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The fetal heart rate (FHR) is a marker of fetal well-being in utero (when monitoring maternal and/or fetal pathologies) and during labor. Here, we developed a smart mobile data module for the remote acquisition and transmission (via a Wi-Fi or 4G connection) of FHR recordings, together with a web-based viewer for displaying the FHR datasets on a computer, smartphone or tablet. In order to define the features required by users, we modelled the fetal monitoring procedure (in home and hospital settings) via semi-structured interviews with midwives and obstetricians. Using this information, we developed a mobile data transfer module based on a Raspberry Pi. When connected to a standalone fetal monitor, the module acquires the FHR signal and sends it (via a Wi-Fi or a 3G/4G mobile internet connection) to a secure server within our hospital information system. The archived, digitized signal data are linked to the patient's electronic medical records. An HTML5/JavaScript web viewer converts the digitized FHR data into easily readable and interpretable graphs for viewing on a computer (running Windows, Linux or MacOS) or a mobile device (running Android, iOS or Windows Phone OS). The data can be viewed in real time or offline. The application includes tools required for correct interpretation of the data (signal loss calculation, scale adjustment, and precise measurements of the signal's characteristics). We performed a proof-of-concept case study of the transmission, reception and visualization of FHR data for a pregnant woman at 30 weeks of amenorrhea. She was hospitalized in the pregnancy assessment unit and FHR data were acquired three times a day with a Philips Avalon® FM30 fetal monitor. The prototype (Raspberry Pi) was connected to the fetal monitor's RS232 port. The emission and reception of prerecorded signals were tested and the web server correctly received the signals, and the FHR recording was visualized in real time on a computer, a tablet and smartphones (running Android and iOS) via the web viewer. This process did not perturb the hospital's computer network. There was no data delay or loss during a 60-min test. The web viewer was tested successfully in the various usage situations. The system was as user-friendly as expected, and enabled rapid, secure archiving. We have developed a system for the acquisition, transmission, recording and visualization of RCF data. Healthcare professionals can view the FHR data remotely on their computer, tablet or smartphone. Integration of FHR data into a hospital information system enables optimal, secure, long-term data archiving.

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The fetal heart rate (FHR) is a screening signal for preventing fetal hypoxia during labor. When experts analyze this signal, they have to position a baseline and identify decelerations and accelerations. These steps can potentially be automated and made more objective by data processing analysis, but training and evaluation datasets are required. Here, we describe a dataset of 155 FHR recordings in which a reference baseline, accelerations and decelerations have been annotated by expert consensus. 66 FHR recordings with a shared expert analysis have been included in a training dataset, and 90 other FHR recordings with a non-shared expert analysis have been included in an evaluation dataset. Researchers wishing to evaluate their automatic analysis method should submit their results for comparison with the expert consensus. The dataset also contains the results produced by 11 re-coded automatic analysis methods from the literature. All the data are available at http://utsb.univ-catholille.fr/fhr-review.

Visual analysis of fetal heart rate (FHR) during labor is subject to inter- and intra-observer variability that is particularly troublesome for anomalous recordings. Automatic FHR analysis has been proposed as a promising way to reduce this variability. The major difficulty with automatic analysis is to determine the baseline from which accelerations and decelerations will be detected. Eleven methods for automatic FHR analysis were reprogrammed using description from the literature and applied to 66 FHR recordings collected during the first stage of delivery. The FHR baselines produced by the automatic methods were compared with the baseline defined by agreement among a panel of three experts. The better performance of the automatic methods described by Mongelli, Lu, Wrobel and Pardey was noted despite their different approaches on signal processing. Nevertheless, for several recordings, none of the automatic studied methods produced a baseline similar to that defined by the experts.