Development of a Smart Mobile Data Module for Fetal Monitoring in E-Healthcare

Clinical efficacy and acceptability of remote fetal heart rate self-monitoring in Southern China

BACKGROUND

Compared to traditional fetal heart rate monitoring (FHR) for the outpatients in clinic, remote FHR monitoring shows real-time assessment of fetal wellbeing at home. The clinical function of remote FHR monitoring in pregnant wome in outpatient is still unclear.

OBJECTIVE

To explore the feasibility of remote FHR self-monitoring in singleton pregnant women from southern China.

STUDY DESIGN

This prospective cohort study was conducted at one tertiary center in southern China. Pregnant women used a mobile cardiotocogram device to measure the FHR at least once a week until delivery in the remote group. For the control group, pregnant women underwent traditional FHR monitoring once a week in the outpatient clinic. The rate of cesarean section, risk of postpartum hemorrhage and adverse neonatal outcomes were compared between the two groups. All the pregnant women completed a questionnaire survey to evaluate their acquisition of remote FHR self-monitoring.

RESULTS

Approximately 500 women were recruited in the remote FHR self-monitoring group (remote group), and 567 women were recruited in the traditional FHR monitoring group (control group). The women in the remote FHR monitoring group were more likely to be nulliparous (P < 0.001), more likely to have a higher education level (P < 0.001) and more likely to be at high risk (P = 0.003). There was no significant difference in the risk of cesarean section (P = 0.068) or postpartum hemorrhage (P = 0.836) between the two groups. No difference in fetal complications was observed across groups, with the exception of the incidence of NICU stays, which was higher in the remote group (12.0% vs. 8.3%, P = 0.044). The questionnaire survey showed that the interval time (P = 0.001) and cost (P = 0.010) of fetal heart rate monitoring were lower in the remote group. Regarding age, prepregnancy BMI, risk factors, education level, maternal risk and household income, senior high school (OR 2.86, 95% CI 1.67-4.90, P < 0.001), undergraduate (OR 2.96, 95% CI 1.73-5.06, P < 0.001), advanced maternal age (OR 1.42, 95% CI 1.07-1.89, P = 0.015) and high-risk pregnancy (OR 1.61, 95% CI 1.11-2.35, P = 0.013) were independent factors for pregnant women to choose remote fetal monitoring. Multiparty (OR 0.33, 95% CI 0.21-0.51, P < 0.001), full-time motherhood (OR 0.47, 95% CI 0.33-0.678, P < 0.001) and high household income (OR 0.67, 95% CI 0.50-0.88, P = 0.004) were negatively correlated with the choice of remote FHR self-monitoring.

CONCLUSION

Remote FHR self-monitoring technology has a lower cost and shows potential clinical efficacy for the outpatient setting in southern China. This approach does not increase the risk of cesarean section or adverse neonatal outcomes. It is acceptable among nulliparous pregnant women with a high education level, high household income or high risk. Further research is needed to assess the impact of this technology on obstetric outcomes in different health settings.

Application of remote fetal heart rate monitoring via internet in late pregnancy during the COVID-19 pandemic

BACKGROUND

Internet-related technologies have rapidly developed and started to impact the traditional medical practices, which combined wireless communication technology as well as "cloud service" technology with electronic fetal heart monitoring have become a mainstream tendency.

OBJECTIVE

To investigate the clinical application value of remote fetal heart rate monitoring mode (RFHRM) on late pregnancy during the coronavirus disease (COVID-19) pandemic.

METHODS

From March 2021 to February 2022, we recruited 800 cases of pregnant women received prenatal examination at the Anhui Province Maternity and Child Healthcare Hospital. These pregnant women were randomly divided into two groups: the control group (n= 400), which was given traditional management, and the observation group (n= 400), which received remote monitoring technology on this basis. The two groups were compared with neonatal asphyxia, pregnancy outcomes, Edinburgh postnatal depression scale (EPDS), prenatal examination expenses and total time consumption.

RESULTS

There were no statistically significant differences between the groups in pregnancy outcome and neonatal outcome (P> 0.05). However, total EPDS score of 12.5% pregnant women in TPE group were higher than 12. The TPE group had significantly higher mean EPDS scores compared with the RFHRM group (7.79 ± 3.58 vs 5.10 ± 3.07; P< 0.05). The results showed a significant difference in maternity expenses (2949.83 ± 456.07 vs 2455.37 ± 506.67; P< 0.05) and total time consumption (42.81 ± 7.60 vs 20.43 ± 4.16; P< 0.05) between the groups.

CONCLUSION

Remote fetal heart rate monitoring via Internet served as an innovative, acceptable, safe and effective reduced-frequency prenatal examination model without affecting the outcome of perinatology of pregnant women with different risk factors.

Telemonitoring System Oriented towards High-Risk Pregnant Women

A high-risk pregnancy is one in which pathological problems or abnormal conditions are latent during pregnancy and childbirth, increasing dangers to the mother's or the infant's health. Based on international standards and studies, most of the harms and risks to both the mother and the infant can be detected, treated, and prevented through proper pregnancy monitoring, as well as through appropriate and timely diagnosis. In this paper, we present the analysis, design, development, and usability assessment of a telemonitoring system focused on the remote monitoring and control of pregnancy in women suffering from hypertension, diabetes, or high-risk pregnancy. Our system is composed of two mobile web applications. One of these is designed for the medical area, allowing remote monitoring of the patient's pregnancy, and the other one is directed towards the patient, who enters the alarm symptom data, hypertension data, diabetes data, and clinical analyses, allowing the detection of a risk situation on time. Furthermore, we performed a usability assessment of our system based on a laboratory study with seven doctors and seven patients to evaluate the users' satisfaction. Our telemonitoring system shows a satisfactory/favorable opinion from the users' perspectives based on the obtained results.

Accuracy, interpretability and usability study of a wireless self-guided fetal heartbeat monitor compared to cardiotocography

Fetal Cardiography is usually performed using in-hospital Cardiotocographic (CTG) devices to assess fetal wellbeing. New technologies may permit home-based, self-administered examinations. We compared the accuracy, clinical interpretability, and user experience of a patient-administered, wireless, fetal heartbeat monitor (HBM) designed for home use, to CTG. Initially, participants had paired HBM and CTG examinations performed in the clinic. Women then used the HBM unsupervised and rated the experience. Sixty-three women had paired clinic-based HBM and CTG recordings, providing 6982 fetal heart rate measures for point-to-point comparison from 126 min of continuous recording. The accuracy of the HBM was excellent, with limits of agreement (95%) for mean fetal heart rate (FHR) between 0.72 and -1.78 beats per minute. The FHR was detected on all occasions and confirmed to be different from the maternal heart rate. Both methods were equally interpretable by Obstetricians, and had similar signal loss ratios. Thirty-four (100%) women successfully detected the FHR and obtained clinically useful cardiographic data using the device at home unsupervised. They achieved the required length of recording required for non-stress test analysis. The monitor ranked in the 96-100^th percentile for usability and learnability. The HBM is as accurate as gold-standard CTG, and provides equivalent clinical information enabling use in non-stress test analyses conducted outside of hospitals. It is usable by expectant mothers with minimal training.

Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings

We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analysis or as a clinical alert system to assist the practitioner. Three models were developed and used to detect (i) FSs on the MHR channel (the FSMHR model), (ii) the MHR and FSs on the Doppler FHR sensor (the FSDop model), and (iii) FSs on the scalp ECG channel (the FSScalp model). The FSDop model was the most useful because FSs are far more frequent on the Doppler FHR channel. All three models were based on a multilayer, symmetric, GRU, and were trained on data recorded during the first and second stages of delivery. The FSMHR and FSDop models were also trained on antepartum recordings. The training dataset contained 1030 expert-annotated periods (mean duration: 36 min) from 635 recordings. In an initial evaluation of routine clinical practice, 30 fully annotated recordings for each sensor type (mean duration: 5 h for MHR and Doppler sensors, and 3 h for the scalp ECG sensor) were analyzed. The sensitivity, positive predictive value (PPV) and accuracy were respectively 62.20%, 87.1% and 99.90% for the FSMHR model, 93.1%, 95.6% and 99.68% for the FSDop model, and 44.6%, 87.2% and 99.93% for the FSScalp model. We built a second test dataset with a more solid ground truth by selecting 45 periods (lasting 20 min, on average) on which the Doppler FHR and scalp ECG signals were recorded simultaneously. Using scalp ECG data, the experts estimated the true FHR value more reliably and thus annotated the Doppler FHR channel more precisely. The models achieved a sensitivity of 53.3%, a PPV of 62.4%, and an accuracy of 97.29%. In comparison, two experts (blinded to the scalp ECG data) respectively achieved a sensitivity of 15.7%, a PPV of 74.3%, and an accuracy of 96.91% and a sensitivity of 60.7%, a PPV of 83.5% and an accuracy of 98.24%. Hence, the models performed at expert level (better than one expert and worse than the other), although a well-trained expert with good knowledge of FSs could probably do better in some cases. The models and datasets have been included in the Fetal Heart Rate Morphological Analysis open-source MATLAB toolbox and can be used freely for research purposes.

Development of a comprehensive database for research on foetal acidosis

OBJECTIVE

To develop a research database for mother-and-child clinical and laboratory data and digital foetal heart rate (FHR) recordings.

METHODS

The Base Bien Naître (BBN) database was derived from a single-centre health data warehouse. It contains exhaustive data on all parturients with a singleton pregnancy, a vaginal or caesarean delivery in labour with a cephalic presentation after at least 37 weeks of amenorrhea, and a live birth between February 1st, 2011, and December 31st, 2018. On arrival in the delivery room, the FHR was recorded digitally for at least 30 min. A cord blood sample was always taken in order to obtain arterial pH (pHa). More than 6,000 recordings were analyzed visually for the risk of foetal acidosis and classified into five groups (according to the French College of Gynaecologists and Obstetricians (CNGOF) classification) or three groups (according to the International Federation of Gynaecology and Obstetrics (FIGO) classification).

RESULTS

Of the 16,089 files in the health data warehouse, 11,026 were complete and met the BBN's inclusion criteria. The FHR digital recordings were of good quality, with low signal loss (median [interquartile range]: 7.0% [4.3;10.9]) and a median recording time of 304 min [190;438]). In 3.7% of the children, the pHa was below 7.10. We selected a subset of 6115 records with good-quality FHR recordings over 120 min and reliable cord blood gas data: 692 (11.3%) had at least a significant risk of acidosis (according to the CNGOF classification), and 1638 (26.8%) were at least suspicious (according to the FIGO classification).

CONCLUSION

The BBN database has been designed as a searchable tool with data reuse. It currently contains over 11,000 records with comprehensive data.

Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review

Cardiovascular diseases (CVD) are the leading cause of mortality globally. Despite improvement in therapies, people with CVD lack support for monitoring and managing their condition at home and out of hospital settings. Smart Home Technologies have potential to monitor health status and support people with CVD in their homes. We explored the Smart Home Technologies available for CVD monitoring and management in people with CVD and acceptance of the available technologies to end-users. We systematically searched four databases, namely Medline, Web of Science, Embase, and IEEE, from 1990 to 2020 (search date 18 March 2020). “Smart-Home” was defined as a system using integrated sensor technologies. We included studies using sensors, such as wearable and non-wearable devices, to capture vital signs relevant to CVD at home settings and to transfer the data using communication systems, including the gateway. We categorised the articles for parameters monitored, communication systems and data sharing, end-user applications, regulations, and user acceptance. The initial search yielded 2462 articles, and the elimination of duplicates resulted in 1760 articles. Of the 36 articles eligible for full-text screening, we selected five Smart Home Technology studies for CVD management with sensor devices connected to a gateway and having a web-based user interface. We observed that the participants of all the studies were people with heart failure. A total of three main categories—Smart Home Technology for CVD management, user acceptance, and the role of regulatory agencies—were developed and discussed. There is an imperative need to monitor CVD patients’ vital parameters regularly. However, limited Smart Home Technology is available to address CVD patients’ needs and monitor health risks. Our review suggests the need to develop and test Smart Home Technology for people with CVD. Our findings provide insights and guidelines into critical issues, including Smart Home Technology for CVD management, user acceptance, and regulatory agency’s role to be followed when designing, developing, and deploying Smart Home Technology for CVD.

Accuracy, Clinical Utility, and Usability of a Wireless Self-Guided Fetal Heart Rate Monitor

Evaluation of a wireless fetal heart rate monitor demonstrates high agreement with heart rate, as assessed by cardiotocography, and is feasible for use at home.

To evaluate the accuracy, clinical utility, and usability of a wireless fetal and maternal heartbeat monitor to monitor fetal heart rate (FHR).

Application mattampu as fetal growth education media during pregnancy based on android

OBJECTIVE

The objective of the study was to determine the increase in knowledge of pregnant women about fetal growth during pregnancy by using an android application that is given the name Mattampu.

METHOD

The study consists of 90 pregnant women; 30 first trimester, 30 second trimester pregnant women, and 30 third trimester pregnant women. The sampling technique uses accidental sampling. Pregnant women are given a questionnaire about fetal growth as a pretest, then are trained using an Android-based learning application about fetal growth. After that, they were asked to read and given a post-test questionnaire. McNemar statistical test is used to assess the knowledge of pregnant women.

RESULTS

All pregnant women in all trimesters given learning through the android application media significantly increased their knowledge of fetal growth.

CONCLUSION

The use of the application could increase the knowledge of pregnant women about fetal growth.

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

Primed for a pandemic: Implementation of telehealth outpatient monitoring for women with mild COVID-19

Close observation and rapid escalation of care is essential for obstetric patients with COVID-19. The pandemic forced widespread conversion of in-person to virtual care delivery and telehealth was primed to enable outpatient surveillance of infected patients. We describe the experience and lessons learned while designing and implementing a virtual telemonitoring COVID-19 clinic for obstetric patients. All patients with suspected for confirmed COVID-19 were referred and enrolled. Telehealth visits were conducted every 24 to 72 hours based on the severity of symptoms and care was escalated to in person when necessary. The outcome of the majority (96.1%) of telehealth visits was to continue outpatient management. With regard to escalation of care, 25 patients (26.6%) presented for in person evaluation and five patients (5.3%) required inpatient admission. A virtual telemonitoring clinic for obstetric patients with mild COVID-19 offers an effective surveillance strategy as it allows for close monitoring, direct connection to in person evaluation, minimization of patient and provider exposure, and scalability.

Evaluation of Outcomes Using a Tablet-Based System to Support Glycemic Management Workflow Operations: A Retrospective Observational Study

The treatment of hospitalized patients with type 2 diabetes requires glycemic management to maintain the patients' blood glucose levels within a normal range. We developed a blood glucose management system (BGM) system in 2015, which is a tablet-based workflow support system. This system enables medical staff to continually confirm the physicians' instructions by measuring the blood glucose levels while using a tablet terminal.In this study, we examined electronic medical records (EMRs) to evaluate the usage frequency of the BGM system and the time required for the glycemic management workflow in comparison to conventional PC terminals in a large hospital setting. The data includes 197,927 blood glucose level measurements that were taken in the general wards of Tottori University Hospital between January 2016 and June 2017. The usage frequency of the glycemic management workflow while using the BGM system was 145,864 times (approximately 74% of the total blood glucose measurements). The mean time until the completion of the glycemic management workflow in the case of hyperglycemia was 16 min 33 s, which is 26% shorter than using a PC terminal for treatment that involves injection or infusion (1454 times). The BGM system is proactively utilized by medical staff, thereby improving the operating efficiency. The results of this study indicate that the BGM system installed on tablet terminals can improve the efficiency in large-scale medical institutions that treat patients with diabetes.

Fetal heart rate baseline computation with a weighted median filter

BACKGROUND

Automated fetal heart rate (FHR) analysis removes inter- and intra-expert variability, and is a promising solution for reducing the occurrence of fetal acidosis and the implementation of unnecessary medical procedures. The first steps in automated FHR analysis are determination of the baseline, and detection of accelerations and decelerations (A/D). We describe a new method in which a weighted median filter baseline (WMFB) is computed and A/Ds are then detected.

METHOD

The filter weightings are based on the prior probability that the sampled FHR is in the baseline state or in an A/D state. This probability is computed by estimating the signal's stability at low frequencies and by progressively trimming the signal. Using a competition dataset of 90 previously annotated FHR recordings, we evaluated the WMFB method and 11 recently published literature methods against the ground truth of an expert consensus. The level of agreement between the WMFB method and the expert consensus was estimated by calculating several indices (primarily the morphological analysis discordance index, MADI). The agreement indices were then compared with the values for eleven other methods. We also compared the level of method-expert agreement with the level of interrater agreement.

RESULTS

For the WMFB method, the MADI indicated a disagreement of 4.02% vs. the consensus; this value is significantly lower (p<10^-13) than that calculated for the best of the 11 literature methods (7.27%, for Lu and Wei's empirical mode decomposition method). The level of inter-expert agreement (according to the MADI) and the level of WMFB-expert agreement did not differ significantly (p=0.22).

CONCLUSION

The WMFB method reproduced the expert consensus analysis better than 11 other methods. No differences in performance between the WMFB method and individual experts were observed. The method Matlab source code is available under General Public Licence at http://utsb.univ-catholille.fr/fhr-wmfb.

An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi

Patients diagnosed with diabetes mellitus must monitor their blood glucose levels in order to control the glycaemia. Consequently, they must perform a capillary test at least three times per day and, besides that, a laboratory test once or twice per month. These standard methods pose difficulty for patients since they need to prick their finger in order to determine the glucose concentration, yielding discomfort and distress. In this paper, an Internet of Things (IoT)-based framework for non-invasive blood glucose monitoring is described. The system is based on Raspberry Pi Zero (RPi) energised with a power bank, using a visible laser beam and a Raspberry Pi Camera, all implemented in a glove. Data for the non-invasive monitoring is acquired by the RPi Zero taking a set of pictures of the user fingertip and computing their histograms. Generated data is processed by an artificial neural network (ANN) implemented on a Flask microservice using the Tensorflow libraries. In this paper, all measurements were performed in vivo and the obtained data was validated against laboratory blood tests by means of the mean absolute error (10.37%) and Clarke grid error (90.32% in zone A). Estimated glucose values can be harvested by an end device such as a smartphone for monitoring purposes.