CN116831546A - Method, device, equipment and storage medium for predicting abnormal degree of fetal heart rate curve - Google Patents
Method, device, equipment and storage medium for predicting abnormal degree of fetal heart rate curve Download PDFInfo
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Abstract
The embodiment of the application provides a method, a device, equipment and a storage medium for predicting the abnormality degree of a fetal heart rate curve. The method comprises the following steps: acquiring a target fetal heart rate curve, determining missing data of the target fetal heart rate curve, and obtaining a corresponding initial mask; dividing a target fetal heart rate curve into a plurality of target fetal heart rate curve sections, and determining a target mask of each target fetal heart rate curve section based on the initial mask; analyzing each target fetal heart rate curve segment and the corresponding target mask through a target deep learning model to obtain a target baseline fetal heart rate; and obtaining a target variation value and a target acceleration number of each target fetal heart rate curve section based on the target baseline fetal heart rate, and obtaining target anomaly of the target fetal heart rate curve based on the target variation value and the target acceleration number. The embodiment of the application aims to determine the abnormality degree of the fetal heart rate curve by a sectional prediction method, so that the prediction efficiency is improved, and the interpretation in the prediction process is improved.
Description
Technical Field
The present application relates to the field of digital medical technology, and in particular, to a method for predicting an abnormality degree of a fetal heart rate curve, a prediction apparatus for an abnormality degree, a computer device, and a computer readable storage medium.
Background
Clinically, medical staff evaluate the oxygenation of a fetus in utero by mainly interpreting the baseline and variability of the fetal heart rate curve. Because fetal heart monitoring needs to continuously detect the fetal heart rate for a long time, the problems of data loss and large curve individual difference of a fetal heart rate curve can be caused due to inaccurate probe positions or the fetal sleep period and the like, and further difficulty is brought to interpretation of human beings, and poor consistency of interpretation results and high false positive rate are caused.
At present, the method based on clinical guideline interpretation is not easy to accurately interpret fetal heart rate baselines when fetal activity is active or baselines are variable. The auxiliary diagnosis model of the supervised learning algorithm, such as a classification algorithm based on machine learning such as deep learning, decision tree and the like, lacks labeling information consistent with the fetal heart rate curve, has low prediction efficiency and lacks interpretability of the result.
Disclosure of Invention
The application provides a method for predicting the abnormality degree of a fetal heart rate curve, a predicting device, computer equipment and a computer readable storage medium for predicting the abnormality degree of the fetal heart rate curve, which aim to determine the abnormality degree of the fetal heart rate curve by a segmented prediction method, so that the predicting efficiency is improved, and the interpretability in the predicting process is improved.
To achieve the above object, the present application provides a method of predicting an abnormality degree of a fetal heart rate curve, the method comprising:
acquiring a target fetal heart rate curve, and determining missing data of the target fetal heart rate curve to obtain a corresponding initial mask;
dividing the target fetal heart rate curve into a plurality of target fetal heart rate curve sections with first preset duration, and determining a target mask of each target fetal heart rate curve section based on the initial mask;
analyzing each target fetal heart rate curve segment and the corresponding target mask through a target deep learning model to obtain a target baseline fetal heart rate;
and obtaining a target variation value and a target acceleration number of each target fetal heart rate curve section based on the target baseline fetal heart rate, and obtaining target anomaly of the target fetal heart rate curve based on the target variation value and the target acceleration number.
In order to achieve the above object, the present application further provides an anomaly prediction apparatus, including:
the acquisition module is used for acquiring a target fetal heart rate curve, determining missing data of the target fetal heart rate curve and obtaining a corresponding initial mask;
The determining module is used for dividing the target fetal heart rate curve into a plurality of target fetal heart rate curve sections with first preset duration, and determining a target mask of each target fetal heart rate curve section based on the initial mask;
the analysis module is used for analyzing each target fetal heart rate curve section and the corresponding target mask through a target deep learning model to obtain a target baseline fetal heart rate;
the prediction module is used for obtaining a target variation value and a target acceleration number of each target fetal heart rate curve section based on the target baseline fetal heart rate, and obtaining target anomaly degree of the target fetal heart rate curve based on the target variation value and the target acceleration number.
In addition, to achieve the above object, the present application also provides a computer apparatus including a memory and a processor; the memory is used for storing a computer program; the processor is configured to execute the computer program and implement the method for predicting the abnormality degree of the fetal heart rate curve according to any one of the embodiments of the present application when executing the computer program.
In addition, to achieve the above object, the present application further provides a computer readable storage medium storing a computer program, where the computer program when executed by a processor causes the processor to implement the steps of the method for predicting the degree of abnormality of a fetal heart rate curve according to any one of the embodiments provided by the present application.
The method for predicting the abnormality degree of the fetal heart rate curve, the abnormality degree predicting device, the computer equipment and the computer readable storage medium disclosed by the embodiment of the application can acquire the target fetal heart rate curve and determine the missing data of the fetal heart rate curve to obtain the corresponding initial mask. Further, the target fetal heart rate curve may be divided into a plurality of target fetal heart rate curve segments of a first predetermined duration, and a target mask for each target fetal heart rate curve segment may be determined based on the initial mask. After each target fetal heart rate curve segment and the corresponding target mask are analyzed through the target deep learning model to obtain a target baseline fetal heart rate, a target variation value and a target acceleration number of each target fetal heart rate curve segment can be obtained based on the target baseline fetal heart rate, and therefore target abnormal degree of a target fetal heart rate curve can be obtained based on the target variation value and the target acceleration number. The embodiment of the application aims at analyzing each target fetal heart rate curve section and a corresponding target mask by utilizing a target deep learning model through a segmentation prediction method to obtain a target baseline fetal heart rate, so as to further realize the prediction of the target abnormal degree of a target fetal heart rate curve. The method provided by the application not only improves the prediction efficiency of the anomaly degree of the target fetal heart rate curve, but also improves the interpretability of the target fetal heart rate curve in the prediction process of the anomaly degree.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic view of a method for predicting abnormality of a fetal heart rate curve according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for predicting an abnormality of a fetal heart rate curve according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of obtaining an initial mask according to an embodiment of the present application;
FIG. 4 is a flow chart of another method for predicting abnormality in a fetal heart rate curve according to an embodiment of the present application;
FIG. 5 is a schematic block diagram of an abnormality prediction apparatus provided by an embodiment of the present application;
fig. 6 is a schematic block diagram of a computer device provided by an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations. In addition, although the division of the functional modules is performed in the apparatus schematic, in some cases, the division of the modules may be different from that in the apparatus schematic.
The term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
As shown in fig. 1, the method for predicting the abnormality degree of the fetal heart rate curve provided by the embodiment of the application can be applied to an application environment as shown in fig. 1. The application environment includes a terminal device 110 and a server 120, where the terminal device 110 may communicate with the server 120 through a network. Specifically, the server 120 may obtain a target fetal heart rate curve, determine missing data of the target fetal heart rate curve, and obtain a corresponding initial mask; dividing the target fetal heart rate curve into a plurality of target fetal heart rate curve sections with first preset duration, and determining a target mask of each target fetal heart rate curve section based on the initial mask; analyzing each target fetal heart rate curve segment and the corresponding target mask through a target deep learning model to obtain a target baseline fetal heart rate; and finally, obtaining a target variation value and a target acceleration number of each target fetal heart rate curve section based on the target baseline fetal heart rate, obtaining a target anomaly of the target fetal heart rate curve based on the target variation value and the target acceleration number, and sending the target anomaly to the terminal equipment 110. The server 120 may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms. The terminal device 110 may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the present application is not limited herein.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for predicting an abnormality degree of a fetal heart rate curve according to an embodiment of the application. The method for predicting the abnormality degree of the fetal heart rate curve can be applied to computer equipment, so that more accurate answers based on target questions are obtained.
As shown in fig. 2, the method for predicting the abnormality degree of the fetal heart rate curve includes steps S11 to S14.
Step S11: and obtaining a target fetal heart rate curve, determining missing data of the target fetal heart rate curve, and obtaining a corresponding initial mask.
The target fetal heart rate curve is the fetal heart rate curve of which the anomaly degree is to be judged.
Note that the fetal heart rate curve (Fetal Heart Rate curve, FHR curve) is a graphical representation of the fetal heart rate recorded during delivery of a pregnant woman. The FHR curve is typically monitored by a fetal heart rate monitor and the changes in fetal heart rate are plotted during the monitoring. Thus, FHR curves are an important indicator during labor to help healthcare workers assess fetal health and monitor whether there are potential difficulties or complications. Normally, the FHR curve should exhibit a certain variation, including different modes of baseline heart rate, acceleration, deceleration, etc. The occurrence and variation of these patterns may provide information about the fetus, helping the physician to make the correct interventions and decisions.
It can be understood that in a specific fetal heart monitoring process, situations such as inaccurate probe position of a detection tool, fetal heart position change or fetal sleep period may be encountered, so that a phenomenon of data missing occurs in a fetal heart rate curve, and further the fetal heart rate curve cannot be accurately judged, so that the abnormality degree of the fetal heart rate curve needs to be predicted.
Based on the above reasons, the embodiment of the application can determine the missing data of the target fetal heart rate curve and identify the corresponding initial mask, namely, the corresponding initial mask is obtained. In this way, the actual data and missing data in the target fetal heart rate curve may be distinguished and the missing data may be selectively processed, e.g., filtered or filled.
In the embodiment of the application, the target fetal heart rate curve can be acquired, the missing data of the target fetal heart rate curve can be determined, and the corresponding initial mask can be obtained. Therefore, the missing data can be processed, and the abnormality of the target fetal heart rate curve can be accurately predicted.
Step S12: dividing the target fetal heart rate curve into a plurality of target fetal heart rate curve sections with first preset duration, and determining a target mask of each target fetal heart rate curve section based on the initial mask.
The first preset duration is not limited by the present application, and may be, for example, window 1 And (3) representing.
Further, the target fetal heart rate curve is divided into a plurality of target fetal heart rate curve segments with first preset duration, and the target fetal heart rate curve segments can be expressed by the following formula:
wherein S is w Is a target fetal heart rate curve section, and the duration of each section is Window 1 I.e.
Optionally, the target fetal heart rate curve section comprises a first fetal heart rate curve section and a second fetal heart rate curve section, and the target mask comprises a first mask corresponding to the first fetal heart rate curve section and a second mask corresponding to the second fetal heart rate curve section; determining a target mask for each target fetal heart rate curve segment based on the initial mask is accomplished by the following expression:
wherein, mask i (t) is a first mask;is a second mask; mask missing (t) is an initial mask;is a target fetal heart rate curve segment.
Further, the first fetal heart rate curve section is a curve section corresponding to normal data; the second fetal heart rate curve segment is a curve segment corresponding to the missing data.
In the embodiment of the application, the target fetal heart rate curve can be divided into a plurality of target fetal heart rate curve sections with first preset duration, and the target mask of each target fetal heart rate curve section is determined based on the initial mask so as to be used for processing missing data, thereby realizing more accurate prediction of the abnormal degree of the target fetal heart rate curve.
Step S13: and analyzing each target fetal heart rate curve section and the corresponding target mask through a target deep learning model to obtain a target baseline fetal heart rate.
Specifically, each target fetal heart rate curve segment and the corresponding target mask may be input to a target deep learning model, and further output to obtain a baseline fetal heart rate.
Wherein the baseline fetal heart rate may be expressed by the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,is the baseline fetal heart rate; FHR (t) is the target fetal heart rate curve section; when the set 2 set (t) is the target depth model and the set 2 set (t), outputting the model; mask i (t) is a target mask.
Optionally, to obtain a smoother baseline fetal heart rate, the baseline fetal heart rate may be filtered by a median filter for 10 minutes, to obtain a filtered baseline fetal heart rate.
In the embodiment of the application, each target fetal heart rate curve segment and the corresponding target mask can be analyzed through the target deep learning model to obtain the target baseline fetal heart rate, so as to be used for predicting the abnormal degree of the target fetal heart rate curve.
Step S14: and obtaining a target variation value and a target acceleration number of each target fetal heart rate curve section based on the target baseline fetal heart rate, and obtaining target anomaly of the target fetal heart rate curve based on the target variation value and the target acceleration number.
Specifically, for the calculation of the target variation value: the duration of the target baseline fetal heart rate may be determined, which is typically at least 10 minutes or more. During this time period, fetal heart rate values per minute may be calculated and subtracted from these values to a baseline fetal heart rate, resulting in variability values per minute. The absolute values of the variance values are added and divided by the total number of variance values to obtain the target variance value.
Calculation of target anomaly: the duration of the baseline fetal heart rate may be determined, again typically at least 10 minutes or more. During this period, it is observed whether the fetal heart rate rises above a certain threshold (typically above 15 bpm) for a certain period of time (typically above 15 seconds). And finally, calculating the number of times that the fetal heart rate rises above a threshold value, namely the target acceleration number.
Optionally, the target baseline fetal heart rate includes a first baseline fetal heart rate and a second baseline fetal heart rate, the first baseline fetal heart rate corresponding to the first mask; the second baseline fetal heart rate corresponds to a second mask, and a target variability value and a target acceleration number of each target fetal heart rate curve segment are obtained based on the target baseline fetal heart rate, including: obtaining a first mutation value and a first acceleration number of a corresponding first fetal heart rate curve section based on the first baseline fetal heart rate; obtaining a second variability value and a second acceleration number of the corresponding second fetal heart rate curve segment based on the second baseline fetal heart rate; the target variation value is obtained based on the first variation value and the second variation value, and the target acceleration number is obtained based on the first acceleration number and the second acceleration number.
The first baseline fetal heart rate is a baseline fetal heart rate corresponding to normal data and corresponds to a first mutation value and a first acceleration number; the second baseline fetal heart rate is the baseline fetal heart rate corresponding to the missing data, and corresponds to a second variability value and a second acceleration number.
Therefore, the mutation value and the acceleration number corresponding to different baseline fetal heart rates can be respectively determined through the steps, so that the target mutation value and the target acceleration number can be further determined.
It is understood that the target variation value includes a first variation value and a second variation value; the target acceleration number includes a first acceleration number and a second acceleration number.
Optionally, on the basis of the above embodiment, obtaining the target anomaly degree of the target fetal heart rate curve based on the target variability value and the target acceleration number includes: sequencing the target variation value and the target acceleration number according to a preset rule to obtain a first order and a second order; and obtaining the target anomaly degree based on the first order, the second order, the target variation value and the target acceleration number.
Specifically, the application is not limited to the sorting rule, and for example, sorting from small to large can be performed.
Further, the target degree of anomaly may be obtained by the following formula:
Ab i ＝λ V Ord(V i )+λ A Ord(A i )
wherein Ab i Is the target anomaly degree; ord (V) i ) Represents V i At the position ofIn the order of (3); v (V) i Is the target variation value; a is that i The target acceleration number; ord (A) i ) Representation A i At->In the order of (3); lambda (lambda) V ,λ A Is a preset parameter.
In the embodiment of the application, the target variation value and the target acceleration number of each target fetal heart rate curve section can be obtained based on the target baseline fetal heart rate, and the target abnormal degree of the target fetal heart rate curve can be obtained based on the target variation value and the target acceleration number, so that the target abnormal degree of the target fetal heart rate curve can be predicted.
The method for predicting the abnormal degree of the fetal heart rate curve disclosed by the embodiment of the application can acquire the target fetal heart rate curve, determine the missing data of the fetal heart rate curve and obtain the corresponding initial mask. Further, the target fetal heart rate curve may be divided into a plurality of target fetal heart rate curve segments of a first predetermined duration, and a target mask for each target fetal heart rate curve segment may be determined based on the initial mask. After each target fetal heart rate curve segment and the corresponding target mask are analyzed through the target deep learning model to obtain a target baseline fetal heart rate, a target variation value and a target acceleration number of each target fetal heart rate curve segment can be obtained based on the target baseline fetal heart rate, and therefore target abnormal degree of a target fetal heart rate curve can be obtained based on the target variation value and the target acceleration number. The embodiment of the application aims at analyzing each target fetal heart rate curve section and a corresponding target mask by utilizing a target deep learning model through a segmentation prediction method to obtain a target baseline fetal heart rate, so as to further realize the prediction of the target abnormal degree of a target fetal heart rate curve. The method provided by the application not only improves the prediction efficiency of the anomaly degree of the target fetal heart rate curve, but also improves the interpretability of the target fetal heart rate curve in the prediction process of the anomaly degree.
Referring to fig. 3, fig. 3 is a flowchart illustrating a process of obtaining an initial mask according to an embodiment of the application. As shown in fig. 3, the initial mask may be obtained through steps S141 to S143.
Step S111: and obtaining a target fetal heart rate curve, and dividing the target fetal heart rate curve into a plurality of initial fetal heart rate curve segments based on a first preset threshold value and a first preset condition.
Optionally, before dividing the target fetal heart rate curve into the plurality of initial fetal heart rate curve segments based on the first preset threshold and the first preset condition, the method further comprises: and filtering the target fetal heart rate curve through a median filter with a second preset duration to obtain a filtered target fetal heart rate curve.
The second preset time period is not limited, for example, the second preset time period is 5 minutes, 10 minutes, and the like, and the second preset time period is 5 minutes.
It will be appreciated that since filters are a common signal processing technique, they are used to denoise and smooth curves or signals. Therefore, the embodiment of the application can filter the target fetal heart rate curve based on the median filter so as to realize the operations of removing noise, retaining edges, detail and the like of the target fetal heart rate curve, thereby realizing the smoother filtered target fetal heart rate curve.
Further, based on the above embodiment, the first preset threshold may be expressed as thres 5 The method comprises the steps of carrying out a first treatment on the surface of the The first preset condition may be expressed as:
wherein C is 5 (t) is a first preset condition; FHR (t) is the target fetal heart rate curve; medianFilter 5 (FHR (t)) is a filtered target fetal heart rate curve; thres 5 A first preset threshold value.
Further, the partitioning of the target fetal heart rate curve into several initial fetal heart rate curve segments based on the first preset threshold and the first preset condition may be formulated as follows:
wherein S is 5 Is an initial fetal heart rate curve segment;respectively, different time periods, the application is not limited andC 5 (t)＝True，/>
in the embodiment of the application, the target fetal heart rate curve can be obtained, and the target fetal heart rate curve is divided into a plurality of initial fetal heart rate curve segments based on the first preset threshold value and the first preset condition, so that the initial fetal heart rate curve is processed.
Step S112: based on a plurality of initial fetal heart rate curve segments, obtaining an initial fetal heart rate curve after linear interpolation through linear interpolation.
It can be understood that the linear interpolation is a common difference method, and can achieve the effects of smoothing curves, supplementing missing values and the like, so that the method can process a plurality of initial fetal heart rate curve segments based on the linear interpolation method to obtain initial fetal heart rate curves after the linear interpolation.
Specifically, the initial fetal heart rate curve after linear interpolation can be obtained based on a plurality of initial fetal heart rate curve segments through the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,an initial fetal heart rate curve after linear interpolation; FHR (t) is the target fetal heart rate curve; s is S 5 Is an initial fetal heart rate curve segment; />The application is not limited by the different time Duan Ben respectively,
If S 5 Including the left and right end points of the original curve, defined at the end points
In the embodiment of the application, the missing data can be estimated and supplemented by the method. By using the linear relation among the known data points, the curve can be completely restored, namely the initial fetal heart rate curve after linear interpolation is obtained, the approximate value of the missing data can be deduced, and then the initial mask of the missing data is determined.
Step S113: and determining an initial mask through a second preset threshold value and a second preset condition based on the initial fetal heart rate curve after linear interpolation.
Optionally, before determining the initial mask by the second preset threshold and the second preset condition based on the initial fetal heart rate curve after the linear interpolation, the method further includes: and filtering the linear interpolated initial fetal heart rate curve through a median filter of a third preset duration to obtain a filtered initial fetal heart rate curve.
The second preset time period is not limited, and for example, the second preset time period is 1 minute, 2 minutes, and the like, and the second preset time period is 1 minute.
It will be appreciated that since filters are a common signal processing technique, they are used to denoise and smooth curves or signals. Therefore, the embodiment of the application can filter the initial fetal heart rate curve after linear interpolation based on the median filter so as to realize the operations of removing noise, retaining edges, retaining details and the like of the initial fetal heart rate curve after linear interpolation, thereby realizing the smoother filtered initial fetal heart rate curve.
On the basis of the above embodiment, the second preset threshold value may be expressed as thres 1 The method comprises the steps of carrying out a first treatment on the surface of the The second preset condition may be expressed as:
wherein C is 1 (t) is a second preset condition; FHR (t) is the target fetal heart rate curve;the filtered initial fetal heart rate curve; thres 1 A second preset threshold.
Further, the initial mask may be expressed by the following formula:
Mask missing (t)＝C 5 (t)|C 1 (t)
wherein, mask missing (t) is an initial mask; c (C) 5 (t) is a first preset condition; c (C) 1 And (t) is a second preset condition.
In the embodiment of the application, a target fetal heart rate curve can be obtained, and the target fetal heart rate curve is divided into a plurality of initial fetal heart rate curve segments based on a first preset threshold value and a first preset condition. And obtaining a linear interpolation initial fetal heart rate curve from the initial fetal heart rate curve segments through linear interpolation, and further determining an initial mask through a second preset threshold value and a second preset condition based on the linear interpolation initial fetal heart rate curve.
With continued reference to fig. 4, fig. 4 is a flowchart illustrating another method for predicting an abnormality degree of a fetal heart rate curve according to an embodiment of the present application. As shown in fig. 4, predicting the degree of abnormality of the fetal heart rate curve may be achieved through steps S21 to S23.
Step S21: a target contraction curve associated with the target fetal heart rate curve is acquired.
Step S22: based on the target contraction curve, an early deceleration value and a late deceleration value are determined.
The target uterine contraction curve is the uterine contraction curve of the mother corresponding to the target fetal heart rate curve.
Further, for early deceleration value determination: the time points at which the contraction starts and ends can be found. Since early deceleration refers to a decrease in fetal heart rate that occurs simultaneously with or shortly after onset of uterine contractions and returns to baseline levels before the onset of uterine contractions. The time interval from the onset of uterine contractions to the onset of early deceleration and the duration of early deceleration can thus be calculated. And thus an early deceleration value, i.e. a fetal heart rate decline event occurring at the onset of uterine contractions or later.
Determination of late deceleration values: in the target contraction curve, the time points at which contraction starts and ends can be found. Since late deceleration refers to a decrease in fetal heart rate that occurs simultaneously with or shortly after the onset of uterine contractions and persists for a period of time after the end of uterine contractions. The time interval from the onset of uterine contractions to the occurrence of late deceleration, and the duration of late deceleration, can thus be calculated. And further determines a late deceleration value, i.e. a long duration fetal heart rate decline event that occurs at the onset or later in uterine contractions.
Step S23: the target anomaly of the target fetal heart rate curve is determined based on the early deceleration value, the late deceleration value, the target variability value, and the target acceleration number.
Specifically, determining the target anomaly for the target fetal heart rate curve may be accomplished by the following formula:
Ab i ＝λ V Ord(V i )+λ A Ord(A i )+λ ED Ord(ED i )+λ LD Ord(LD i )
wherein Ab i Is the target anomaly degree; ord (V) i ) Represents V i At the position ofIn the order of (3); v (V) i Is the target variation value; a is that i The target acceleration number; ord (A) i ) Representation A i At->In the order of (3); ED (ED) and method for producing the same i Is an early deceleration value; ord (ED) i ) Indicating ED i At->In the order of (3); LD (laser diode) i Is a late deceleration value; ord (LD) i ) Representing LD i At the position ofIn the order of (3); lambda (lambda) V ,λ A ,λ ED ,λ LD Is a preset parameter.
In the embodiment of the application, the target uterine contraction curve related to the target fetal heart rate curve can be obtained, the early deceleration value and the late deceleration value are determined based on the target uterine contraction curve, and finally the target abnormal degree of the target fetal heart rate curve is determined based on the early deceleration value, the late deceleration value, the target variation value and the target acceleration number.
Referring to fig. 5, fig. 5 is a schematic block diagram of an abnormality prediction apparatus according to an embodiment of the present application. The abnormality prediction device may be configured in a server, and may be configured to perform the aforementioned method of predicting the abnormality of the fetal heart rate curve.
As shown in fig. 5, the abnormality prediction apparatus 200 includes: the system comprises an acquisition module 201, a determination module 202, an analysis module 203 and a prediction module 204.
The acquisition module 201 is configured to acquire a target fetal heart rate curve, determine missing data of the target fetal heart rate curve, and obtain a corresponding initial mask;
a determining module 202, configured to divide the target fetal heart rate curve into a plurality of target fetal heart rate curve segments with a first preset duration, and determine a target mask of each of the target fetal heart rate curve segments based on the initial mask;
the analysis module 203 is configured to analyze each target fetal heart rate curve segment and the corresponding target mask through a target deep learning model to obtain a target baseline fetal heart rate;
the prediction module 204 is configured to obtain a target variability value and a target acceleration number of each of the target fetal heart rate curve segments based on the target baseline fetal heart rate, and obtain a target anomaly of the target fetal heart rate curve based on the target variability value and the target acceleration number.
The obtaining module 201 is further configured to obtain the target fetal heart rate curve, and divide the target fetal heart rate curve into a plurality of initial fetal heart rate curve segments based on a first preset threshold and a first preset condition; obtaining an initial fetal heart rate curve after linear interpolation through linear interpolation based on a plurality of initial fetal heart rate curve segments; and determining the initial mask through a second preset threshold value and a second preset condition based on the initial fetal heart rate curve after the linear interpolation.
The determining module 202 is further configured to filter the target fetal heart rate curve through a median filter of a second preset duration, so as to obtain a filtered target fetal heart rate curve; and filtering the initial fetal heart rate curve after linear interpolation through a median filter with a third preset duration to obtain the filtered initial fetal heart rate curve.
The determining module 202 is further configured to determine a target mask for each of the target fetal heart rate curve segments based on the initial mask, and is implemented by the following expression:
wherein the Mask i (t) is the first mask; the saidIs the second mask; the Mask is provided with missing (t) is the initial mask; said->Is the target fetal heart rate curve section.
The prediction module 204 is further configured to obtain a first variability value and a first acceleration number of the corresponding first fetal heart rate curve segment based on the first baseline fetal heart rate; obtaining a second variation value and a second acceleration number of the corresponding second fetal heart rate curve section based on the second baseline fetal heart rate; the target variation value is obtained based on the first variation value and the second variation value, and the target acceleration number is obtained based on the first acceleration number and the second acceleration number.
The prediction module 204 is further configured to sort the target variation value and the target acceleration number according to a preset rule, so as to obtain a first order and a second order; the target abnormality is obtained based on the first order, the second order, the target variation value, and the target acceleration number.
A prediction module 204, further configured to obtain a target uterine contraction curve associated with the target fetal heart rate curve; determining an early deceleration value and a late deceleration value based on the target Gong Su curve; a target anomaly of the target fetal heart rate curve is determined based on the early deceleration value, the late deceleration value, the target variability value, and the target number of accelerations.
It should be noted that, for convenience and brevity of description, specific working processes of the above-described apparatus and each module, unit may refer to corresponding processes in the foregoing method embodiments, which are not repeated herein.
The methods and apparatus of the present application are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
By way of example, the methods, apparatus described above may be implemented in the form of a computer program that is executable on a computer device as shown in fig. 6.
Referring to fig. 6, fig. 6 is a schematic diagram of a computer device according to an embodiment of the application. The computer device may be a server.
As shown in fig. 6, the computer device includes a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a volatile storage medium, a non-volatile storage medium, and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program comprises program instructions that, when executed, cause the processor to perform any one of a number of methods for predicting the degree of abnormality of a fetal heart rate curve.
The processor is used to provide computing and control capabilities to support the operation of the entire computer device.
The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by a processor, causes the processor to perform any of a number of methods for predicting the degree of abnormality of a fetal heart rate curve.
The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by those skilled in the art that the architecture of the computer device, which is merely a block diagram of some of the structures associated with the present application, is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein in some embodiments the processor is configured to run a computer program stored in the memory to implement the steps of: acquiring a target fetal heart rate curve, and determining missing data of the target fetal heart rate curve to obtain a corresponding initial mask; dividing the target fetal heart rate curve into a plurality of target fetal heart rate curve sections with first preset duration, and determining a target mask of each target fetal heart rate curve section based on the initial mask; analyzing each target fetal heart rate curve segment and the corresponding target mask through a target deep learning model to obtain a target baseline fetal heart rate; and obtaining a target variation value and a target acceleration number of each target fetal heart rate curve section based on the target baseline fetal heart rate, and obtaining target anomaly of the target fetal heart rate curve based on the target variation value and the target acceleration number.
In some embodiments, the processor is further configured to obtain the target fetal heart rate curve and divide the target fetal heart rate curve into a number of initial fetal heart rate curve segments based on a first preset threshold and a first preset condition; obtaining an initial fetal heart rate curve after linear interpolation through linear interpolation based on a plurality of initial fetal heart rate curve segments; and determining the initial mask through a second preset threshold value and a second preset condition based on the initial fetal heart rate curve after the linear interpolation.
In some embodiments, the processor is further configured to filter the target fetal heart rate curve through a median filter of a second preset duration, to obtain a filtered target fetal heart rate curve; and filtering the initial fetal heart rate curve after linear interpolation through a median filter with a third preset duration to obtain the filtered initial fetal heart rate curve.
In some embodiments, the processor is further configured to determine a target mask for each of the target fetal heart rate curve segments based on the initial mask by:
/>
wherein the Mask i (t) is the first mask; the saidIs the second mask; the Mask is provided with missing (t) is the initial mask; said->Is the target fetal heart rate curve section.
In some embodiments, the processor is further configured to obtain a corresponding first variability value and first number of accelerations for the first fetal heart rate curve segment based on the first baseline fetal heart rate; obtaining a second variation value and a second acceleration number of the corresponding second fetal heart rate curve section based on the second baseline fetal heart rate; the target variation value is obtained based on the first variation value and the second variation value, and the target acceleration number is obtained based on the first acceleration number and the second acceleration number.
In some embodiments, the processor is further configured to sort the target variation value and the target acceleration number according to a preset rule, to obtain a first order and a second order; the target abnormality is obtained based on the first order, the second order, the target variation value, and the target acceleration number.
In some embodiments, the processor is further configured to obtain a target contraction curve associated with the target fetal heart rate curve; determining an early deceleration value and a late deceleration value based on the target Gong Su curve; a target anomaly of the target fetal heart rate curve is determined based on the early deceleration value, the late deceleration value, the target variability value, and the target number of accelerations.
The embodiment of the application also provides a computer readable storage medium, and a computer program is stored on the computer readable storage medium, wherein the computer program comprises program instructions, and the program instructions are executed to realize any method for predicting the abnormality degree of the fetal heart rate curve provided by the embodiment of the application.
The computer readable storage medium may be an internal storage unit of the computer device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, which are provided on the computer device.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. A method of predicting an anomaly in a fetal heart rate curve, the method comprising:
acquiring a target fetal heart rate curve, and determining missing data of the target fetal heart rate curve to obtain a corresponding initial mask;
dividing the target fetal heart rate curve into a plurality of target fetal heart rate curve sections with first preset duration, and determining a target mask of each target fetal heart rate curve section based on the initial mask;
analyzing each target fetal heart rate curve segment and the corresponding target mask through a target deep learning model to obtain a target baseline fetal heart rate;
and obtaining a target variation value and a target acceleration number of each target fetal heart rate curve section based on the target baseline fetal heart rate, and obtaining target anomaly of the target fetal heart rate curve based on the target variation value and the target acceleration number.
2. The method of claim 1, wherein the obtaining the target fetal heart rate profile and determining missing data of the target fetal heart rate profile results in a corresponding initial mask, comprising:
acquiring the target fetal heart rate curve, and dividing the target fetal heart rate curve into a plurality of initial fetal heart rate curve segments based on a first preset threshold value and a first preset condition;
Obtaining an initial fetal heart rate curve after linear interpolation through linear interpolation based on a plurality of initial fetal heart rate curve segments;
and determining the initial mask through a second preset threshold value and a second preset condition based on the initial fetal heart rate curve after the linear interpolation.
3. The method of claim 2, wherein prior to dividing the target fetal heart rate curve into a number of initial fetal heart rate curve segments based on a first preset threshold and a first preset condition, further comprising:
filtering the target fetal heart rate curve through a median filter with a second preset duration to obtain a filtered target fetal heart rate curve;
the method for determining the initial mask based on the linear interpolation initial fetal heart rate curve through a second preset threshold value and a second preset condition further comprises:
and filtering the initial fetal heart rate curve after linear interpolation through a median filter with a third preset duration to obtain the filtered initial fetal heart rate curve.
4. The method of claim 1, wherein the target fetal heart rate curve segment comprises a first fetal heart rate curve segment and a second fetal heart rate curve segment, the target mask comprising a first mask corresponding to the first fetal heart rate curve segment and a second mask corresponding to the second fetal heart rate curve segment; said determining a target mask for each of said target fetal heart rate curve segments based on said initial mask is accomplished by the expression:
Wherein the Mask i (t) is the first mask; the saidIs the second mask; the Mask is provided with missing (t) is the initial mask; said->Is the target fetal heart rate curve section.
5. The method of claim 4, wherein the target baseline fetal heart rate comprises a first baseline fetal heart rate and a second baseline fetal heart rate, the first baseline fetal heart rate corresponding to the first mask; the second baseline fetal heart rate corresponds to the second mask, the obtaining, based on the target baseline fetal heart rate, a target variability value and a target acceleration number for each of the target fetal heart rate curve segments includes:
obtaining a first variability value and a first acceleration number of the corresponding first fetal heart rate curve section based on the first baseline fetal heart rate; the method comprises the steps of,
obtaining a second variability value and a second acceleration number of the corresponding second fetal heart rate curve segment based on the second baseline fetal heart rate;
the target variation value is obtained based on the first variation value and the second variation value, and the target acceleration number is obtained based on the first acceleration number and the second acceleration number.
6. The method of claim 5, wherein the deriving the target anomaly of the target fetal heart rate profile based on the target variability value and the target number of accelerations comprises:
Sequencing the target variation value and the target acceleration number according to a preset rule to obtain a first order and a second order;
the target abnormality is obtained based on the first order, the second order, the target variation value, and the target acceleration number.
7. The method of claim 1, wherein the determining a target anomaly of the target fetal heart rate curve based on the target variability value and the target number of accelerations further comprises:
acquiring a target uterine contraction curve associated with the target fetal heart rate curve;
determining an early deceleration value and a late deceleration value based on the target Gong Su curve;
a target anomaly of the target fetal heart rate curve is determined based on the early deceleration value, the late deceleration value, the target variability value, and the target number of accelerations.
8. An abnormality degree prediction apparatus, comprising:
the acquisition module is used for acquiring a target fetal heart rate curve, determining missing data of the target fetal heart rate curve and obtaining a corresponding initial mask;
the determining module is used for dividing the target fetal heart rate curve into a plurality of target fetal heart rate curve sections with first preset duration, and determining a target mask of each target fetal heart rate curve section based on the initial mask;
The analysis module is used for analyzing each target fetal heart rate curve section and the corresponding target mask through a target deep learning model to obtain a target baseline fetal heart rate;
the prediction module is used for obtaining a target variation value and a target acceleration number of each target fetal heart rate curve section based on the target baseline fetal heart rate, and obtaining target anomaly degree of the target fetal heart rate curve based on the target variation value and the target acceleration number.
9. A computer device, comprising: a memory and a processor; wherein the memory is connected to the processor for storing a program, the processor being configured to implement the steps of the method of predicting an abnormality of a fetal heart rate curve according to any one of claims 1-7 by running the program stored in the memory.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to carry out the steps of the method of predicting an abnormality of a fetal heart rate curve as claimed in any one of claims 1 to 7.
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| CN117421548B (en) * | 2023-12-18 | 2024-03-12 | 四川互慧软件有限公司 | Method and system for treating loss of physiological index data based on convolutional neural network |
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