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.

Physiowise: A Physics-aware Approach to Dicrotic Notch Identification.

Dicrotic Notch (DN), one of the most significant and indicative features of the arterial blood pressure (ABP) waveform, becomes less pronounced and thus harder to identify as a matter of aging and pathological vascular stiffness. Generalizable and automatic DN identification for such edge cases is even more challenging in the presence of unexpected ABP waveform deformations that happen due to internal and external noise sources or pathological conditions that cause hemodynamic instability. We propose a physics-aware approach, named Physiowise (PW), that first employs a cardiovascular model to augment the original ABP waveform and reduce unexpected deformations, then apply a set of predefined rules on the augmented signal to find DN locations. We have tested the proposed method on in-vivo data gathered from 14 pigs under hemorrhage and sepsis study. Our result indicates 52% overall mean error improvement with 16% higher detection accuracy within the lowest permitted error range of 30ms. An additional hybrid methodology is also proposed to allow combining augmentation with any application-specific user-defined rule set.

BASS: Safe Deep Tissue Optical Sensing for Wearable Embedded Systems.

In wearable optical sensing applications whose target tissue is not superficial, such as deep tissue oximetry, the task of embedded system design has to strike a balance between two competing factors. On one hand, the sensing task is assisted by increasing the radiated energy into the body, which in turn, improves the signal-to-noise ratio (SNR) of the deep tissue at the sensor. On the other hand, patient safety consideration imposes a constraint on the amount of radiated energy into the body. In this paper, we study the trade-offs between the two factors by exploring the design space of the light source activation pulse. Furthermore, we propose BASS, an algorithm that leverages the activation pulse design space exploration, which further optimizes deep tissue SNR via spectral averaging, while ensuring the radiated energy into the body meets a safe upper bound. The effectiveness of the proposed technique is demonstrated via analytical derivations, simulations, and in vivo measurements in both pregnant sheep models and human subjects.

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.

Dicrotic Notch Identification: a Generalizable Hybrid Approach under Arterial Blood Pressure (ABP) Curve Deformations.

Dicrotic Notch (DN) is a distinctive and clinically significant feature of the arterial blood pressure curve. Its automatic identification has been the focus of many kinds of research using either model-based or rule-based methodologies. However, since DN morphology is quite variant following the patient-specific underlying physiological and pathological conditions, its automatic identification with these methods is challenging. This work proposes a hybrid approach that employs both model-based and rule-based approaches to enhance DN detection's generalizability. We have tested our approach on ABP data gathered from 14 pigs. Our result strongly indicates 36% overall mean error improvement with maximum 52% and -11% accuracy enhancement and degradation in extreme cases.

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.

Design and In Vivo Evaluation of a Non-Invasive Transabdominal Fetal Pulse Oximeter.

OBJECTIVE: Current intrapartum fetal monitoring technology is unable to provide physicians with an objective metric of fetal well-being, leading to degraded patient outcomes and increased litigation costs. Fetal oxygen saturation (SpO2) is a more suitable measure of fetal distress, but the inaccessibility of the fetus prior to birth makes this impossible to capture through current means. In this paper, we present a fully non-invasive, transabdominal fetal oximetry (TFO) system that provides in utero measures of fetal SpO2. METHODS: TFO is performed by placing a reflectance-mode optode on the maternal abdomen and sending photons into the body to investigate the underlying fetal tissue. The proposed TFO system design consists of a multi-detector optode, an embedded optode control system, and custom user-interface software. To evaluate the developed TFO system, we utilized an in utero hypoxic fetal lamb model and performed controlled desaturation experiments while capturing gold standard arterial blood gases (SaO2). RESULTS: Various degrees of fetal hypoxia were induced with true SaO2 values ranging between 10.5% and 66%. The non-invasive TFO system was able to accurately measure these fetal SpO2 values, supported by a root mean-squared error of 6.37% and strong measures of agreement with the gold standard. CONCLUSION: The results support the efficacy of the presented TFO system to non-invasively measure a wide-range of fetal SpO2 values and identify critical levels of fetal hypoxia. SIGNIFICANCE: TFO has the potential to improve fetal outcomes by providing obstetricians with a non-invasive measure of fetal oxygen saturation prior to delivery.

Validation of a Novel Transabdominal Fetal Oximeter in a Hypoxic Fetal Lamb Model.

Current intrapartum fetal oxygen saturation (SaO2) monitoring methodologies are limited, mostly consisting of fetal heart rate monitoring which is a poor predictor of fetal hypoxia. A newly developed transabdominal fetal oximeter (TFO) may be able to determine fetal SaO2 non-invasively. This study is to validate a novel TFO in determining fetal SaO2 in a hypoxic fetal lamb model. Fetal hypoxia was induced in at-term pregnant ewe by placing an aortic occlusion balloon infrarenally and inflating it in a stepwise fashion to decrease blood flow to the uterine artery. The inflation was held at each step for 10 min, and fetal arterial blood gases (ABGs) were intermittently recorded from the fetal carotid artery. The balloon catheter was deflated when fetal SaO2 fell below 15%, and the fetus was recovered. A total of three desaturation experiments were performed. The average fetal SpO2 reported by the TFO was derived at each hypoxic level and correlated with the ABG measures. Fetal SaO2 from the ABGs ranged from 10.5 to 66%. The TFO SpO2 correlated with the ABG fetal SaO2 (r-squared = 0.856) with no significant differences (p > 0.5). The fetal SpO2 measurements from TFO were significantly different than the maternal SpO2 (p < 0.01), which suggests that the transcutaneous measurements are penetrating through the maternal abdomen sufficiently and are expressing the underlying fetal tissue physiology. The recently developed TFO system was able to non-invasively report the fetal SpO2, which showed strong correlation with ABG measures and showed no significant differences.

Optode Design Space Exploration for Clinically-robust Non-invasive Fetal Oximetry.

Non-invasive transabdominal fetal oximetry (TFO) has the potential to improve delivery outcomes by providing physicians with an objective metric of fetal well-being during labor. Fundamentally, the technology is based on sending light through the maternal abdomen to investigate deep fetal tissue, followed by detection and processing of the light that returns (via scattering) to the outside of the maternal abdomen. The placement of the photodetector in relation to the light source critically impacts TFO system performance, including its operational robustness in the face of fetal depth variation. However, anatomical differences between pregnant women cause the fetal depths to vary drastically, which further complicates the optical probe (optode) design optimization. In this paper, we present a methodology to solve this problem. We frame optode design space exploration as a multi-objective optimization problem, where hardware complexity (cost) and performance across a wider patient population (robustness) form competing objectives. We propose a model-based approach to characterize the Pareto-optimal points in the optode design space, through which a specific design is selected. Experimental evaluation via simulation and in vivo measurement on pregnant sheep support the efficacy of our approach.