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Intrapartum care uses electronic fetal heart rate monitoring (EFHRM) for over 50 years to indirectly assess fetal oxygenation. However, this approach has been associated with an increase in cesarean delivery rates and limited improvements in neonatal hypoxic outcome. To address these shortcomings, a novel transabdominal fetal pulse oximeter (TFO) is being developed to provide an objective measurement of fetal oxygenation. Previous studies have evaluated the performance of TFO on pregnant ewe. Building on the animal model, this study aims to determine whether TFO can successfully capture human fetal heart rate (FHR) signals during non-stress testing (NST) as a proof-of-concept. Eight ongoing pregnancies meeting specific inclusion criteria (18-40 years old, singleton, and at least 36 weeks' gestation) were enrolled with consent. Each study session was 15 to 20 min long. Reference maternal heart rate (MHR) and FHR were obtained using finger pulse oximetry and cardiotocography for subsequent comparison. The overall root-mean-square error was 9.7BPM for FHR and 4.4 for MHR, while the overall mean-absolute error was 7.6BPM for FHR and 1.8 for MHR. Bland-Altman analysis displayed a mean bias ± standard deviation between TFO and reference of -3.9 ± 8.9BPM, with limits of agreement ranging from -21.4 to 13.6 BPM. Both maternal and fetal heart rate measurements obtained from TFO exhibited a p-value < 0.001, showing significant correlation with the reference. This proof-of-concept study successfully demonstrates that TFO can accurately differentiate maternal and fetal heart signals in human subjects. This achievement marks the initial step towards enabling fetal oxygen saturation measurement in humans using TFO., (© 2024. The Author(s), under exclusive licence to Society for Reproductive Investigation.)

Objective: Fetal heart rate (FHR) is critical for perinatal fetal monitoring. However, motions, contractions and other dynamics may substantially degrade the quality of acquired signals, hindering robust tracking of FHR. We aim to demonstrate how use of multiple sensors can help overcome these challenges., Methods: We develop KUBAI 1 , a novel stochastic sensor fusion algorithm, to improve FHR monitoring accuracy. To demonstrate the efficacy of our approach, we evaluate it on data collected from gold standard large pregnant animal models, using a novel non-invasive fetal pulse oximeter., Results: The accuracy of the proposed method is evaluated against invasive ground-truth measurements. We obtained below 6 beats-per-minute (BPM) root-mean-square error (RMSE) with KUBAI, on five different datasets. KUBAI's performance is also compared against a single-sensor version of the algorithm to demonstrate the robustness due to sensor fusion. KUBAI's multi-sensor estimates are found to give overall 23.5% to 84% lower RMSE than single-sensor FHR estimates. The mean ± SD of improvement in RMSE is 11.95 ±9.62 BPM across five experiments. Furthermore, KUBAI is shown to have 84% lower RMSE and  ∼ 3 times higher R 2 correlation with reference compared to another multi-sensor FHR tracking method found in literature., Conclusion: The results support the effectiveness of KUBAI, the proposed sensor fusion algorithm, to non-invasively and accurately estimate fetal heart rate with varying levels of noise in the measurements., Significance: The presented method can benefit other multi-sensor measurement setups, which may be challenged by low measurement frequency, low signal-to-noise ratio, or intermittent loss of measured signal.

Fetal electrocardiogram (fECG) or photoplethysmogram (fPPG) devices are being developed for fetal heart rate (FHR) monitoring. However, deep tissue sensing is challenged by low fetal signal-to-noise ratio (SNR). Data quality is easily degraded by motion, or interference from maternal tissues and data losses can happen due to communication faults. In this paper, we propose to combine fECG and fPPG measurements in order to increase robustness against such dynamic challenges and increase FHR estimation accuracy. To the author's knowledge the fusion of two sensory data types (fECG, fPPG) has not been investigated for FHR tracking purposes in the literature. The proposed methods are evaluated on real-world data captured from gold-standard large pregnant animal experiments. A particle filtering algorithm with sensor fusion in the measurement likelihood, called KUBAI, is used to estimate FHR. Fusion of PPG&ECG data resulted in 36.6% improvement in root-mean-square-error (RMSE) and 20.3% improvement in R 2 correlation between estimated and reference FHR values compared to single sensor-type (PPG-only or ECG-only) data. We demonstrate that using different types of sensory data improves the robustness and accuracy of FHR tracking.

Intrapartum fetal well-being assessment relies on fetal heart rate (FHR) monitoring. Studies have shown that FHR monitoring has a high false-positive rate for detecting fetal hypoxia during labor and delivery. A transabdominal fetal pulse oximeter device that measures fetal oxygen saturation non-invasively through NIR light source and photodetectors could increase the accuracy of hypoxia detection. As light travels through both maternal and fetal tissue, photodetectors on the surface of mother's abdomen capture mixed signals comprising fetal and maternal information. The fetal information should be extracted first to enable fetal oxygen saturation calculation. A multi-detector fetal signal extraction method is presented in this paper where adaptive noise cancellation is applied to four mixed signals captured by four separate photodetectors placed at varying distances from the light source. As a result of adaptive noise cancellation, we obtain four separate FHR by peak detection. Weighting, outlier rejection and averaging are applied to these four fetal heart rates and a mean FHR is reported. The method is evaluated in utero on data collected from hypoxic lamb model. Ground truth for FHR is measured through hemodynamics. The results showed that using multi-detector fetal signal extraction gave up to 18.56% lower root-mean-square FHR error, and up to 57.87% lower maximum absolute FHR error compared to single-detector fetal signal extraction.