Short-term variability assessment from abdominal electrocardiogram during the antepartum period

A systematic scoping review to identify the design and assess the performance of devices for antenatal continuous fetal monitoring

BACKGROUND

Antepartum fetal monitoring aims to assess fetal development and wellbeing throughout pregnancy. Current methods utilised in clinical practice are intermittent and only provide a 'snapshot' of fetal wellbeing, thus key signs of fetal demise could be missed. Continuous fetal monitoring (CFM) offers the potential to alleviate these issues by providing an objective and longitudinal overview of fetal status. Various CFM devices exist within literature; this review planned to provide a systematic overview of these devices, and specifically aimed to map the devices' design, performance and factors which affect this, whilst determining any gaps in development.

METHODS

A systematic search was conducted using MEDLINE, EMBASE, CINAHL, EMCARE, BNI, Cochrane Library, Web of Science and Pubmed databases. Following the deletion of duplicates, the articles' titles and abstracts were screened and suitable papers underwent a full-text assessment prior to inclusion in the review by two independent assessors.

RESULTS

The literature searches generated 4,885 hits from which 43 studies were included in the review. Twenty-four different devices were identified utilising four suitable CFM technologies: fetal electrocardiography, fetal phonocardiography, accelerometry and fetal vectorcardiography. The devices adopted various designs and signal processing methods. There was no common means of device performance assessment between different devices, which limited comparison. The device performance of fetal electrocardiography was reduced between 28 to 36 weeks' gestation and during high levels of maternal movement, and increased during night-time rest. Other factors, including maternal body mass index, fetal position, recording location, uterine activity, amniotic fluid index, number of fetuses and smoking status, as well as factors which affected alternative technologies had equivocal effects and require further investigation.

CONCLUSIONS

A variety of CFM devices have been developed, however no specific approach or design appears to be advantageous due to high levels of inter-device and intra-device variability.

Foetal ECG recovery using dynamic neural networks

Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coefficient) and statistical (analysis of variance, ANOVA) measures allows us to select the best recovery model. Finally, finite impulse response (FIR) and gamma neural networks are included in the adaptive noise cancellation (ANC) scheme in order to provide highly non-linear, dynamic capabilities to the recovery model. Neural networks are benchmarked with classical adaptive methods such as the least mean squares (LMS) and the normalized LMS (NLMS) algorithms in simulated and real registers and some conclusions are drawn. For synthetic registers, the most determinant factor in the identification of the models is the foetal-maternal signal-to-noise ratio (SNR). In addition, as the electromyogram contribution becomes more relevant, neural networks clearly outperform the LMS-based algorithm. From the ANOVA test, we found statistical differences between LMS-based models and neural models when complex situations (high foetal-maternal and foetal-noise SNRs) were present. These conclusions were confirmed after doing robustness tests on synthetic registers, visual inspection of the recovered signals and calculation of the recognition rates of foetal R-peaks for real situations. Finally, the best compromise between model complexity and outcomes was provided by the FIR neural network. Both the methodology for selecting a model and the introduction of advanced neural models are the main contributions of this paper.

Noninvasive fetal ECG mode fetal heart rate monitoring by adaptive digital filtering

Beat-to-beat variability (BTBV) of the fetal heart rate (FHR) is considered an indication of the neural integrity and is an important prognostic indicator of fetal well-being. We report the initial evaluation of a recently developed abdominal fetal ECG (AFECG) mode of FHR monitoring using Adaptive Digital Filtering (ADF) to accurately obtain BTBV noninvasively. Five women in labor at term were monitored with the direct fetal scalp electrode (FSE) and simultaneously with the AFECG using ADF. A computer analysis of 3298 seconds (55 minutes) of data provided a one-to-one comparison of the R-R intervals. One analysis of the direct FSE data with a second simultaneous analysis from the same electrode, to serve as control, was compared with the noninvasive AFECG data. The study group has a standard deviation of only 1.50 bpm compared to 0.79 bpm for the control group. The AFECG method agrees with the direct FSE method within 1 bpm for 92.6% of the reported R-R intervals and within 2 bpm for 98.9% of the reported intervals. This new noninvasive AFECG technique with ADF provides a continuous record of instantaneous FHR and BTBV that may be relied upon to provide an accurate continuous clinical record. The reliability of the technique has yet to be determined over a wide range of subjects.

Computerized detection of low-level fetal signals in the maternal abdominal electrocardiogram

This paper describes a method for detecting low-level fetal ECG signals in maternal abdominal ECG recordings. Detection is based on a systematic application of the principle that the fetal ECG contains proportionately greater high-frequency components than does the maternal ECG. Adaptive subtraction of the maternal high-frequency components is used to detect the fetal R-waves. The method is found to detect the fetal ECG even in many cases where the maternal and fetal R-waves coincide or occur in close proximity to each other. Recursive time-coherent averaging is then used to significantly improve the signal-to-noise ratio of the fetal ECG to the point where the fetal P and T waves may be observed.