Non-invasive Fetal Heart Rate Research Articles

Evaluation and patient experience of wireless noninvasive fetal heart rate monitoring devices.

In clinical practice, fetal heart rate monitoring is performed intermittently using Doppler ultrasound, typically for 30 minutes. In case of a non-reassuring heart rate pattern, monitoring is usually prolonged. Noninvasive fetal electrocardiography may be more suitable for prolonged monitoring due to improved patient comfort and signal quality. This study evaluates the performance and patient experience of four noninvasive electrocardiography devices to assess candidate devices for prolonged noninvasive fetal heart rate monitoring. Non-critically sick women with a singleton pregnancy from 24 weeks of gestation were eligible for inclusion. Fetal heart rate monitoring was performed during standard care with a Doppler ultrasound device (Philips Avalon-FM30) alone or with this Doppler ultrasound device simultaneously with one of four noninvasive electrocardiography devices (Nemo Fetal Monitoring System, Philips Avalon-Beltless, Demcon Dipha-16 and Dräger Infinity-M300). Performance was evaluated by: success rate, positive percent agreement, bias, 95% limits of agreement, regression line, root mean square error and visual agreement using FIGO guidelines. Patient experience was captured using a self-made questionnaire. A total of 10 women were included per device. For fetal heart rate, Nemo performed best (success rate: 99.4%, positive percent agreement: 94.2%, root mean square error 5.1 BPM, bias: 0.5 BPM, 95% limits of agreement: -9.7 - 10.7 BPM, regression line: y = -0.1x + 11.1) and the cardiotocography tracings obtained simultaneously by Nemo and Avalon-FM30 received the same FIGO classification. Comparable results were found with the Avalon-Beltless from 36 weeks of gestation, whereas the Dipha-16 and Infinity-M300 performed significantly worse. The Avalon-Beltless, Nemo and Infinity-M300 closely matched the performance of the Avalon-FM30 for maternal heart rate, whereas the performance of the Dipha-16 deviated more. Patient experience scores were higher for the noninvasive electrocardiography devices. Both Nemo and Avalon-Beltless are suitable devices for (prolonged) noninvasive fetal heart rate monitoring, taking their intended use into account. But outside its intended use limit of 36 weeks' gestation, the Avalon-Beltless performs less well, comparable to the Dipha-16 and Infinity-M300, making them currently unsuitable for (prolonged) noninvasive fetal heart rate monitoring. Noninvasive electrocardiography devices appear to be preferred due to greater comfort and mobility.

Fetal Movement Cancellation in Abdominal Electrocardiogram Recordings Using Signal-to-Signal Translation.

This study addresses the cancellation of fetal movement in abdominal electrocardiogram (AECG) recordings through deep neural networks. For this purpose, a generative signal-to-signal translation model consisting of two coupled generators is employed to discover the relations between fetal movement-contaminated and clean AECG recordings. The model is trained on the fetal ECG synthetic database (FECGSYNDB) which provides AECG recordings from 10 pregnancies along with their ground-truth maternal and fetal ECG signals. The signals are initially segmented into 4-second segments and then fed into the network for denoising. It is demonstrated that the signal-to-signal translation method can reconstruct clean AECG signals with average mean-absolute-error (MAE), root-mean-square deviation (RMSD), and Pearson correlation coefficient (PCC) of 0.099, 0.124, and 99.12% respectively, between clean and denoised AECG signals. Furthermore, the robustness of the method to low signal-to-noise ratio (SNR) input values is shown by an RMSD range of (0.047, 0.352) for SNR values within the range of (-3, 3) dB. Clinical Relevance- The proposed framework allows for the denoising of abdominal ECG signals for non-invasive fetal heart rate monitoring. The approach is accurate due to the use of advanced neural network techniques.

Hybrid Methods Based on Empirical Mode Decomposition for Non-Invasive Fetal Heart Rate Monitoring

This study focuses on fetal electrocardiogram (fECG) processing using hybrid methods that combine two or more individual methods. Combinations of independent component analysis (ICA), wavelet transform (WT), recursive least squares (RLS), and empirical mode decomposition (EMD) were used to create the individual hybrid methods. Following four hybrid methods were compared and evaluated in this study: ICA-EMD, ICA-EMD-WT, EMD-WT, and ICA-RLS-EMD. The methods were tested on two databases, the ADFECGDB database and the PhysioNet Challenge 2013 database. Extraction evaluation is based on fetal heart rate (fHR) determination. Statistical evaluation is based on determination of correct detection (ACC), sensitivity (Se), positive predictive value (PPV), and harmonic mean between Se and PPV (F1). In this study, the best results were achieved by means of the ICA-RLS-EMD hybrid method, which achieved accuracy (ACC) > 80% at 9 out of 12 recordings when tested on the ADFECGDB database, reaching an average value of ACC > 84%, Se > 87%, PPV > 92%, and F1 > 90%. When tested on the Physionet Challenge 2013 database, ACC > 80% was achieved at 12 out of 25 recordings with an average value of ACC > 64%, Se > 69%, PPV > 79%, and F1 > 72%.

Current methods of non-invasive fetal heart rate surveillance

Current methods of non-invasive fetal heart rate surveillance

Novel Hybrid Extraction Systems for Fetal Heart Rate Variability Monitoring Based on Non-Invasive Fetal Electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.

Linear phase FIR filter to compute fetal heart rate variability

Continuous monitoring of fetal heart rate (FHR) can detect the well-being of the fetus and thus indicates non-reassuring fetal status. In- vasive fetal electrocardiography (FECG) using the fetal scalp electrode applied to the fetus scalp allows accurate detection of fetal QRS (FQRS) complexes, however with a risk of infection to the fetus. We have proposed a non-invasive fetal heart rate (NIFHR) filtering technique employing finite impulse response (FIR) filters. We applied Fast Fourier Transform (FFT) to the Physionet abdominal ECG (aECG) records and derived the fiduciary edges of the spectrum of the FECG. A FIR band pass filter (BPF) is designed which is a com- posite filter consisting of a high pass filter (HPF) followed by a low pass filter (LPF) in that order. The cut off frequencies of these com- posite filters are the fiduciary edges of the fetal electrocardiography spectrum. A FQRS detector to obtain fetal heart rate variability (FHRV) processes the FQRS signal filtered through these composite FIR filters. It is observed that channel 4 from records r01 and r08 obtained 100% results for sensitivity, positive predictive value and accuracy while, the overall accuracy was 92.21%. This design can also be extended to compute maternal heart rate.

Monitoring of fetal heart rate using sharp transition FIR filter

Abstract We propose a novel technique which uses a linear phase sharp transition finite impulse response filter with reduction in band edge ripples due to Gibb’s phenomenon. This is accomplished by a slope matching technique. This design can be used for processing a wide variety of signals irrespective of their bandwidth. This paper includes the mathematical design analysis of the band pass filter with slope matching which used fetal frequency fiduciary edges to filter the raw abdominal ECG signals. Our filter design displayed a fairly good magnitude response in all passband, stopband and transition regions with very little computation time and thus is suitable for real time processing of numerous records at a time. The magnitude responses of our finite impulse response filter design was compared to Parks-McClellan algorithm. It was observed that our algorithm showed better results in the passband and stopband at lower filter orders. The peak passband in conventional finite impulse response designs is about 18% which is reduced to around 1.5% with the use of trigonometric functions in the proposed filter model combined with slope equalization technique. The QRS detector effectively extracts the fetal R-peaks to display the fetal heart rate variability which diagnoses the mother-fetus health status. The accuracy of the noninvasive fetal heart rate measurement using our technique is found to be impressive and matches closely with the fetal scalp records. The obtained results show that the sensitivity ranges from 88.36% to 100%, the positive predictive value ranging from 92.31% to 100% and an overall average accuracy recorded at 95.19%.

Monitoring Fetal Heart Rate during Labor: A Comparison of Three Methods

The purpose of the study was to compare the accuracy of a noninvasive fetal heart rate monitor with that of ultrasound, using a fetal scalp electrode as the gold standard, in laboring women of varying body habitus, throughout labor and delivery. Laboring women requiring fetal scalp electrode were monitored simultaneously with the investigational device (noninvasive fetal ECG), ultrasound, and fetal scalp electrode. An algorithm extracted the fetal heart rate from the noninvasive fetal ECG signal. Each noninvasive device recording was compared with fetal scalp electrode with regard to reliability by positive percent agreement and accuracy by root mean squared error. Seventy-one women were included in this analysis. Positive percent agreement was 83.4 ± 15.4% for noninvasive fetal ECG and 62.4 ± 26.7% for ultrasound. The root mean squared error compared with fetal scalp electrode-derived fetal heart rate was 4.8 ± 2.0 bpm for noninvasive fetal ECG and 14.3 ± 8.2 bpm for ultrasound. The superiority of noninvasive fetal ECG was maintained for stages 1 and 2 of labor and increases in body mass index. Compared with fetal scalp electrode-derived fetal heart rate, noninvasive fetal ECG is more accurate and reliable than ultrasound for intrapartum monitoring for stages 1 and 2 of labor and is less affected by increasing maternal body mass index. This confirms the results of other workers in this field.

Continuous Glucose Monitoring in Type 1 Diabetes Pregnancy Shows that Fetal Heart Rate Correlates with Maternal Glycemia.

Much evidence has shown that pregnancies in women with preexisting diabetes are affected by an increased risk of maternal and fetal adverse outcomes, probably linked to poor glycemic control. Despite great progress in medical care, the rate of stillbirths remains much higher in diabetes patients than in the general population. Recent technological advances in the field of glucose monitoring and noninvasive fetal heart rate monitoring made it possible to observe the fetal-maternal dependencies in a continuous manner. Fourteen type 1 diabetes patients were involved into the study and fitted with a blinded continuous glucose monitoring (CGM) recorder. Fetal electrocardiogram data were recorded using the Monica AN24™ device (Monica Healthcare Ltd., Nottingham, United Kingdom), the recordings of which were matched with CGM data. Statistical analysis was performed using a generalized mixed-effect logistic regression to account for individual factors. The mean number of paired data points per patient was 254±106, representing an observation period of 21.2±8.8 h. Mean glycemia equaled 5.64±0.68 mmol/L, and mean fetal heart rate was 135±6 beats/min. Higher glycemia correlated with fetal heart rate (R=0.32; P<0.0001) and was associated with higher odds of the fetus developing small accelerations (odds ratio=1.05; 95% confidence interval, 1.00-1.10; P=0.04). Elevated maternal glycemia of mothers with diabetes is associated with accelerations of fetal heart rate.

An efficient unsupervised fetal QRS complex detection from abdominal maternal ECG

Non-invasive fetal heart rate is of great relevance in clinical practice to monitor fetal health state during pregnancy. To date, however, despite significant advances in the field of electrocardiography, the analysis of abdominal fetal ECG is considered a challenging problem for biomedical and signal processing communities. This is mainly due to the low signal-to-noise ratio of fetal ECG and difficulties in cancellation of maternal QRS complexes, motion and electromyographic artefacts. In this paper we present an efficient unsupervised algorithm for fetal QRS complex detection from abdominal multichannel signal recordings combining ICA and maternal ECG cancelling, which outperforms each single method. The signal is first pre-processed to remove impulsive artefacts, baseline wandering and power line interference. The following steps are then applied: maternal ECG extraction through independent component analysis (ICA); maternal QRS detection; maternal ECG cancelling through weighted singular value decomposition; enhancing of fetal ECG through ICA and fetal QRS detection. We participated in the Physionet/Computing in Cardiology Challenge 2013, obtaining the top official scores of the challenge (among 53 teams of participants) of event 1 and event 2 concerning fetal heart rate and fetal interbeat intervals estimation section. The developed algorithms are released as open-source on the Physionet website.

An application of reconfigurable technologies for non-invasive fetal heart rate extraction

This paper illustrates the use of a reconfigurable system for fetal electrocardiogram (FECG) estimation from mother's abdomen ECG measurements. The system is based on two different reconfigurable devices. Initially, a field-programmable analog array (FPAA) device implements the analog reconfigurable preprocessing for ECG signal acquisition. The signal processing chain continues onto a field-programmable gate array (FPGA) device, which contains all the communication and interfacing protocols along with specific digital signal processing blocks required for fundamental period extraction from FECG waveforms. The synergy between these devices provides the system the ability to change any necessary parameter during the acquisition process for enhancing the result. The use of a FPGA allows implementing different algorithms for FECG signal extraction, such as adaptive signal filtering. Preliminary works employ commercially available development platforms for test experiments, which suffice for the processing of real FECG signals from biomedical databases, as the presented results illustrate.