Segmented independent component analysis for improved separation of fetal cardiac signals from nonstationary fetal magnetocardiograms.

Fetal magnetocardiograms (fMCGs) have been successfully processed with independent component analysis (ICA) to separate the fetal cardiac signals, but ICA effectiveness can be limited by signal nonstationarities due to fetal movements. We propose an ICA-based method to improve the quality of fetal signals separated from fMCG affected by fetal movements. This technique (SegICA) includes a procedure to detect signal nonstationarities, according to which the fMCG recordings are divided in stationary segments that are then processed with ICA. The first and second statistical moments and the signal polarity reversal were used at different threshold levels to detect signal transients. SegICA effectiveness was assessed in two fMCG datasets (with and without fetal movements) by comparing the signal-to-noise ratio (SNR) of the signals extracted with ICA and with SegICA. Results showed that the SNR of fetal signals affected by fetal movements improved with SegICA, whereas the SNR gain was negligible elsewhere. The best measure to detect signal nonstationarities of physiological origin was signal polarity reversal at threshold level 0.9. The first statistical moment also provided good results at threshold level 0.6. SegICA seems a promising method to separate fetal cardiac signals of improved quality from nonstationary fMCG recordings affected by fetal movements.

Brain activation inhomogeneity highlighted by the Isotropic Anomalous Diffusion filter.

The visual appealing nature of the now popular BOLD fMRI may give the false impression of extreme simplicity, as if the the functional maps could be generated with the press of a single button. However, one can only get plausible maps after long and cautious processing, considering that time and noise come into play during acquisition. One of the most popular ways to account for noise and individual variability in fMRI is the use of a Gaussian spatial filter. Although very robust, this filter may introduce excessive blurring, given the strong dependence of results on the central voxel value. Here, we propose the use of the Isotropic Anomalous Diffusion (IAD) approach, aiming to reduce excessive homogeneity while retaining the natural variability of signal across brain space. We found differences between Gaussian and IAD filters in two parameters gathered from Independent Component maps (ICA), identified on brain areas responsible for auditory processing during rest. Analysis of data gathered from 7 control subjects shows that the IAD filter rendered more localized active areas and higher contrast-to-noise ratios, when compared to equivalent Gaussian filtered data (Student t-test, p<0.05). The results seem promising, since the anomalous filter performs satisfactorily in filtering noise with less distortion of individual localized brain responses.

Anisotropic anomalous diffusion filtering applied to relaxation time estimation in magnetic resonance imaging.

Relaxometry mapping is a quantitative modality in magnetic resonance imaging (MRI) widely used in neuroscience studies. Despite its relevance and utility, voxel measurement of relaxation time in relaxometry MRI is compromised by noise that is inherent to MRI modality and acquisition hardware. In order to enhance signal to noise ratio (SNR) and quality of relaxometry mapping we propose application of anisotropic anomalous diffusion (AAD) filter that is consistent with inhomogeneous complex media. Here we evaluated AAD filter in comparison to two usual spatial filters: Gaussian and non local means (NLM) filters applied to real and simulated T2 relaxometry image sequences. The results demonstrate that AAD filter is comparatively more efficient in noise reducing and maintaining the image structural edges. AAD shows to be a robust and reliable spatial filter for brain image relaxometry.

Open architecture software platform for biomedical signal analysis.

Biomedical signals are very important reporters of the physiological status in human body. Therefore, great attention is devoted to the study of analysis methods that help extracting the greatest amount of relevant information from these signals. There are several free of charge softwares which can process biomedical data, but they are usually closed architecture, not allowing addition of new functionalities by users. This paper presents a proposal for free open architecture software platform for biomedical signal analysis, named JBioS. Implemented in Java, the platform offers some basic functionalities to load and display signals, and allows the integration of new software components through plugins. JBioS facilitates validation of new analysis methods and provides an environment for multi-methods analysis. Plugins can be developed for preprocessing, analyzing and simulating signals. Some applications have been done using this platform, suggesting that, with these features, JBioS presents itself as a software with potential applications in both research and clinical area.