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Device and method for filling missing value of fetal heart rate signal

A filling method and signal missing technology, applied in the field of medical signal processing, can solve problems such as poor adaptability and difficulty in realizing multi-segment lost data retrieval, and achieve the effect of improving the accuracy rate and minimizing the optimization iterative solution.

Pending Publication Date: 2022-04-08
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

Most of the existing missing value filling methods use simple interpolation processing, such as cubic spline interpolation, linear interpolation, etc.
This type of algorithm is only suitable for the case of single point loss, and the adaptability is poor. In clinical practice, the data loss of in vitro FHR signal monitoring by Doppler ultrasound and other equipment is relatively serious, and the loss rate can even be as high as 40%. In addition, this class completely ignores the physiological and pathological information that may be contained in the waveform characteristics of the FHR signal.
Therefore, it is difficult to achieve reliable multi-segment lost data retrieval

Method used

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  • Device and method for filling missing value of fetal heart rate signal
  • Device and method for filling missing value of fetal heart rate signal
  • Device and method for filling missing value of fetal heart rate signal

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Embodiment Construction

[0023] The technical solutions of the present invention will be further specifically described below through the embodiments and in conjunction with the accompanying drawings.

[0024] A kind of FHR signal missing value filling method of this embodiment, such as figure 1 shown, including the following steps:

[0025] A kind of FHR signal missing value filling method is characterized in that comprising the following steps:

[0026] S1: Construct a reconstruction model for missing FHR data; specifically:

[0027] 1-1 Obtain FHR signal, record The signal is a one-dimensional incomplete signal with length L;

[0028] 1-2 Set the FHR signal after the missing value is filled to y, approximately expressed as y=Dw, where D∈R L×N is a dictionary, N represents the characteristic atom, w∈R N×1 Represents a sparse coefficient vector;

[0029] 1-3 Considering the reconstruction problem of missing FHR data, the following relationship exists:

[0030]

[0031] where R represents t...

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Abstract

The invention discloses an FHR signal missing value filling device and method. Constructing a reconstruction model of the missing FHR data; optimizing the reconstruction model of the missing FHR data by using a minimization sparse dictionary learning algorithm to obtain an iterative solution formula of a sparse coefficient matrix and a regular term error; and calculating a corresponding optimal dictionary by using the obtained optimal sparse coefficient matrix and the regular term error, and obtaining an FHR signal after missing value filling in combination with a reconstruction model of the missing FHR data. The method is applied to the field of FHR monitoring, is used as a preprocessing link of data analysis, and provides an important reference basis for FHR monitoring.

Description

technical field [0001] The invention relates to the technical field of medical signal processing, in particular to a device and method for filling missing values ​​of fetal heart rate signals. Background technique [0002] Clinically, fetal distress hypoxia is the main cause of adverse events such as fetal and neonatal asphyxia, disability and death, and it often occurs in late pregnancy and labor. The longer the fetal distress, the more serious the harm. Therefore, it is particularly critical to carry out real-time monitoring of the fetal intrauterine status in the third trimester, to detect problems in time and to adopt effective diagnosis and treatment plans. In clinical practice, fetal heart rate (Fetal Heart Rate, FHR) monitoring is a prenatal and intrapartum diagnostic technique widely used by clinical medical staff. The accuracy of this artificial interpretation greatly depends on the doctor's clinical experience and ability level, it is highly subjective, and there...

Claims

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Application Information

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IPC IPC(8): A61B5/024A61B5/00G06F17/16G06F30/30
Inventor 赵治栋张烨菲邓艳军周志鑫张显飞
Owner HANGZHOU DIANZI UNIV