Quantification of fetal magnetoencephalographic activity in low-risk fetuses using burst duration and interburst interval

Govindan, Rathinaswamy B.; Haddad, Naim; Preißl, Hubert; Lowery, Curtis L.; Siegel, Eric R.; Eswaran, Hari

doi:10.1016/j.clinph.2013.11.018

Cited by 9 publications

(3 citation statements)

References 29 publications

(37 reference statements)

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“…Magnetoencephalography has notably been used to investigate spontaneous brain activity in fetuses of between 27 and 39 wGA [175]. The characteristic EEG features described in the premature neonates (such as delta brushes, transient bursts and "tracé alternant") were also recorded in utero [47] (Figure 22).…”

Section: Aspects Of Magnetoencephalography In Fetusesmentioning

confidence: 99%

Back to basics: the neuronal substrates and mechanisms that underlie the electroencephalogram in premature neonates

Wallois

Routier

Heberlé

et al. 2021

Neurophysiologie Clinique

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Section: Aspects Of Magnetoencephalography In Fetusesmentioning

confidence: 99%

Back to basics: the neuronal substrates and mechanisms that underlie the electroencephalogram in premature neonates

Wallois

Routier

Heberlé

et al. 2021

Neurophysiologie Clinique

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“…These scenarios present us with future areas of investigation wherein the proposed objective fBS detection approach could help clinicians to decipher the development of ANS, assess fetal wellbeing and ultimately contribute to the better management of high-risk pregnancies including growth-restricted or hypoxic fetuses. In addition, as indicated in [35], the automated detection of fBS could facilitate the detection of spontaneous brain patterns during sleep states which could serve to define a fetal neurological maturation chart for normal fetuses similar to the growth chart for newborns.…”

Section: Discussionmentioning

confidence: 99%

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

Ulusar

Eswaran

Preißl

et al. 2016

Computers in Biology and Medicine

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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.

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“…There is also evidence for relatively similar signatures of spontaneous activity in the EEG of premature newborn and the MEG of fetus at the same gestational age (although evidence is rare in fMEG). General knowledge, supported by animal models [13,14], indicates that the resting state activity of the early developing neural networks is characterized by discontinuity, built around bursts of activity separated by quiescent periods, called Inter-burst intervals (IBIs), of a few seconds [1,15,16]. The duration of the bursts and IBIs is modulated during development, and an excessively long discontinuity at a given age is an unfavorable prognostic factor.…”

Section: Introductionmentioning

confidence: 99%

Transforming spontaneous premature neonatal EEG to unpaired spontaneous fetal MEG using a CycleGan learning approach

Gallard,

Brebion,

Sippel

et al. 2024

Preprint

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large body of electroencephalography (EEG) studies has characterized the spontaneous neural activity of premature neonates at different gestational ages. However, evaluation of normal and pathological fetal brain development is still a challenge due to the complexity of the extraction and analysis of fetal neural activity. Fetal magnetoencephalography (fMEG) is currently the only available technique to record fetal neural activity with a time resolution equivalent to that of EEG. However, the signatures and characteristics of fetal spontaneous neural activity are still largely unknown. Benefiting from progress in machine learning and artificial intelligence, we aimed to transfer premature EEG to fMEG, to characterize the manifestation of spontaneous activity using the knowledge obtained from premature EEG. In this study, 30 high-resolution EEG recordings from premature newborns and 44 fMEG recordings, both from 34 to 37 weeks of gestation (wGA) were used to develop a transfer function to predict the spontaneous neural activity of the fetus. After preprocessing, bursts of spontaneous activity were detected using the non-linear energy operator over both EEG and fMEG signals. Next, we proposed a CycleGAN-based model to transform the premature EEG to fMEG and vice versa and evaluated its performance with both time and frequency measurements on both forward and inverse conversions. In the time domain, the values were similar for the mean square error (< 5%) and correlation (0.91 +- 0.05 and 0.89 +- 0.08) for the EEG to fMEG and fMEG to EEG transformations between the original data and that generated by CycleGAN. However, considering the frequency content, the CycleGAN-based model modulated the frequency content of EEG to MEG transformed signals relative to the original signals by increasing the power, on average, in all frequency bands, except for the slow delta frequency band. Our developed model showed promising potential to generate a priori signatures of fMEG manifestations related to spontaneous neural activity. Collectively, this study represents the first steps toward identifying neurobiomarkers of fetal brain development.

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Quantification of fetal magnetoencephalographic activity in low-risk fetuses using burst duration and interburst interval

Cited by 9 publications

References 29 publications

Back to basics: the neuronal substrates and mechanisms that underlie the electroencephalogram in premature neonates

Back to basics: the neuronal substrates and mechanisms that underlie the electroencephalogram in premature neonates

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

Transforming spontaneous premature neonatal EEG to unpaired spontaneous fetal MEG using a CycleGan learning approach

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