Application and Clinical Analysis of Remote Fetal Heart Rate Monitoring Platform in Continuous Fetal Heart Rate Monitoring Images

Abstract

Fetal heart sound is an important part of fetal monitoring and has attracted extensive research and attention from scholars at home and abroad in recent years. The fetal heart rate, extracted from the fetal heart sound signal, is one of the important indicators that reflect the health of the fetus in the womb. In this study, a maternal-fetal Holter monitor based on f ECG technology was used to collect maternal heart rate, fetal heart rate, and uterine contractions signals, isolate the fetal heart rate, and design an algorithm to extract the fetal heart rate baseline, acceleration, variation, wake-up cycle, and nonlinear parameters. Using statistical methods to analyze the average value and range of various characteristic parameters of fetal heart rate under continuous long-term monitoring, the results show that the baseline has a downward trend from 10 o’clock in the night to 4 o’clock in the morning and is the lowest around 2 o’clock in the morning. The area and acceleration time were significantly higher than those in the suspicious group. However, there was no significant difference in the number of acceleration values between the two groups; the proportion of small mutations in the normal group was lower than that of the suspicious group and the proportion of medium mutations was higher than that of the suspicious group. There is no statistically significant difference in maternal age, gestational age at childbirth, pregnancy comorbidities, and complication rates in the five-level interpretation system of ACOG (2009), RCOG (2007), SOGC (2007), and the United States (2007). The difference of pregnancy and parity in various images was statistically significant, P < 0.05. The second type of fetal heart rate monitoring images appeared in the highest among the diagnostic standards, and the difference in the second type of fetal heart rate monitoring images between the various diagnostic standards was statistically significant, P ≤ 0.001.

Application of Remote Fetal Heart Rate Monitoring Based on Internet in Late Pregnancy During the COVID-19 Pandemic

“…The monitoring hardware is the attached CWFM1 microfetal heart monitor, which is designed with 9-chip medical Doppler ultrasound probe, with a wide beam signal range and 1MHz ultrasound frequency, equipped with Bluetooth, which can transmit digital signals to the smartphone APP. The fetal movement information is collected by clicking on the smartphone APP interface, and data upload and result feedback are realized through mobile Internet [34]. It realized that pregnant women do not need to visit the hospital, saving time and economic costs, and at the same time, they can monitor intrauterine fetal abnormalities at home in a timely manner and transmit to the hospital monitoring system in case of abnormalities to obtain feedback from doctors and diagnosis and treatment plans for timely treatment.…”

Heart rate monitoring of physical fitness training load based on wavelet transform

“…inverse transform) is formula (5): $$\begin{equation} f\left( t \right) = \frac{1}{{{C}_\psi }}\mathop \smallint \limits_{ - \infty }^\infty \mathop \smallint \limits_{ - \infty }^\infty \frac{1}{{|a{|}^2}}{W}_f\left( {a,b} \right)\psi \left( {\frac{{t - b}}{a}} \right)dadb \end{equation}$$The CWT coefficients have a large amount of redundancy, which is its disadvantage in terms of computational savings during decomposition and reconstruction. However, on the other hand, we can use the redundancy of CWT to achieve the purpose of signal denoising and data recovery, from this aspect, redundancy is an irreplaceable advantage of CWT, which can serve for signal processing [16–18].…”

“…According to the Mallat algorithm, the signal f (t) can be decomposed into the sum of approximate components and detail components of different frequencies in the $$\{ {{V}_j,j \in z} \}$$ space [15, 16], that is, the Hilbert space is decomposed into the orthogonal sum of the wavelet subspace $${W}_j,j \in z$$, namely $${L}^2( R ) = \mathop \oplus \limits_{j \in z} {W}_j\ j \in z$$…”

“…The CWT coefficients have a large amount of redundancy, which is its disadvantage in terms of computational savings during decomposition and reconstruction. However, on the other hand, we can use the redundancy of CWT to achieve the purpose of signal denoising and data recovery, from this aspect, redundancy is an irreplaceable advantage of CWT, which can serve for signal processing [16][17][18]. Decomposition and Reconstruction of Signals According to the Mallat algorithm, the signal f(t) can be decomposed into the sum of approximate components and detail components of different frequencies in the {V j , j ∈ z} space [15,16], that is, the Hilbert space is decomposed into the orthogonal sum of the wavelet subspace W j , j ∈ z, namely…”

Analysis of Transmitted and Received ECG Signal Based on Internet of Thing Using Web Browser and Server-Client HTML Protocol

“…In this study, the results were quite accurate even though there was a fairly poor QoS value. In this study, only the BPM value is displayed without displaying the form of the ECG signal in real time [13] [14]. Then, in 2019, Tamanna Shaown et al, conducted research on IoT-based ECG monitoring for Smart Healthcare [15].…”