adjusted. Their corresponding relationship can be a pre-set mapping relationship. For example, the signal amplitude, signal-to-noise ratio, and signal frequency form a mapping set with the weight factors corresponding to the pre-set signal amplitude, signal-to-noise ratio, and signal frequency in the uterine EMG database. The real-time signal amplitude, signal-to-noise ratio, and signal frequency are brought into the mapping set to obtain the weight factors corresponding to the signal amplitude, signal-to-noise ratio, and signal frequency. The mapping relationship therein can be a one-to-one or many-to-one relationship.

[0051] Specifically, the EMG acquisition environment interference values at each time node are obtained by processing the EMG acquisition environment data. The specific processing process is as follows: The EMG acquisition environment data includes the temperature, humidity, electromagnetic interference value, and light intensity at each time node.

[0052] The reference standard temperature, allowable deviation temperature, reference standard humidity, allowable deviation humidity, critical electromagnetic interference value, reference light intensity, and allowable deviation light intensity are extracted from the uterine EMG database, and the EMG acquisition environment interference value is obtained through comprehensive analysis.

[0053] In a specific embodiment, temperature and humidity conditions that do not deviate from the reference standard help maintain good contact between the acquisition device and biological tissues, reduce contact resistance, and thus improve the signal transmission quality. The temperature can be obtained through a thermometer, and the humidity can be obtained through a hygrometer; by controlling the electromagnetic interference value in the environment, the interference on the uterine EMG signal can be reduced, and thus the acquisition accuracy can be improved. The electromagnetic interference value can be obtained through an electromagnetic interference tester; light conditions similar to the reference standard help the acquisition personnel better observe and analyze the signal waveform, reduce errors caused by visual fatigue or misjudgment, and the light intensity can be obtained through a light intensity meter.

[0054] Among them, the EMG acquisition environment interference value at each time node, the specific numerical expression is:

[0055]

[0056] Among them, En j represents the EMG acquisition environment interference value at the j-th time node, T j represents the temperature at the j-th time node, T0 represents the reference standard temperature, ΔT represents the allowable deviation temperature, H jrepresents the humidity at the j-th time node, H0 represents the reference standard humidity, ΔH represents the allowable deviation humidity, d j represents the electromagnetic interference value at the j-th time node, d0 represents the critical electromagnetic interference value, f j represents the light intensity at the j-th time node, f0 represents the reference standard light intensity, Δf represents the allowable deviation light intensity, ρ1 represents the influence factor of the myoelectric acquisition environment interference corresponding to the set temperature, ρ2 represents the influence factor of the myoelectric acquisition environment interference corresponding to the set humidity, ρ3 represents the influence factor of the myoelectric acquisition environment interference corresponding to the set electromagnetic interference value, ρ4 represents the influence factor of the myoelectric acquisition environment interference corresponding to the set vibration frequency, j represents the number of each time node, j = 1, 2, 3,..., n, and n represents the total number of time nodes.

[0057] The algorithm of this embodiment combines the temperature, humidity, electromagnetic interference value, and light intensity of each time node, and comprehensively analyzes to obtain the myoelectric acquisition environment interference value of each time node. Generally speaking, as the temperature rises, the water vapor content in the air will increase, resulting in an increase in humidity; an environment with a higher temperature may exacerbate the thermal noise of electronic devices, thereby increasing the level of electromagnetic interference; the influence of humidity on electromagnetic interference is relatively small, but under extreme humidity conditions, such as a very humid or very dry environment, it may affect the performance and stability of electronic devices, thereby indirectly affecting the level of electromagnetic interference. Through comprehensive analysis, a more comprehensive, accurate, and in-depth myoelectric acquisition environment interference value can be obtained.

[0058] It should be noted that in this embodiment, four key factors are considered, namely the temperature, humidity, electromagnetic interference value, and light intensity at each time node. This can reduce the failure rate of the acquisition device, extend the service life of the device, and also reduce the mutual influence between devices, avoid device damage or performance degradation, help reduce interference and errors in the acquisition process, improve the efficiency and accuracy of acquisition, reduce the number of repeated acquisitions caused by errors or interference, thereby saving time and resources, and further ensuring that the collected myometrial electrical signals have high quality and reliability. By standardizing the temperature, humidity, electromagnetic interference value, and light intensity at each time node, it is ensured that they are compared on the same magnitude, improving the fairness and comparability of the evaluation. At the same time, the setting of T0, H0, d0, and f0 helps to solve the problems of device performance and signal quality caused by unreasonable temperature, humidity, and light intensity and excessive electromagnetic interference. By weighting the influences of the temperature, humidity, electromagnetic interference value, and light intensity at each time node, the relative importance of them in the evaluation index is reflected, and the weights of different factors can be adjusted according to different needs, making the formula have good adaptability. It is not difficult to see that the greater the temperature deviation, humidity deviation, electromagnetic interference value, or light intensity deviation, the greater the interference value of the myoelectric acquisition environment. By evaluating the interference value of the myoelectric acquisition environment at each time node, it is possible to identify which time periods or which environmental factors have a significant impact on the myometrial electrical signals, and thus take targeted measures to reduce these interferences, which helps to improve the signal-to-noise ratio of the myometrial electrical signals, make the collected data clearer and more accurate, can also significantly reduce data errors, improve the accuracy of data analysis, help ensure that high-quality data can be obtained for each acquisition, and improve the overall acquisition efficiency.

[0059] In a specific embodiment, the value range of the interference evaluation influence factors of the myoelectric acquisition environment corresponding to the temperature, humidity, electromagnetic interference value, and light intensity is between 0 and 1, which represents the numerical value of the influence degree of the temperature, humidity, electromagnetic interference value, and light intensity on the interference value of the myoelectric acquisition environment. Each interference evaluation influence factor of the myoelectric acquisition environment can be obtained from the myometrial electrical database. By adjusting the value of the influence factor, the influence degree of different factors on the final interference value of the myoelectric acquisition environment can be flexibly adjusted, and its corresponding relationship can be a pre-set mapping relationship. For example, the temperature, humidity, electromagnetic interference value, and light intensity form a mapping set with the weight factors corresponding to the pre-set temperature, humidity, electromagnetic interference value, and light intensity in the myometrial electrical database. Substituting the real-time temperature, humidity, electromagnetic interference value, and light intensity into the mapping set to obtain the weight factors corresponding to the temperature, humidity, electromagnetic interference value, and light intensity, and the mapping relationship therein can be a one-to-one or many-to-one relationship.

[0060] Specifically, the electromyogram acquisition device data is processed to obtain the electromyogram acquisition device accuracy evaluation index at each time node. The specific processing process is as follows: The electromyogram acquisition device data includes the amplifier gain, storage capacity, sampling rate, and delay time at each time node.

[0061] Extract the reference standard amplifier gain, allowable deviation amplifier gain, critical storage capacity, critical sampling rate, and critical delay time from the uterine electromyogram database, comprehensively analyze to obtain the electromyogram acquisition device accuracy evaluation index at each time node, and determine whether maintenance is required according to the electromyogram acquisition device accuracy evaluation index at each time node.

[0062] In a specific embodiment, the amplifier gain generally refers to the voltage gain, that is, the ratio of the output voltage to the input voltage. A signal generator can be used to input a signal with a known amplitude to the amplifier, and then the amplitude of the output signal is measured by the electromyogram device and calculated; the storage capacity refers to the amount of data that can be stored in the internal memory of the uterine electromyogram acquisition device, which can be obtained by querying the internal memory of the device; the sampling rate refers to the number of data points collected per second by the uterine electromyogram acquisition device, and the sampling rate can be determined by inputting a signal with a known frequency to the device and then observing the ratio of the number of collected data points to the time; the delay time refers to the time required from the generation of the uterine electromyogram signal to the acquisition and processing of the signal by the device. A signal generator can be used to input a signal at a known time point to the device, and the time point of the device's response is recorded, and the delay time is determined by calculating the difference between these two time points.

[0063] Among them, the specific numerical expression of the electromyogram device accuracy evaluation index at each time node is:

[0064]

[0065] Among them, Mp j represents the electromyogram device accuracy evaluation index at the j-th time node, e represents the natural constant, b j represents the amplifier gain at the j-th time node, b0 represents the reference standard amplifier gain, Δb represents the allowable deviation amplifier gain, L j represents the storage capacity at the j-th time node, L0 represents the critical storage capacity, S j represents the sampling rate at the j-th time node, S0 represents the critical sampling rate, t j$t_j$ represents the delay time at the $j$-th time node, $t_0$ represents the critical delay time, $\beta_1$ represents the impact factor of the electromyography device accuracy evaluation corresponding to the set amplifier gain, $\beta_2$ represents the impact factor of the electromyography device accuracy evaluation corresponding to the set storage capacity, $\beta_3$ represents the impact factor of the electromyography device accuracy evaluation corresponding to the set sampling rate, $\beta_4$ represents the impact factor of the electromyography device accuracy evaluation corresponding to the set delay time, $j$ represents the number of each time node, $j = 1, 2, 3, \cdots, n$, and $n$ represents the total number of time nodes.

[0066] The algorithm of this embodiment combines the amplifier gain, storage capacity, sampling rate and delay time, and comprehensively analyzes to obtain the electromyography device accuracy evaluation index for each time node. An appropriate amplifier gain can, to a certain extent, suppress noise and improve the signal-to-noise ratio of the signal; the higher the sampling rate, the greater the amount of data collected per unit time, so a larger storage capacity is required to store this data; increasing the sampling rate means that the device needs to process data faster, which may lead to an increase in the delay time. Through comprehensive analysis, a more comprehensive electromyography device accuracy evaluation index can be obtained.

[0067] It should be explained that in this embodiment, four key factors, namely amplifier gain, storage capacity, sampling rate and delay time, are considered. It can enhance the intensity of uterine electromyography signals, make them easier to detect and record, be able to capture more signal details, including the minute changes and rapid fluctuations of uterine electromyography, help to more comprehensively understand the characteristics of uterine electromyography activities, and also ensure that the device can record long-term uterine electromyography signals without interruption of acquisition due to insufficient storage space, which helps to real-time monitor and warn potential abnormal signals, providing the possibility for timely intervention measures. Further, it makes the device more efficient, accurate and reliable in aspects such as data acquisition, storage, processing and analysis. By standardizing the amplifier gain, storage capacity, sampling rate and delay time, it ensures that they are compared on the same magnitude, improving the fairness and comparability of the evaluation. At the same time, the settings of $b_0$, $L_0$, $S_0$ and $t_0$ help to avoid device abnormalities caused by unreasonable amplifier gain, insufficient storage capacity, too low sampling rate and too long delay time. By weighting the impacts of the amplifier gain, storage capacity, sampling rate and delay time, it reflects their relative importance in the evaluation index, and the weights of different factors can be adjusted according to different needs, making the formula have good adaptability. It is not difficult to see that when the deviation of the amplifier gain is smaller, or the storage capacity is larger, or the sampling rate is larger, or the delay time is smaller, the electromyography device accuracy evaluation index is larger. By evaluating the electromyography device accuracy evaluation index for each time node, it can accurately capture uterine electromyography signals, avoid signal distortion or omission, provide reliable monitoring data for doctors, can timely detect and correct possible errors of the device, ensure the accuracy of monitoring results, can provide accurate uterine contraction information for doctors, and further reduce repeated examinations caused by device errors.

[0068] In a specific embodiment, the value ranges of the influencing factors for evaluating the accuracy of the myoelectric device corresponding to the amplifier gain, storage capacity, sampling rate, and delay time are between 0 and 1, which represent the numerical values of the influence degrees of the amplifier gain, storage capacity, sampling rate, and delay time on the myoelectric device accuracy evaluation index. Each influencing factor for evaluating the accuracy of the myoelectric device can be obtained from the uterine myoelectric database. By adjusting the values of the influencing factors, the influence degrees of different factors on the final myoelectric device accuracy evaluation index can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the amplifier gain, storage capacity, sampling rate, and delay time form a mapping set with the weight factors corresponding to the pre-set amplifier gain, storage capacity, sampling rate, and delay time in the uterine myoelectric database. Substituting the real-time amplifier gain, storage capacity, sampling rate, and delay time into the mapping set, the weight factors corresponding to the amplifier gain, storage capacity, sampling rate, and delay time can be obtained, and the mapping relationship therein can be a one-to-one or many-to-one relationship.

[0069] Among them, it is judged whether maintenance is required according to the myoelectric acquisition device accuracy evaluation index of each time node. The specific judgment process is as follows: Extract the myoelectric acquisition device accuracy threshold and maintenance threshold from the uterine myoelectric database, compare the myoelectric acquisition device accuracy evaluation index of each time node with the myoelectric acquisition device accuracy threshold, and count the number of times that the myoelectric acquisition device accuracy evaluation index is less than the myoelectric acquisition device accuracy threshold. If the number of times is greater than or equal to the maintenance threshold, relevant personnel shall be immediately notified for maintenance. If the number of times is less than the maintenance threshold, no additional operation shall be performed.

[0070] Specifically, the uterine myoelectric data validity evaluation value of each time node is obtained through comprehensive analysis. The specific analysis process is as follows: According to the myoelectric signal stability evaluation index, myoelectric acquisition environment interference value, and myoelectric acquisition device accuracy evaluation index of each time node, the uterine myoelectric data validity evaluation value of each time node is obtained through comprehensive analysis. The uterine myoelectric data validity evaluation value is used to quantify the effective degree of the uterine myoelectric data.

[0071] Among them, the uterine myoelectric data validity evaluation value of each time node, the specific numerical expression is:

[0072]

[0073] Among them, Ue j represents the uterine myoelectric data validity evaluation value of the j-th time node, Ps j represents the myoelectric signal stability evaluation index of the j-th time node, En j represents the myoelectric acquisition environment interference value of the j-th time node, Mp jIt represents the myoelectric device accuracy evaluation index at the j-th time node. θ1 represents the influencing factor for the evaluation of the validity of uterine myoelectric data corresponding to the set myoelectric signal stability evaluation index. θ2 represents the influencing factor for the evaluation of the validity of uterine myoelectric data corresponding to the set interference value of the myoelectric acquisition environment. θ3 represents the influencing factor for the evaluation of the validity of uterine myoelectric data corresponding to the set myoelectric device accuracy evaluation index. j represents the serial number of each time node, j = 1, 2, 3,..., n, and n represents the total number of time nodes.

[0074] As Figure 3 shown, in a specific embodiment, θ1 = 0.4, θ2 = 0.3, θ3 = 0.4, Mp j = 0.5. When En i = 0.1, the functional relationship between the evaluation value of the validity of uterine myoelectric data at each time node and the myoelectric signal stability evaluation index at each time node is shown as curve a; when En i = 0.5, the functional relationship between the evaluation value of the validity of uterine myoelectric data at each time node and the myoelectric signal stability evaluation index at each time node is shown as curve b; when En i = 1, the functional relationship between the evaluation value of the validity of uterine myoelectric data at each time node and the myoelectric signal stability evaluation index at each time node is shown as curve c.

[0075] The algorithm of this embodiment combines the myoelectric signal stability evaluation index, the interference value of the myoelectric acquisition environment, and the myoelectric acquisition device accuracy evaluation index at each time node, and comprehensively analyzes to obtain the evaluation value of the validity of uterine myoelectric data at each time node. The higher the environmental interference value, the worse the stability of the uterine myoelectric signal. Because interference factors will cause an increase in signal fluctuations, thereby reducing the signal stability; the higher the device accuracy evaluation index, the more stable the acquired uterine myoelectric signal, because high-precision devices can collect and record signals more accurately, reducing errors and distortions; and in the case of greater environmental interference, even if the device accuracy is higher, the signal quality may still decline due to excessive interference. Comprehensive analysis can obtain a more comprehensive evaluation value of the validity of uterine myoelectric data.

[0076] It should be noted that in this embodiment, three key factors are considered, namely, the myoelectric signal stability evaluation index at each time node, the myoelectric acquisition environment interference value, and the myoelectric acquisition device accuracy evaluation index. The best acquisition timing can be selected to ensure acquisition under the conditions of stable signal, low environmental interference, and high device accuracy, which helps to improve the acquisition efficiency, reduce unnecessary repeated acquisitions, can optimize the acquisition environment, reduce the influence of external factors such as electromagnetic interference and noise, helps to ensure that the device is always in the best working state, improve the reliability and accuracy of the acquired data, and can provide more comprehensive and accurate myoelectric signal data for doctors. By weighting the influences of the myoelectric signal stability evaluation index at each time node, the myoelectric acquisition environment interference value, and the myoelectric acquisition device accuracy evaluation index, their relative importance in the evaluation index is reflected, and the weights of different factors can be adjusted according to different needs, making the formula have good adaptability. It is not difficult to see that when the myoelectric signal stability evaluation index is larger, or the myoelectric acquisition environment interference value is smaller, or the myoelectric acquisition device accuracy evaluation index is larger, the uterine myoelectric data validity evaluation value is larger. By evaluating the uterine myoelectric data validity evaluation values at each time node, the authenticity and reliability of the uterine myoelectric signal can be objectively reflected, thereby providing more accurate diagnostic basis for doctors, improving the accuracy and reliability of clinical decision-making, reducing medical risks, can identify and eliminate invalid or abnormal data, ensure the quality and accuracy of the data, helps to optimize the data processing process, reduce the complexity and workload of data processing, and improve the data processing efficiency.

[0077] In a specific embodiment, the value ranges of the influencing factors of the uterine myoelectric data validity evaluation corresponding to the myoelectric signal stability evaluation index, the myoelectric acquisition environment interference value, and the myoelectric acquisition device accuracy evaluation index are between 0 and 1, which represent the numerical values of the influence degrees of the myoelectric signal stability evaluation index, the myoelectric acquisition environment interference value, and the myoelectric acquisition device accuracy evaluation index on the uterine myoelectric data validity evaluation value. Each uterine myoelectric data validity evaluation influencing factor can be obtained from the uterine myoelectric database. By adjusting the values of the influencing factors, the influence degrees of different factors on the final uterine myoelectric data validity evaluation value can be flexibly adjusted, and their corresponding relationship can be a pre-set mapping relationship. For example, the myoelectric signal stability evaluation index, the myoelectric acquisition environment interference value, and the myoelectric acquisition device accuracy evaluation index form a mapping set with the weight factors corresponding to the pre-set myoelectric signal stability evaluation index, myoelectric acquisition environment interference value, and myoelectric acquisition device accuracy evaluation index in the uterine myoelectric database. Substituting the real-time myoelectric signal stability evaluation index, myoelectric acquisition environment interference value, and myoelectric acquisition device accuracy evaluation index into the mapping set to obtain the weight factors corresponding to the myoelectric signal stability evaluation index, myoelectric acquisition environment interference value, and myoelectric acquisition device accuracy evaluation index, and the mapping relationship therein can be a one-to-one or many-to-one relationship.

[0078] Specifically, each piece of effective myoelectrical data is screened based on the evaluation values of the uterine myoelectrical data at each time node. The specific process is as follows: Extract the evaluation threshold of the uterine myoelectrical data from the uterine myoelectrical database, compare the evaluation values of the uterine myoelectrical data at each time node with the evaluation threshold of the uterine myoelectrical data. If the evaluation value of the uterine myoelectrical data at a certain time node is greater than or equal to the evaluation threshold of the uterine myoelectrical data, mark the uterine myoelectrical data at that time node as effective myoelectrical data. If the evaluation value of the uterine myoelectrical data at a certain time node is less than the evaluation threshold of the uterine myoelectrical data, mark the uterine myoelectrical data at that time node as invalid myoelectrical data.

[0079] Count each piece of effective myoelectrical data.

[0080] Among them, feedback is performed according to each piece of effective myoelectrical data. The specific process is as follows: Construct an effective myoelectrical data set from each piece of effective myoelectrical data, and store the effective myoelectrical data set in the uterine myoelectrical database for premature birth judgment. That is, extract the features related to premature birth from the uterine myoelectrical database, including time-domain features, frequency-domain features, and non-linear features. Use statistical methods to compare the uterine myoelectrical signal features of pregnant women at different gestational weeks. For example, the Mann-Whitney U test and analysis of variance are used to determine whether there are significant differences. For example, in the analysis of variance, it is necessary to first calculate the mean and variance of the uterine myoelectrical signal features of each group of pregnant women, and then construct an F statistic to test whether there are significant differences between the means of each group. If the value of the F statistic is greater than the critical value or the corresponding P value is less than the significance level, then we can consider that there are significant differences in the uterine myoelectrical signal features of each group of pregnant women. If there are significant differences in the uterine myoelectrical signal features of a certain pregnant woman, notify the relevant medical staff for premature birth warning.

[0081] As Figure 2 shown, the second aspect of the present invention provides a data processing system based on uterine electromyogram, including: a data acquisition module, which is used to divide the uterine myoelectrical acquisition area and mark it as each myoelectrical acquisition area, and at the same time obtain the myoelectrical acquisition environment data, myoelectrical acquisition device data, and signal data of each myoelectrical acquisition area.

[0082] A uterine myoelectrical data validity analysis module, which is used to process the signal data of each myoelectrical acquisition area to obtain a myoelectrical signal stability evaluation index, process the myoelectrical acquisition environment data to obtain a myoelectrical acquisition environment interference value, process the myoelectrical acquisition device data to obtain a myoelectrical device accuracy evaluation index, and comprehensively analyze to obtain a uterine myoelectrical data validity evaluation value.

[0083] A uterine myoelectrical data validity evaluation module, which is used to give feedback according to the uterine myoelectrical data validity evaluation value.

[0084] Uterine electromyogram database, used to store uterine electromyogram-related data, including: critical signal amplitude, critical signal-to-noise ratio, reference standard temperature, allowable deviation temperature, reference standard humidity, allowable deviation humidity, critical electromagnetic interference value, reference light intensity, allowable deviation light intensity, reference standard amplifier gain, allowable deviation amplifier gain, critical storage capacity, critical sampling rate, critical delay time, impact factors for evaluating the validity of uterine electromyogram data corresponding to the set myoelectric signal stability evaluation index, impact factors for evaluating the validity of uterine electromyogram data corresponding to the set myoelectric acquisition environment interference value, impact factors for evaluating the validity of uterine electromyogram data corresponding to the set myoelectric device accuracy evaluation index, myoelectric acquisition device accuracy threshold, maintenance threshold, and uterine electromyogram data validity evaluation threshold and other indicators.

[0085] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A data processing method based on uterine electromyography, characterized in that: include: The uterine electromyography acquisition area is divided and marked into various electromyography acquisition areas, and the electromyography acquisition environment data, electromyography acquisition equipment data and signal data of each electromyography acquisition area are monitored at the same time; The signal data of each electromyography acquisition area is processed to obtain the electromyography signal stability evaluation index at each time node, the electromyography acquisition environment data is processed to obtain the electromyography acquisition environment interference value at each time node, the electromyography acquisition equipment data is processed to obtain the electromyography acquisition equipment accuracy evaluation index at each time node, and the comprehensive analysis is performed to obtain the uterine electromyography data validity evaluation value at each time node; Valid electromyographic data are obtained by screening according to the validity evaluation value of the uterine electromyographic data at each time node, and feedback is provided based on each valid electromyographic data.

2. The data processing method based on uterine electromyography according to claim 1, characterized in that: The signal data of each electromyographic acquisition area is processed to obtain the electromyographic signal stability evaluation index of each time node, and the specific processing process is: The signal data includes the signal amplitude within the monitoring period and the signal-to-noise ratio and signal frequency at each time node; The critical signal amplitude and critical signal-to-noise ratio were extracted from the uterine electromyography database, and the electromyography signal stability evaluation index at each time node was obtained through comprehensive analysis.

3. The data processing method based on uterine electromyography according to claim 1, characterized in that: The electromyography acquisition environment data is processed to obtain the electromyography acquisition environment interference value at each time node, and the specific processing process is: The electromyography acquisition environment data includes temperature, humidity, electromagnetic interference value and light intensity at each time node; The reference standard temperature, allowable deviation temperature, reference standard humidity, allowable deviation humidity, critical electromagnetic interference value, reference light intensity and allowable deviation light intensity were extracted from the uterine electromyography database, and the electromyography acquisition environmental interference value was obtained through comprehensive analysis.

4. The data processing method based on uterine electromyography according to claim 1, characterized in that: The electromyography acquisition device data is processed to obtain the electromyography acquisition device accuracy evaluation index at each time node, and the specific processing process is: The electromyography acquisition device data includes amplifier gain, storage capacity, sampling rate and delay time at each time node; The reference standard amplifier gain, allowable deviation amplifier gain, critical storage capacity, critical sampling rate and critical delay time were extracted from the uterine electromyography database, and the accuracy evaluation index of the electromyography acquisition equipment at each time node was obtained through comprehensive analysis.

5. The data processing method based on uterine electromyography according to claim 4 is characterized in that: The comprehensive analysis obtains the effectiveness evaluation value of uterine electromyography data at each time point, and the specific analysis process is as follows: According to the electromyographic signal stability evaluation index at each time node, the electromyographic acquisition environment interference value and the electromyographic acquisition equipment accuracy evaluation index, a comprehensive analysis is performed to obtain the uterine electromyographic data validity evaluation value at each time node, and the uterine electromyographic data validity evaluation value is used to quantify the effectiveness of the uterine electromyographic data.

6. The data processing method based on uterine electromyography according to claim 5 is characterized in that: The specific process of screening and obtaining each valid electromyographic data according to the effectiveness evaluation value of the uterine electromyographic data at each time node is as follows: Extracting a uterine myoelectric data validity assessment threshold from a uterine myoelectric database, comparing the uterine myoelectric data validity assessment value at each time node with the uterine myoelectric data validity assessment threshold, if the uterine myoelectric data validity assessment value at a certain time node is greater than or equal to the uterine myoelectric data validity assessment threshold, then marking the uterine myoelectric data at the time node as valid myoelectric data, if the uterine myoelectric data validity assessment value at a certain time node is less than the uterine myoelectric data validity assessment threshold, then marking the uterine myoelectric data at the time node as invalid myoelectric data; The effective electromyographic data were obtained by statistics.

7. The data processing method based on uterine electromyography according to claim 6 is characterized in that: The specific process of providing feedback based on each valid electromyographic data is as follows: Each valid electromyographic data is constructed to obtain a valid electromyographic data set, and the valid electromyographic data set is stored in a uterine electromyographic database for use in premature birth judgment.

8. The data processing method based on uterine electromyography according to claim 5, characterized in that: The specific numerical expression of the effectiveness evaluation value of the uterine electromyography data at each time node is: Among them, Ue j Ps represents the validity evaluation value of uterine electromyography data at the jth time node. j Indicates the electromyographic signal stability evaluation index at the jth time node, En j Mp represents the interference value of the electromyography acquisition environment at the jth time node. j represents the electromyographic equipment accuracy evaluation index of the jth time node, θ1 represents the uterine electromyographic data validity evaluation influencing factor corresponding to the set electromyographic signal stability evaluation index, θ2 represents the uterine electromyographic data validity evaluation influencing factor corresponding to the set electromyographic acquisition environment interference value, θ3 represents the uterine electromyographic data validity evaluation influencing factor corresponding to the set electromyographic equipment accuracy evaluation index, j represents the number of each time node, j=1,2,3,...,n, and n represents the total number of time nodes.

9. A system using the data processing method based on uterine electromyography according to any one of claims 1 to 8, characterized in that: include: The data acquisition module is used to divide the uterine electromyography acquisition area, mark it into various electromyography acquisition areas, and simultaneously obtain electromyography acquisition environment data, electromyography acquisition equipment data, and signal data of each electromyography acquisition area; The uterine electromyography data validity analysis module is used to process the signal data of each electromyography acquisition area to obtain the electromyography signal stability evaluation index, process the electromyography acquisition environment data to obtain the electromyography acquisition environment interference value, process the electromyography acquisition equipment data to obtain the electromyography equipment accuracy evaluation index, and comprehensively analyze to obtain the uterine electromyography data validity evaluation value; The uterine electromyography data validity evaluation module is used to provide feedback based on the uterine electromyography data validity evaluation value.

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