Connectivity Measures of Uterine Activity using Magnetomyography.

This work explores the use of graph-theoretical metrics of network topography to investigate interactions in the uterine activity using a multi-channel SQUID array. Magnetomyography (MMG) is a noninvasive technique that records magnetic fields associated with the uterine activity. Graph analysis was applied to 30s no-overlap epochs of MMG data for evaluating the evolution of local and global connectivity, and centrality indicators within the network. Binary graphs were obtained by applying a range of thresholds from 10% to 35% of the strongest edges preserved. Network analysis was applied to 24 simulated MMG data when independent noise realizations were added. Simulated data was generated from a multiscale forward model that uses a realistic uterus representation. Additionally, we applied network analysis to repeated real MMG measurements obtained from a subject at different gestational ages (GA) to observe the evolution of the network until subject reaches active labor. Results show in the simulation setting that network metrics were higher during the burst activity reflecting the propagation activity of the signal across the uterus of the multiscale mathematical model. The local efficiency values were higher than the global efficiency for any threshold used. For real MMG recordings, global and local efficiency, and clustering coefficient values increased as the patient approached active labor at any binarized threshold whereas betweenness centrality quantity decreased with days to active labor.

Estimating gene signals from noisy microarray images.

In oligonucleotide microarray experiments, noise is a challenging problem, as biologists now are studying their organisms not in isolation but in the context of a natural environment. In low photomultiplier tube (PMT) voltage images, weak gene signals and their interactions with the background fluorescence noise are most problematic. In addition, nonspecific sequences bind to array spots intermittently causing inaccurate measurements. Conventional techniques cannot precisely separate the foreground and the background signals. In this paper, we propose analytically based estimation technique. We assume a priori spot-shape information using a circular outer periphery with an elliptical center hole. We assume Gaussian statistics for modeling both the foreground and background signals. The mean of the foreground signal quantifies the weak gene signal corresponding to the spot, and the variance gives the measure of the undesired binding that causes fluctuation in the measurement. We propose a foreground-signal and shape-estimation algorithm using the Gibbs sampling method. We compare our developed algorithm with the existing Mann-Whitney (MW)- and expectation maximization (EM)/iterated conditional modes (ICM)-based methods. Our method outperforms the existing methods with considerably smaller mean-square error (MSE) for all signal-to-noise ratios (SNRs) in computer-generated images and gives better qualitative results in low-SNR real-data images. Our method is computationally relatively slow because of its inherent sampling operation and hence only applicable to very noisy-spot images. In a realistic example using our method, we show that the gene-signal fluctuations on the estimated foreground are better observed for the input noisy images with relatively higher undesired bindings.

Real-time access of magnetoencephalographic / -cardiographic data: technical realization & application to online fetal heart rate recording.

Current standard magnetoencephalographic and -cardiographic systems do not allow real-time access to the measured data. We developed a software solution for real-time access and used it to create an online fetal heart rate monitor.

EEG/MEG error bounds for a dynamic dipole source with a realistic head model.

This work presents the background and derivation of Cramér-Rao bounds on the errors of estimating the parameters (moment and location) of a dynamic current dipole source using data from electro- and magneto-encephalography. A realistic head model, based on knowledge of surfaces separating tissues of different conductivities, is used.

Multiframe temporal estimation of cardiac nonrigid motion.

A robust, flexible system for tracking the point to point nonrigid motion of the left ventricular (LV) endocardial wall in image sequences has been developed. This system is unique in its ability to model motion trajectories across multiple frames. The foundation of this system is an adaptive transversal filter based on the recursive least-squares algorithm. This filter facilitates the integration of models for periodicity and proximal smoothness as appropriate using a contour-based description of the object's boundaries. A set of correspondences between contours and an associated set of correspondence quality measures comprise the input to the system. Frame-to-frame relationships from two different frames of reference are derived and analyzed using synthetic and actual images. Two multiframe temporal models, both based on a sum of sinusoids, are derived. Illustrative examples of the system's output are presented for quantitative analysis. Validation of the system is performed by comparing computed trajectory estimates with the trajectories of physical markers implanted in the LV wall. Sample case studies of marker trajectory comparisons are presented. Ensemble statistics from comparisons with 15 marker trajectories are acquired and analyzed. A multiframe temporal model without spatial periodicity constraints was determined to provide excellent performance with the least computational cost. A multiframe spatiotemporal model provided the best performance based on statistical standard deviation, although at significant computational expense.

Magnetoencephalography with diversely oriented and multicomponent sensors.

To locate endocranial current sources, a magnetoencephalography (MEG) system usually measures the magnetic field at many points around the skull with an array of radial sensors. Despite the success of using radial components of the field, we show that using nonradial components may potentially also be beneficial. We demonstrate some benefits of using diversely oriented and multicomponent sensors to measure the nonradial components. A framework is provided for analyzing the accuracy of a system that estimates the location and direction of a current dipole inside a spherical skull. The framework is then used to determine the effect on accuracy of varying the orientations of sensors in an array and, as a consequence, it is found that the radial orientations commonly used in practice are suboptimal for locating dipoles near the array's center. A diversely oriented array that improves performance is presented. We show how a single multicomponent sensor can locate a dipole, and derive a simple algorithm for locating a dipole near the sensor.