Abstract
Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named “MKNet” and recurrent neural network named “MKRNN” are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification.
Highlights
With the general improvement of people’s living standards in today’s society, people’s demands for health are constantly increasing, especially for the health of the generation.More people want their children to be well cared for during their unborn childbirth
During the development of the fetus, parents hope to be able to grasp the physiological information of the fetus in real time, such as the fetal heart rate and contraction pressure, to help early detection of potential risks, and to guide fetal health development [1]
Recent studies have shown that patients who are clinically diagnosed with high blood pressure and are likely to develop heart disease, after a
Summary
With the general improvement of people’s living standards in today’s society, people’s demands for health are constantly increasing, especially for the health of the generation More people want their children to be well cared for during their unborn childbirth. Professor Johnson has demonstrated the use of remote fetal monitoring methods to allow pregnant women and fetuses to measure important physiological indicators such as blood pressure, blood oxygen, and electrocardiogram at home [3]. This environment that allows pregnant women to feel at ease is more conducive to pregnant women, fetuses, and to detect the condition of the newborncorrectly [4]. The use of remote fetal monitoring systems at home in perinatal pregnant women can improve the quality of perinatal care
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