Doppler Ultrasound Imaging Combined with Fetal Heart Detection in Predicting Fetal Distress in Pregnancy-Induced Hypertension under the Guidance of Artificial Intelligence Algorithm

This study was to improve the feasibility and economic benefits of intelligent medical system Doppler ultrasound (DUS) imaging technology combined with fetal heart detection to predict the fetal distress in pregnancy-induced hypertension (PIH), so as to reduce the risk of deterioration of the patient’s condition. The characteristics of DUS images were analyzed, and a diffusion filter reducing the specificity was adopted to improve the smooth speckle noise of DUS images. 120 pregnant women in hospital were the subjects of the study, all of whom received ultrasound cord blood flow testing and fetal heart monitoring. 88 PIH patients with fetal distress were diagnosed and included in the observation group, and 32 healthy pregnant women tested during the same period were identified as the control group. Clinical data were reviewed and analyzed. The diagnostic rates of fetal distress by simple fetal heart monitoring and DUS detection combined with fetal heart monitoring were compared. The results showed that 26.7% of fetal distress were diagnosed by fetal heart monitoring alone, and 73.3% of fetal distress were diagnosed by combined testing, so the diagnostic accuracy of the combined detection method was greatly higher than the single fetal heart detection ( P < 0.05 ). The Pulsatility index (PI), resistance index (RI), and S/D values detected by the umbilical artery in the observation group were 1.48, 0.85, and 4.31, respectively. The PI, RI, and S/D values detected by the umbilical artery in the control group were 0.96, 0.64, and 3.59, respectively. The results of arterial detection were significantly higher than those of the control group, and the difference was of significant scientific significance ( P < 0.05 ). In summary, the PI and RI values of the middle cerebral artery (MCA) detected by DUS diagnosis can effectively reflect the current status of the fetus in the uterus and reduce the mortality of the fetus. The images guided by DUS imaging technology can clearly show the current status of the fetus in the uterus, effectively improve the medical diagnostic efficiency, and have important reference value for the development of intelligent medical equipment.

The Clinical Application and Prospect of Smart Prenatal Care and Postpartum Recovery

Scientific and technological advancement has increased the requirement for modern medical systems, leading to smartphone-based intelligent prenatal care and postpartum recovery. This kind of prenatal care and postpartum recovery including a remote monitoring system for fetal heart monitoring, blood glucose, and weight overcomes the restrictions of time and space and provides all-round, convenient, rapid, and accurate services to the medical systems, doctors, and pregnant women. This paper reviews the current research on intelligent medical services for pregnant women, particularly for prenatal care and postpartum recovery.

Fetal Heart Rate Extraction Based on Wavelet Transform to Prevent Fetal Distress In Utero

In order to improve the effective extraction of fetal heart rate and prevent fetal distress in utero, a study of fetal heart rate feature extraction based on wavelet transform to prevent fetal distress in utero was proposed. This paper adopts a fetal heart rate detection method based on the maximum value of the binary wavelet transform modulus. The method is simulated by the Doppler fetal heart signal obtained from the clinic. Compared with the original curve, the transformed curve can roughly see the change rule of the original signal and identify the peak point of the signal, but due to the large disturbance of the peak point, the influence on the computer processing is also great. The periodicity of the transformed signal is greatly enhanced, making it easier to deal with the computation. A total of 300 pregnant women with full-term fetal heart monitoring from January 2018 to January 2020 were selected as the research subjects and divided into the observation group and the control group. The observation group consisted of 100 patients with abnormal fetal heart monitoring, and the control group consisted of 200 patients with normal fetal heart monitoring. The uterine contractions and fetal heart rate were recorded, and the incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid and fecal contamination were observed. The incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid fecal stain in the observation group were significantly higher than those in the control group. Fetal heart monitoring can accurately judge the situation of the fetus in pregnant women and timely diagnose the abnormal fetal heart rate, which has a better effect on the prognosis of perinatal infants and can reduce their mortality. It can effectively solve the problems existing in the autocorrelation algorithm and extract the fetal heart rate more accurately. It is an effective improved scheme of fetal heart rate extraction. It is very helpful in preventing fetal distress in utero.

CardiacWave

Using wireless signals to monitor human vital signs, especially heartbeat information, has been intensively studied in the past decade. This non-contact sensing modality can drive various applications from cardiac health, sleep, and emotion management. Under the circumstance of the COVID-19 pandemic, non-contact heart monitoring receives increasingly market demands. However, existing wireless heart monitoring schemes can only detect limited heart activities, such as heart rate, fiducial points, and Seismocardiography (SCG)-like information. In this paper, we present CardiacWave to enable a non-contact high-definition heart monitoring. CardiacWave can provide a full spectrum of Electrocardiogram (ECG)-like heart activities, including the details of P-wave, T-wave, and QRS complex. Specifically, CardiacWave is built upon the Cardiac-mmWave scattering effect (CaSE), which is a variable frequency response of the cardiac electromagnetic field under the mmWave interrogation. The CardiacWave design consists of a noise-resistant sensing scheme to interrogate CaSE and a cardiac activity profiling module for extracting cardiac electrical activities from the interrogation response. Our experiments show that the CardiacWave-induced ECG measures have a high positive correlation with the heart activity ground truth (i.e., measurements from a medical-grade instrument). The timing difference of P-waves, T-waves, and QRS complex is 0.67%, 0.71%, and 0.49%, respectively, and a mean cardiac event difference is within a delay of 5.3 milliseconds. These results indicate that CaridacWave offers high-fidelity and integral heart clinical characteristics. Furthermore, we evaluate the CardiacWave system with participants under various conditions, including heart and breath rates, ages, and heart habits (e.g., tobacco use).

Clifford Wavelet Entropy for Fetal ECG Extraction

Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother ECG during pregnancy. This approach has the advantage of being a reliable and non-invasive technique. In the present paper, a wavelet/multiwavelet method is proposed to perfectly extract the fetal ECG parameters from the abdominal mother ECG. In a first step, due to the wavelet/mutiwavelet processing, a denoising procedure is applied to separate the noised parts from the denoised ones. The denoised signal is assumed to be a mixture of both the MECG and the FECG. One of the well-known measures of accuracy in information processing is the concept of entropy. In the present work, a wavelet/multiwavelet Shannon-type entropy is constructed and applied to evaluate the order/disorder of the extracted FECG signal. The experimental results apply to a recent class of Clifford wavelets constructed in Arfaoui, et al. J. Math. Imaging Vis. 2020, 62, 73–97, and Arfaoui, et al.Acta Appl. Math.2020, 170, 1–35.. Additionally, classical Haar–Faber–Schauder wavelets are applied for the purpose of comparison. Two main well-known databases have been applied, the DAISY database and the CinC Challenge 2013 database. The achieved accuracy over the test databases resulted in Se=100%, PPV=100% for FECG extraction and peak detection.

Safety and efficacy of ferric carboxymaltose in anaemic antenatal women: a prospective study

Background: Anemia is one of the most common nutritional deficiency disorders affecting the pregnant women, and is a worldwide problem. In India, prevalence ranges from 23.6%-61.4% and contributes to 80% of maternal mortality as a direct or indirect cause. This study aims to assess the efficacy and safety of intravenous ferric carboxymaltose in antenatal women with moderate anemia in the second and third trimester of pregnancy.Methods: Prospective clinical study; total of 100 antenatal women with moderate anemia in second and third trimester were included in the study from January 2019 to September 2019 and given 1000mg of intravenous ferric carboxymaltose injection. Efficacy was assessed by rate of improvement in hemoglobin at 3 and 6 weeks post infusion. Safety analysis was done by assessing the adverse drug reactions and fetal heart monitoring during the infusion.Results: A total 100 antenatal women with median gestational age of 28week at presentation received 1000mg of intravenous ferric carboxymaltose. There was rise in hemoglobin from 8.3gm% before injection to 11.12gm% 6 weeks post infusion, showing a mean rise of 2.8gm%. There were no reports of drug related major adverse effects in the mother or the fetus.Conclusions: From this study, ferric carboxymaltose is found to be safe and effective treatment option for anemia in pregnancy.

Design and Evaluation of User Interface Design of Mobile Heart Monitoring Application

Many mobile health applicationsincluding mobile heart monitoring applications are available on Apple App Store, and Google Play Store. However, when the user feels dissatisfied with the user interface design of the mobile heart monitoring application, it impacts user satisfaction level to the extent that the users do not use it anymore. User satisfaction level isa very important factor in usability to identify a mobile heart monitoring application whichis pleasant and comfortable forcontinuous use. This study recruited four cardiologists in a hospital in Palembang, Indonesia for evaluatingtheuser interface design ofa mobile heart monitoring prototype called HeartM. The prototype has several features including measuringpatient’s heart rate and blood pressurerate, treatment record, forum, and chat to ease communication between heart patients and their cardiologist. The focus of the user interface design evaluation are oncontent, visual and navigation aspects.This paper contributes to user interface design knowledge and design recommendations particularly for mobile heart monitoring applications.

Can We Provide Safe Training and Competition for All Athletes? From Mobile Heart Monitoring to Side Effects of Performance-Enhancing Drugs and MicroRNA Research

The foundations of sports cardiology include promoting physical activity and an ability to provide a safe environment for training and competition for all athletes at all levels, from professional to recreational [...]