Device and method for improving accuracy of recognizing deceleration of fetal heart rate data

Abstract

The invention relates to the field of biomedical signal processing, in particular to a device for improving accuracy of recognizing deceleration of fetal heart rate data, and a method implemented by the device. By adopting a dynamic threshold value area method, a continuous crest detection method, a mixed deceleration detection method and other methods, a baseline variability part can be effectively rejected, deceleration and the type thereof can be accurately recognized, and the defects that a deceleration recognition error is large, the continuous deceleration cannot be recognized and the baseline variability cannot be rejected, the mixed deceleration cannot be recognized and the like in the conventional method can be avoided. The scheme is suitable for a typical deceleration condition that deceleration independently occurs, and continuous deceleration and mixed deceleration conditions.

Description

technical field [0001] The invention relates to the field of biomedical signal processing, in particular to a device for improving the accuracy of fetal heart rate data deceleration recognition and a method for its realization. Background technique [0002] Fetal heart rate deceleration is the simplest and most effective method used to provide reference and analysis for judging fetal safety during childbirth. During the progress of the entire labor process, about 50% to 70% of births will have fetal heart rate deceleration, which represents The scenarios are early onset deceleration, late onset deceleration and variable deceleration. Premature deceleration is generally used as a reference for the phenomenon caused by fetal head compression, and has little to do with fetal hypoxia, but if it occurs frequently in the early stage of labor, the possibility of fetal hypoxia due to umbilical cord compression should also be considered; Hypoxia leads to vagus hyperactivity and / or m...

AI Technical Summary

Problems solved by technology

[0003] At present, there is no uniform quantitative standard for the judgment of fetal heart rate deceleration and its type in clinical medicine, which leads to the clinical automatic identification of fetal heart rate deceleration data. It is calculated according to the deceleration standards set by the empirical parameters such as the duration of various decelerations and the rate of decline, that is, firstly, the fetal heart rate data is collected through the fetal monitoring module and the baseline of the fetal heart rate is identified, and then the deceleration is based on the empirical parameters of clinical deceleration. Set criteria to count the number of decelerations below baseline and the duration, magnitude, type, etc. of each deceleration

However, in clinical applications, this method of identifying decelerations based on empirical parameters has the following disadvantages: First, there are various types of decelerations in the actual clinical environment, and empirical parameters are neither necessarily accurate nor can cover all situations, which will lead to deceleration identification data Second, this method cannot identify continuous deceleration and eliminate baseline variation, resulting in fewer deceleration data than the actual number and counting baseline variation as deceleration; third, there may be There is a mixture of multiple deceleration types. This method can only identify one type of deceleration data collected, and cannot identify the mixed deceleration data.

[0004] The empirical parameters cannot meet the requirements of automatic identification of deceleration. Identify continuous deceleration and eliminate baseline variation, but cannot identify mixed deceleration and other deficiencies. By using algorithms such as dynamic threshold area method, continuous peak detection and mixed deceleration detection, it can effectively eliminate baseline variation and accurately identify each deceleration and its type

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