Transcutaneous Discrimination of Fetal Heart Rate from Maternal Heart Rate: A Fetal Oximetry Proof-of-Concept.

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.

Robust Fetal Heart Rate Tracking through Fetal Electrocardiography (ECG) and Photoplethysmography (PPG) Fusion.

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 R2 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.

KUBAI: Sensor Fusion for Non-Invasive Fetal Heart Rate Tracking.

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 KUBAI1, 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  âˆ¼ 3 times higher R2 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.

Short: Prediction of fetal blood oxygen content in response to partial occlusion of maternal aorta.

Acute hemorrhage in pregnancy may lead to maternal and/or fetal morbidity or mortality. In emergency medicine, blockage of the aorta via an inflatable endovascular balloon, technically referred to Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA), is used to manage hemorrhage. However, the application of REBOA in pregnancy needs to strike a balance between two competing objectives of limiting maternal blood loss and ensuring fetal wellness, for which one would need to predict the impact of regulated blood pressure on fetal wellness. To address this problem, we propose an efficient machine learning-based method to predict the temporal impact of the distal Mean Arterial Blood Pressure (dMAP) controlled by the REBOA on the oxygen content in the fetal blood. Evaluation of the algorithm on data collected from in-vivo experiments from pregnant ewe animal models exhibits mean absolute error of 0.61, 1.09, 1.42, 1.70 mmHg, and coefficient of determination of 0.95, 0.86, 0.76, 0.64 for prediction of partial pressure of oxygen in fetal arterial blood, a key predictor of fetal wellness, in 2.5, 5, 7.5, 10-minute prediction horizons, respectively.

Multi-Detector Heart Rate Extraction Method for Transabdominal Fetal Pulse Oximetry.

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.

Estimation of Fetal Blood Oxygen Saturation from Transabdominally Acquired Photoplethysmogram Waveforms.

Transabdominal Fetal Pulse Oximetry (TFO) faces several challenges, including the acquisition of noisy Photoplethysmogram (PPG) signals that contain a mixture of maternal and weak fetal information and scarcity of the data points on which an estimation model can be calibrated. This paper presents a novel algorithm that addresses these problems and contributes to the estimation of fetal blood oxygen saturation from PPG signals sensed through the maternal abdomen in a non-invasive manner. Our approach is composed of two critical steps. First, we develop methods to approximate the contribution of pulsating and non-pulsating fetal tissue from the sensed mixed signal. Furthermore, we leverage prior information about the system under observation, such as the physiological plausibility of fetal SpO2 estimates, to mitigate measurement noise and infer additional data samples, enabling improvements in the inferred SpO2 estimation model. We have validated our approach in-vivo, using a pregnant sheep model with a hypoxic fetal lamb. Compared with gold standard SaO2 obtained from blood gas analysis, our fetal SpO2 estimation algorithm yields the cross-validation mean absolute error (MAE) of 6.29% and correlation factor of r=0.82.

Towards Noninvasive Accurate Detection of Intrapartum Fetal Hypoxic Distress.

Current intrapartum fetal well-being assessment is performed using electronic fetal monitoring (EFM), technically referred to as cardiotocography (CTG), which transabdominally monitors fetal heart rate (FHR) in relationship to maternal uterine contractions. Sometimes the deceleration in FHR following a uterine contraction can be sign of fetal hypoxic distress, but it may also be a normal physiological response. Multiple studies have shown that EFM has a high false positive rate for detecting fetal hypoxia. This has caused a rise in emergency Cesarean section (C-section) deliveries performed in the US over the years, while the rates of various conditions associated with anoxic brain injury at birth remain unchanged. The underlying problem is that many factors other than hypoxia can cause non-reassuring CTG traces and a more objective measure of oxygen supply to the fetal brain is not conveniently available. We are working to develop a transabdominal fetal pulse oximetry (TFO) system to non-invasively measure fetal arterial blood oxygen saturation (FSpO2) in order to enhance intrapartum fetal monitoring. This paper gives an overview of the past and ongoing work performed to develop TFO, highlights the main engineering and clinical challenges faced and presents preliminary results that demonstrate feasibility of TFO in both pregnant sheep models and human subjects.