Is there a relationship between fetal brain function and the fetal behavioral state? A fetal MEG-study

Fetal states identification in cardiotocographic tracings through discrete emissions multivariate hidden Markov models

BACKGROUND AND OBJECTIVES

Computerized Cardiotocography (cCTG) allows to analyze the Fetal Heart Rate (FHR) objectively and thoroughly, providing valuable insights on fetal condition. A challenging but crucial task in this context is the automatic identification of fetal activity and quiet periods within the tracings. Different neural mechanisms are involved in the regulation of the fetal heart, depending on the behavioral states. Thereby, their correct identification has the potential to increase the interpretability and diagnostic capabilities of FHR quantitative analysis. Moreover, the most common pathologies in pregnancy have been associated with variations in the alternation between quiet and activity states.

METHODS

We address the problem of fetal states clustering by means of an unsupervised approach, resorting to the use of a multivariate Hidden Markov Models (HMM) with discrete emissions. A fixed length sliding window is shifted on the CTG traces and a small set of features is extracted at each slide. After an encoding procedure, these features become the emissions of a multivariate HMM in which quiet and activity are the hidden states. After an unsupervised training procedure, the model is used to automatically segment signals.

RESULTS

The achieved results indicate that our developed model exhibits a high degree of reliability in identifying quiet and activity states within FHR signals. A set of 35 CTG signals belonging to different pregnancies were independently annotated by an expert gynecologist and segmented using the proposed HMM. To avoid any bias, the physician was blinded to the results provided by the algorithm. The overall agreement between the HMM's predictions and the clinician's interpretations was 90%.

CONCLUSIONS

The proposed method reliably identified fetal behavioral states, the alternance of which is an important factor in the fetal development. One key strength of our approach lies in the ease of interpreting the obtained results. By utilizing a small set of parameters that are already used in cCTG and possess clear intrinsic meanings, our method provides a high level of explainability. Another significant advantage of our approach is its fully unsupervised learning process. The states identified by our model using the Baum-Welch algorithm are associated with the "Active" and "Quiet" states only after the clustering process, removing the reliance on expert annotations. By autonomously identifying the clusters based solely on the intrinsic characteristics of the signal, our method achieves a more objective evaluation that overcomes the limitations of subjective interpretations. Indeed, we believe it could be integrated in cCTG systems to obtain a more complete signal analysis.

Evaluation of parameters for fetal behavioural state classification

Fetal behavioural states (fBS) describe periods of fetal wakefulness and sleep and are commonly defined by features such as body and eye movements and heart rate. Automatic state detection through algorithms relies on different parameters and thresholds derived from both the heart rate variability (HRV) and the actogram, which are highly dependent on the specific datasets and are prone to artefacts. Furthermore, the development of the fetal states is dynamic over the gestational period and the evaluation usually only separated into early and late gestation (before and after 32 weeks). In the current work, fBS detection was consistent between the classification algorithm and visual inspection in 87 fetal magnetocardiographic data segments between 27 and 39 weeks of gestational age. To identify how automated fBS detection could be improved, we first identified commonly used parameters for fBS classification in both the HRV and the actogram, and investigated their distribution across the different fBS. Then, we calculated a receiver operating characteristics (ROC) curve to determine the performance of each parameter in the fBS classification. Finally, we investigated the development of parameters over gestation through linear regression. As a result, the parameters derived from the HRV have a higher classification accuracy compared to those derived from the body movement as defined by the actogram. However, the overlapping distributions of several parameters across states limit a clear separation of states based on these parameters. The changes over gestation of the HRV parameters reflect the maturation of the fetal autonomic nervous system. Given the higher classification accuracy of the HRV in comparison to the actogram, we suggest to focus further research on the HRV. Furthermore, we propose to develop probabilistic fBS classification approaches to improve classification in less prototypical datasets.

A pilot study: Auditory steady-state responses (ASSR) can be measured in human fetuses using fetal magnetoencephalography (fMEG)

Background

Auditory steady-state responses (ASSRs) are ongoing evoked brain responses to continuous auditory stimuli that play a role for auditory processing of complex sounds and speech perception. Transient auditory event-related responses (AERRs) have previously been recorded using fetal magnetoencephalography (fMEG) but involve different neurological pathways. Previous studies in children and adults demonstrated that the cortical components of the ASSR are significantly affected by state of consciousness and by maturational changes in neonates and young infants. To our knowledge, this is the first study to investigate ASSRs in human fetuses.

Methods

47 fMEG sessions were conducted with 24 healthy pregnant women in three gestational age groups (30–32 weeks, 33–35 weeks and 36–39 weeks). The stimulation consisted of amplitude-modulated (AM) tones with a duration of one second, a carrier frequency (CF) of 500 Hz and a modulation frequency (MF) of 27 Hz or 42 Hz. Both tones were presented in a random order with equal probability adding up to 80–100 repetitions per tone. The ASSR across trials was quantified by assessing phase synchrony in the cortical signals at the stimulation frequency.

Results and conclusion

Ten out of 47 recordings were excluded due to technical problems or maternal movements. Analysis of the included 37 fetal recordings revealed a statistically significant response for the phase coherence between trials for the MF of 27 Hz but not for 42 Hz. An exploratory subgroup analysis moreover suggested an advantage in detectability for fetal behavioral state 2F (active asleep) compared to 1F (quiet asleep) detected using fetal heart rate. In conclusion, this pilot study is the first description of a method to detect human ASSRs in fetuses. The findings warrant further investigations of the developing fetal brain.

A computer-aided approach to detect the fetal behavioral states using multi-sensor Magnetocardiographic recordings

We propose a novel computational approach to automatically identify the fetal heart rate patterns (fHRPs), which are reflective of sleep/awake states. By combining these patterns with presence or absence of movements, a fetal behavioral state (fBS) was determined. The expert scores were used as the gold standard and objective thresholds for the detection procedure were obtained using Receiver Operating Characteristics (ROC) analysis. To assess the performance, intraclass correlation was computed between the proposed approach and the mutually agreed expert scores. The detected fHRPs were then associated to their corresponding fBS based on the fetal movement obtained from fetal magnetocardiogaphic (fMCG) signals. This approach may aid clinicians in objectively assessing the fBS and monitoring fetal wellbeing.

The functional foetal brain: A systematic preview of methodological factors in reporting foetal visual and auditory capacity

Due to technological advancements in functional brain imaging, foetal brain responses to visual and auditory stimuli is a growing area of research despite being relatively small with much variation between research laboratories. A number of inconsistencies between studies are, nonetheless, present in the literature. This article aims to explore the potential contribution of methodological factors to variation in reports of foetal neural responses to external stimuli. Some of the variation in reports can be explained by methodological differences in aspects of study design, such as brightness and wavelength of light source. In contrast to visual foetal processing, auditory foetal processing has been more frequently investigated and findings are more consistent between different studies. This is an early preview of an emerging field with many articles reporting small sample sizes with techniques that are yet to be replicated. We suggest areas for improvement for the field as a whole, such as the standardisation of stimulus delivery and a more detailed reporting of methods and results. This will improve our understanding of foetal functional response to light and sound. We suggest that enhanced technology will allow for a more reliable description of the developmental trajectory of foetal processing of light stimuli.

Heart rate variability parameters and fetal movement complement fetal behavioral states detection via magnetography to monitor neurovegetative development

Fetal behavioral states are defined by fetal movement and heart rate variability (HRV). At 32 weeks of gestational age (GA) the distinction of four fetal behavioral states represented by combinations of quiet or active sleep or awakeness is possible. Prior to 32 weeks, only periods of fetal activity and quiesence can be distinguished. The increasing synchronization of fetal movement and HRV reflects the development of the autonomic nervous system (ANS) control. Fetal magnetocardiography (fMCG) detects fetal heart activity at high temporal resolution, enabling the calculation of HRV parameters. This study combined the criteria of fetal movement with the HRV analysis to complete the criteria for fetal state detection. HRV parameters were calculated including the standard deviation of the normal-to-normal R–R interval (SDNN), the mean square of successive differences of the R–R intervals (RMSSD, SDNN/RMSSD ratio, and permutation entropy (PE) to gain information about the developing influence of the ANS within each fetal state. In this study, 55 magnetocardiograms from healthy fetuses of 24–41 weeks’ GA were recorded for up to 45 min using a fetal biomagnetometer. Fetal states were classified based on HRV and movement detection. HRV parameters were calculated for each state. Before GA 32 weeks, 58.4% quiescence and 41.6% activity cycles were observed. Later, 24% quiet sleep state (1F), 65.4% active sleep state (2F), and 10.6% active awake state (4F) were observed. SDNN increased over gestation. Changes of HRV parameters between the fetal behavioral states, especially between 1F and 4F, were statistically significant. Increasing fetal activity was confirmed by a decrease in PE complexity measures. The fHRV parameters support the differentiation between states and indicate the development of autonomous nervous control of heart rate function.