[Show abstract][Hide abstract] ABSTRACT: Disturbances of fetal autonomic brain development can be evaluated from fetal heart rate patterns (HRP) reflecting the activity of the autonomic nervous system. Although HRP analysis from cardiotocographic (CTG) recordings is established for fetal surveillance, temporal resolution is low. Fetal magnetocardiography (MCG), however, provides stable continuous recordings at a higher temporal resolution combined with a more precise heart rate variability (HRV) analysis. A direct comparison of CTG and MCG based HRV analysis is pending. The aims of the present study are: (i) to compare the fetal maturation age predicting value of the MCG based fetal Autonomic Brain Age Score (fABAS) approach with that of CTG based Dawes-Redman methodology; and (ii) to elaborate fABAS methodology by segmentation according to fetal behavioral states and HRP. We investigated MCG recordings from 418 normal fetuses, aged between 21 and 40 weeks of gestation. In linear regression models we obtained an age predicting value of CTG compatible short term variability (STV) of R (2) = 0.200 (coefficient of determination) in contrast to MCG/fABAS related multivariate models with R (2) = 0.648 in 30 min recordings, R (2) = 0.610 in active sleep segments of 10 min, and R (2) = 0.626 in quiet sleep segments of 10 min. Additionally segmented analysis under particular exclusion of accelerations (AC) and decelerations (DC) in quiet sleep resulted in a novel multivariate model with R (2) = 0.706. According to our results, fMCG based fABAS may provide a promising tool for the estimation of fetal autonomic brain age. Beside other traditional and novel HRV indices as possible indicators of developmental disturbances, the establishment of a fABAS score normogram may represent a specific reference. The present results are intended to contribute to further exploration and validation using independent data sets and multicenter research structures.
Frontiers in Human Neuroscience 11/2014; 8:948. DOI:10.3389/fnhum.2014.00948 · 2.99 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The maturation of fetal auditory evoked cortical responses (fAECRs) is an important aspect of developmental medicine, but their reliable identification is limited due to the technical restrictions in prenatal diagnosis. The signal-to-noise ratio of the fAECRs extracted exclusively from fetal magnetoencephalography is a known issue which limits their analysis as markers of brain development. The objective of this work was to develop a signal analysis strategy to address these problems and find appropriate processing steps. In this study, a group of 147 normal fetuses with gestations between 26 and 41 weeks underwent auditory evoked response testing. We combine different approaches that address data cleaning, fAECR determination and statistical fAECR validation to reduce the uncertainty in the detection of the auditory evoked responses. For the statistical validation of the evoked responses, we use parameters computed from bootstrap-based test statistics and the correlation between different averaging modes. Appropriate thresholds for those parameters are identified using linear regression analyses by looking at the maximum correlation coefficients. The results show that by using different validation parameters, the selected fAECRs conduct to similar regression slopes with an average of −13.6 ms/week gestational age which agree with previous studies. Our novel processing framework provides an objective way to identify and eliminate non-physiological variation in the data induced by artifacts. This approach has the potential to produce more reliable data needed in clinical studies for fetal brain maturation as well as extending the investigations to high-risk groups.
[Show abstract][Hide abstract] ABSTRACT: The increasing functional integrity of the organism during fetal maturation is connected with increasing complex internal coordination. We hypothesize that time scales of complexity and dynamics of heart rate patterns reflect the increasing inter-dependencies within the fetal organism during its prenatal development. We investigated multi-scale complexity, time irreversibility and fractal scaling from 73 fetal magnetocardiographic 30min recordings over the third trimester. We found different scale dependent complexity changes, increasing medium scale time irreversibility, and increasing long scale fractal correlations (all changes p<0.05). The results confirm the importance of time scales to be considered in fetal heart rate based developmental indices.
Computers in Biology and Medicine 05/2011; 42(3):335-41. DOI:10.1016/j.compbiomed.2011.05.003 · 1.24 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Monitoring of neural function in newborns may enable the early prediction of the long-term neurodevelopmental outcome of infants. Artefacts, like eye movements or muscular artefacts are common during long-term recording of neural activity and may lead to erroneous results. Fourier analysis, wavelet analysis and principal component analysis (PCA) are some of major approaches used to extract the artefacts from the time series. The limitation of stationarity, time resolution (wavelet) or the lack of sufficient number of sources (PCA) are some of the motivations for the use of new analysis techniques for the identification and classification of such artefacts prior to further signal processing. In this paper we have developed and studied the effectivness of an Empirical Mode Decomposition (EMD) -based method for online processing and characterisation of EEG data contaminated with different types of artefacts like ocular and muscle artefacts or power line noise. The novel approach allows the detection of different types of artefacts based on the characterization of the intrinsic oscillatory modes, adaptively extracted from the EEG signal. The performance of the model, as concerns the computational time, allows the real time processing and classification up to 4 channel EEG data acquired at 256Hz (the time needed for the decomposition of a 2048 samples data segment is in average 0.1s on a Pentium4 at 3GHz PC).
[Show abstract][Hide abstract] ABSTRACT: The analysis of auditory evoked cortical responses in the MEG of preterm neonates may result in early markers of functional
cerebral development. A major constraint to this approach is the very low signal-to-noise ratio due to the fact that fMEG
recordings are contaminated by other biological sources (mainly maternal and fetal cardiac activities). This paper presents
a study of the fetal auditory response to external stimuli using a novel algorithm to remove the maternal and fetal cardiac
activities based on Periodic Component Analysis, an improved extension of the conventional source separation techniques being
customized for MCG signal decomposition and filtering. Acoustic stimuli were delivered to 10 normal healthy fetuses at different
stages of gestation (between 29th and 34th completed gestational weeks). The MEG sensors were placed in a location over the women’s abdomen nearest to the fetal head,
as determined by ultrasound images. In 7 out of the 10 cases clear responses of the fetal auditory evoked potentials could
be detected by visual examination of the averaged time courses, characterized by a clear prominent component having latencies
in the range 150–300 ms. A significance measure based on a bootstrap confidence interval has been employed in order to validate
the identified responses. The accurate identification and removal of the MCG components enabled the detection of the evoked
cortical responses in low signal-to-noise ratio conditions.
4th European Conference of the International Federation for Medical and Biological Engineering, 12/2008: pages 1390-1393;
[Show abstract][Hide abstract] ABSTRACT: Accumulating evidence suggests the existence of a shared neural substrate between imagined and executed movements. However, a better understanding of the mechanisms involved in the motor execution and motor imagery requires knowledge of the way the co-activated brain regions interact to each other during the particular (real or imagined) motor task. Within this general framework, the aim of the present study is to investigate the cortical activation and connectivity sub-serving real and imaginary rhythmic finger tapping, from the analysis of multi-channel electroencephalogram (EEG) scalp recordings. A sequence of 250 auditory pacing stimuli has been used for both the real and imagined right finger tapping task, with a constant inter-stimulus interval of 1.5 s length. During the motor execution, healthy subjects were asked to tap in synchrony with the regular sequence of stimulus events, whereas in the imagery condition subjects imagined themselves tapping in time with the auditory cue. To improve the spatial resolution of the scalp fields and suppress unwanted interferences, the EEG data have been spatially filtered. Further, event related synchronization and desynchronization phenomena and phase synchronization analysis have been employed for the study of functionally active brain areas and their connectivity during real and imagery finger tapping. Our results show a fronto-parietal co-activation during both real and imagined movements and similar connectivity patterns among contralateral brain areas. The results support the hypothesis that functional connectivity over the contralateral hemisphere during finger tapping is preserved in imagery. The approach and results can be regarded as indicative evidences of a new strategy for recognizing imagined movements in EEG-based brain computer interface research.
[Show abstract][Hide abstract] ABSTRACT: In this paper we assess the connectivity among brain areas engaged in a real and an imagined finger tapping task from Electroencephalogram (EEG) measurements using linear and non-linear measures of synchronization. In the linear approach, connectivity is inferred using the measures of Coherence and Partial Coherence. Non-linear measures of connectivity are further employed using phase dynamics. Information encoded in the phase dynamics is quantified using measures of phase synchronization. Instantaneous phases of each single trial are calculated using time-frequency methods best fitted for non-stationary signals. Local phase extraction is performed using wavelet transform and a newly developed signal analysis technique based on the Hilbert-Huang transform. The inferred patterns of connectivity highlight similarities in the structural and functional organization of cortical networks involved in actual and imagined motor movement. The consistency of results produced by the various measures of functional connectivity employed here provides further evidence that synchronization analysis can be used for the detection of movement intention in EEG-based brain computer interface applications.
[Show abstract][Hide abstract] ABSTRACT: The present work explores the spatiotemporal aspects of the event-related desynchronization (ERD) and synchronization (ERS)
during rhythmic finger tapping execution and imagery task. High resolution event related brain potentials were recorded to
capture the brain activation underlying the motor execution and motor imagery. ERS and ERD were studied using a complex morlet
wavelet decomposition of EEG responses. The results show similar patterns of beta ERD/ERS after the stimulus onset, for both
the actual and imagery finger tapping task. This approach and results can be regarded as indicative evidences of a new strategy
for recognizing imagined movements in EEG-based brain computer interface research. The long-term objective of this study is
to create a multiposition brain controlled switch that is activated by signals that are measured directly from a human’s brain.
[Show abstract][Hide abstract] ABSTRACT: Ischemic preconditioning (IP) has been used as a strategy to prevent cell death in various organs, including the brain and the heart. Investigation of the effects of ischemic preconditioning mostly employed models with reduced complexity, such as cell cultures, tissue slices or perfused organ preparations. Although such models can provide valuable insight into the protective mechanism of preconditioning, the functional (re)organization of the control mechanisms at the level of the living organism cannot be assessed. The purpose of the present animal model study was to evaluate the effect of global ischemic preconditioning on the heart rate variability (HRV) response to the asphyxia insult. The data consisted of 4 h RR interval measurements recorded in five preconditioned and five non-preconditioned Wistar rats. Using linear (time and frequency domain) and nonlinear (approximate entropy and parameters of Poincare plots) measures, we evaluated the dynamic time course of the HRV response to the asphyxia insult and the effect of preconditioning on the autonomic neurocardiac control. Both the linear and nonlinear parameters indicate a faster recovery of the baseline HRV corresponding to the preconditioned groups, though only the spectral analysis identifies a statistically significant difference between the two groups. For the preconditioned group, at about 90 min after the asphyxic insult, the autonomic neural balance (measured by LF/HF ratio) appears fully recovered. The small variation of the rest of the parameters indicates the necessity of further investigation including the design of a larger study with a higher statistical power. Our results show for the first time that global ischemic preconditioning influences the HRV response to the asphyxia injury. The neuroprotective effect of preconditioning translates into a faster recovery of the basal HRV and the autonomic modulation of the heart.
[Show abstract][Hide abstract] ABSTRACT: A non-invasive method to monitor the functioning of the autonomous nervous system consists in heart rate variability (HRV) analysis. The aim of this study was to investigate the changes on HRV after an asphyxia experiment in rats, using several linear (time and frequency domain) and nonlinear parameters (approximate entropy, SD1 and SD2 indices derived from Poincare plots).
The experiments involved the study of HRV changes after cardiac arrest (CA) resulting from 5 min of hypoxia and asphyxia, followed by manual resuscitation and return of spontaneous circulation. 5 min stationary periods of RR intervals were selected for further analysis from 5 rats in following distinct situations: 1) baseline, 2) 30 min after CA, 3) 60 min after CA, 4) 90 min after CA, 5) 120 min after CA, 6) 150 min after CA. The ANS contribution has been delineated based on time and frequency domain analysis. RESULTS and
The results indicate that the recovery process following the asphyxia cardiac arrest reflects the impaired functioning of the autonomic nervous system. Both linear and nonlinear parameters track the different phases of the experiment, with an increased sensitivity displayed by the approximate entropy (ApEn). After 150 min the ApEn RRI parameter recovers to its baseline value. The results forward the ApEn as a more sensitive parameter of the recovery process following the asphyxia.
Methods of Information in Medicine 02/2004; 43(1):118-21. DOI:10.1267/METH04010118 · 2.25 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The present work proposes an optimal symbol dynamics transformation as an approach to analyze dynamic aspects of heart rate variability (HRV) in an asphyxia experiment in rats. The approximate entropy (ApEn) was used to quantify the regularity of short symbolic sequences derived from 5 min R-R intervals (RRI). The comparison of several symbol transformations applied in HRV analysis indicates our approach as optimal for characterizing the transitions between different phases in the recovery process following asphyxia in rats.
Digital Signal Processing, 2002. DSP 2002. 2002 14th International Conference on; 02/2002
[Show abstract][Hide abstract] ABSTRACT: The human brain and nervous system comprise the most complex organ in the human body. Hundreds of billions of nerve cells, each one connected to up to ten thousand others, make possible complex functions as perception, learning, or memory. Nowadays, researchers have a very good idea of how these nerve cells communicate with each other, however, still a long way from understanding completely how do they interact in the nervous system. Diseases of the nervous system constitute an ever-greater medical problem, as they aect many millions of people of all ages, in all nations, in all walks of life. Neuroscience is a highly interdisciplinary research area, making possible the integration of methods like Genomic, Proteomic and Electrical Brain Signals, towards better understanding of normal and pathophysiological processes in the nervous system. The great challenge of modern neuroscience is to develop new personalized approaches for treatment of neurological disorders.