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Intelligent antenatal fetal heart monitoring model

A model and intelligent technology, applied in the measurement of pulse rate/heart rate, medical science, sensors, etc., can solve the problems of high cost, low specificity, increased stillbirth rate, etc., and achieve improved classification performance, excellent denoising ability, and classification performance Enhanced effect

Pending Publication Date: 2022-03-11
GUANGZHOU SUNRAY MEDICAL APP +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] To sum up, manual interpretation of fetal heart rate monitoring (CTG) has high requirements for doctors, and requires a lot of financial and material resources to train doctors, and it is easy to lead to misdiagnosis. However, the low specificity brought by traditional machine learning application methods is easy to make Increased Cesarean or Stillbirth Rates

Method used

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  • Intelligent antenatal fetal heart monitoring model
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  • Intelligent antenatal fetal heart monitoring model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0039] The present invention provides an intelligent prenatal fetal heart rate monitoring model based on a multimodal feature deep learning strategy. The model includes the following steps:

[0040] S1: Obtain the original CTG signal data including fetal heart rate signal and uterine contraction signal and the basic information of pregnant woman including pregnant woman's age and gestational week;

[0041] S2: Preprocessing the above-mentioned fetal heart rate signal, uterine contraction signal, pregnant woman's age and gestational week to form a multimodal feature case set;

[0042] The preprocessing includes interpolating or deleting the fetal heart rate signal and the uterine contraction signal, and interpolating the age of the pregnant woman.

[0043] The preprocessing also includes segmenting the interpolated or deleted fetal heart rate signal before standardizing the interpolated or deleted fetal heart rate signal to obtain a fetal heart rate signal with a signal length ...

Embodiment 2

[0094] In order to verify the influence of different convolutional neural network structures on the classification performance of the intelligent prenatal fetal heart rate monitoring model of the present invention, the present invention designs eight convolutional neural networks with different structures, and conducts comparative analysis with verification example 2. The experimental results are shown in Table 2.

[0095] Eight different convolutional neural networks are, 6C2D: 6-layer convolutional layer, 2-layer fully connected layer (the same below); 6C3D; 6C4D; 6C6D; 3C5D; 4C5D; 5C5D; 7C5D. The network structure of Verification Example 1 is 6C5D, that is, 6 convolutional layers and 5 fully connected layers. The last neuron of all networks is 2, and the activation function is a fully connected layer of sigmoid to output the probability distribution.

[0096] Table 2 Comparative analysis results of classification performance of convolutional neural networks with different ...

Embodiment 3

[0100] In order to compare the classification effect of this experimental model, the present invention uses KNN, SVM, DT, RF, BP, Bayes, GBDT machine learning models based on clinical features as the model comparison of this experiment. The experimental results are shown in Table 3.

[0101] The k-algorithm of the nearest neighbor (k-Nearest Neighbor, KNN) is a relatively mature method in theory. The idea is that, in a particular space, a sample also belongs to that class, although most of the k samples closest to it belong to that class.

[0102] A vector machine (Support Vector Machine, SVM) is an advanced linear classifier that classifies data according to a supervised learning method, and the maximum resolution is the maximum approximate mid-region distance of the learning samples.

[0103] Backpropagation (Back-propagation, BP) neural network is a multi-layer feed-forward network formed by using the error inversion algorithm. BP can study and store a large number of inp...

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Abstract

The invention discloses an intelligent prenatal fetal heart rate monitoring model which comprises the following steps: preprocessing a fetal heart rate signal, a uterine contraction signal, the age of a pregnant woman and the gestational week to obtain a prenatal standardized fetal heart rate data set, a uterine contraction signal data set and a basic information data set of the pregnant woman; then designing a convolutional neural network to perform high-dimensional feature extraction on the preprocessed fetal heart rate signal and uterine contraction signal; a base classifier LGBM is designed and selected according to the characteristics of the prenatal fetal heart monitoring data; fusing the extracted high-dimensional features of the signals with the preprocessed basic information of the pregnant woman to form multi-modal features, and inputting the multi-modal features into a base classifier LGBM to obtain a classification judgment result; according to the intelligent antenatal fetal heart rate monitoring model, features of four different modes including fetal heart rate signals, uterine contraction signals, pregnant woman age and pregnant weeks are fused, a multi-mode feature deep learning strategy is adopted for training, a design base classifier LGBM is selected for classification and discrimination, and compared with other machine learning models based on the fetal heart rate and uterine contraction signals, the model has the advantages that the model is simple in structure and convenient to use. Compared with other traditional machine learning models based on clinical features, the method has better classification performance compared with deep learning models of different feature combinations.

Description

technical field [0001] The present invention relates to a deep learning strategy, in particular to an intelligent prenatal fetal heart rate monitoring model, which is used for evaluating and intelligently classifying and judging the condition of the prenatal fetus. Background technique [0002] Before and during delivery, intrauterine hypoxia and acidosis may cause fetal distress, leading to fetal death or sequelae to the newborn. In order to improve productivity and protect the health of the fetus and pregnant women, the development of the fetus should be monitored in real time. Once some danger signs appear, the doctor will immediately take emergency measures. [0003] In clinical practice, cardiotocography (CTG) is a technical means of monitoring fetal development. It records the placental curve and production pressure wave, and is the main inspection and testing method for evaluating the status of the fetus in the uterus. It is helpful to detect abnormal problems such a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/024
CPCA61B5/02411A61B5/7264
Inventor 魏航曹珍王国强李朝伟陈沁群李丽黄俊林伙旺
Owner GUANGZHOU SUNRAY MEDICAL APP
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