Biomedical subjects

Antonio Fernando C Infantosi

Publications and source records attributed to Antonio Fernando C Infantosi.

5 recordsLinked to original sources

PubMed2006

Spectral F-test power evaluation in the EEG during intermittent photic stimulation.

Intermittent photic stimulation (IPS) is an important functional test, which can induce the photic driving in the electroencephalogram (EEG). It is capable of enhancing latent oscillations manifestations not present in the resting EEG. However, for adequate quantitative evaluation of the photic driving, these changes should be assessed on a statistical basis. With this aim, the sampling distribution of spectral F test was investigated. On this basis, confidence limits of the SFT-estimate could be obtained for different practical situations, in which the signal-to-noise ratio and the number of epochs used in the estimation may vary. The technique was applied to the EEG of 10 normal subjects during IPS, and allowed detecting responses not only at the fundamental IPS frequency but also at higher harmonics. It also permitted to assess the strength of the photic driving responses and to compare them in different derivations and in different subjects.

Adolescent↗

PubMed2006

A coherence-based technique for separating phase-locked from non-phase-locked power spectrum estimates during intermittent stimulation.

In addition to evoked responses, which are phase-locked to the stimuli, the stimulation may also change the ongoing EEG in a time-locked manner. This change has been investigated in event-related synchronization/desynchronization (ERS/ERD) studies by comparing the spectra before and during stimulation or alternatively by using the intertrial variance method (IVM). In the present work, a technique based on the coherence estimate (kappa(y)(2)(f)) between the stimulation signal and the EEG is proposed for separating the ongoing EEG activity spectrum from that of the evoked responses. Furthermore, a statistical criterion is applied to reduce spurious spectral peaks. The performance of this procedure was assessed through simulation and illustrated with EEG during photic stimulation. For simulated data (signal-to-noise-ratio of 0.995 within 10-12.5 Hz) kappa(y)(2)(f) led to a non-phase-locked spectrum estimate with an average normalized error of 12.4%, which is reduced to only 0.2% after applying the statistical criterion. The methodology proposed is asymptotically equivalent to the IVM but it does not require previous filtering the EEG data. Kappa(y)(2)(f) together with the statistical correction criterion allows investigating the entrainment within a narrow-band range, particularly in frequencies close to that of the alpha peak. Hence it is useful in ERS/ERD studies. Moreover, it can be also used for characterizing frequencies within the gamma band.

Adult↗

PubMed2005

Estimation of coherence between blood flow and spontaneous EEG activity in neonates.

Blood flow to the brain responds to changes in neuronal activity and, thus, metabolic demand. In earlier work, we observed correlation between cerebral blood flow and spontaneous electroencephalogram (EEG) activity in neonates. Using coherence, we now found that during Tracé Alternant EEG activity in quiet sleep of normal term neonates, this correlation is strongest at frequencies around 0.1 Hz, reaching statistical significance (p < 0.05) in six of the nine subjects studied (p < 0.07 in eight subjects). Due to noise, artifact, and spontaneous changes in the subjects' EEG patterns, the signals investigated included epochs of missing samples. We, therefore, developed a novel algorithm for the estimation of coherence in such data and applied a Monte Carlo (surrogate data) method for its statistical analysis. This process provides a test for the statistical significance of the maximum coherence within a selected frequency band. In addition to permitting further insight into the mechanisms of cerebral blood flow control, these algorithms are potentially of great benefit in a wide range of biomedical applications, where interrupted (gapped) recordings are often a problem.

Algorithms↗

PubMed2004

A matrix-based algorithm for estimating multiple coherence of a periodic signal and its application to the multichannel EEG during sensory stimulation.

The coherence between the stimulation signal and the electroencephalogram (EEG) has been used in the detection of evoked responses. The detector's performance, however, depends on both the signal-to-noise ratio (SNR) of the responses and the number of data segments (M) used in coherence estimation. In practical situations, when a given SNR occurs, detection can only be improved by increasing M and hence the total data length. This is particularly relevant when monitoring is the objective. In the present study, we propose a matrix-based algorithm for estimating the multiple coherence of the stimulation signal taking into account a set of N EEG channels as a way of increasing the detection rate for a fixed value of M. Monte Carlo simulations suggest that thresholds for such multivariate detector are the same as those for multiple coherence of Gaussian signals and that using more than six signals is not advisable for improving the detection rate with M = 10. The results with EEG from 12 normal subjects during photic stimulation at 10 Hz showed a maximum detection for N greater than 2 in 58% of the subjects with M = 10, and hence suggest that the proposed multivariate detector is valuable in evoked responses applications.

Adolescent↗

PubMed2002

Artificial neural networks for infant mortality modelling.

This work aims to investigate a simple to use and easy to interpret methodology for assessing the relative importance of input variables in artificial neural networks (ANNs) applied to epidemiological modelling. The independent variables were 43 variables of the social, economic, environmental and health sector of 59 Brazilian municipalities, and the outcomes were infant mortality rates from these municipalities. Two assays were developed for the ANN modelling. On the first, all 43 variables were taken as input; and on the second, input variables were chosen with the help of factor analysis (FA). The relative importance of the input variables was investigated by means of bootstrap replications of the ANN model on the second assay. Further, multiple linear regression models (LRMs) were developed with the same data set and compared to the ANN models. The FA analysis allowed the selection of eight variables for the second assay. The percent of explained variance R(2) on the ANNs was in the range 0.74-0.80, while linear models had R(2)=0.4-0.5. These findings were validated by the bootstrap replications, in which the ANN models remained with higher R(2) and lower mean square error than the LRMs. The analysis of the best (second) ANN model indicated the highest ranking of importance for the variables literacy, agricultural and livestock sector jobs, number of commercial establishments and telephones. The approach presented here successfully integrated a data-oriented model with expert knowledge, indicating the potentiality of ANN modelling in the prediction, planning and assessment of public health actions.

Brazil↗