Douglas S. Pfeil

Simultaneous Low-Pass Filtering and Total Variation Denoising

This paper seeks to combine linear time-invariant (LTI) filtering and sparsity-based denoising in a principled way in order to effectively filter (denoise) a wider class of signals. LTI filtering is most suitable for signals restricted to a known frequency band, while sparsity-based denoising is suitable for signals admitting a sparse representation with respect to a known transform. However, some signals cannot be accurately categorized as either band-limited or sparse. This paper addresses the problem of filtering noisy data for the particular case where the underlying signal comprises a low-frequency component and a sparse or sparse-derivative component. A convex optimization approach is presented and two algorithms derived: one based on majorization-minimization (MM), and the other based on the alternating direction method of multipliers (ADMM). It is shown that a particular choice of discrete-time filter, namely zero-phase noncausal recursive filters for finite-length data formulated in terms of banded matrices, makes the algorithms effective and computationally efficient. The efficiency stems from the use of fast algorithms for solving banded systems of linear equations. The method is illustrated using data from a physiological-measurement technique (i.e., near infrared spectroscopic time series imaging) that in many cases yields data that is well-approximated as the sum of low-frequency, sparse or sparse-derivative, and noise components.

A Programmable Laboratory Testbed in Support of Evaluation of Functional Brain Activation and Connectivity

An important determinant of the value of quantitative neuroimaging studies is the reliability of the derived information, which is a function of the data collection conditions. Near infrared spectroscopy (NIRS) and electroencelphalography are independent sensing domains that are well suited to explore principal elements of the brains response to neuroactivation, and whose integration supports development of compact, even wearable, systems suitable for use in open environments. In an effort to maximize the translatability and utility of such resources, we have established an experimental laboratory testbed that supports measures and analysis of simulated macroscopic bioelectric and hemodynamic responses of the brain. Principal elements of the testbed include 1) a programmable anthropomorphic head phantom containing a multisignal source array embedded within a matrix that approximates the background optical and bioelectric properties of the brain, 2) integrated translatable headgear that support multimodal studies, and 3) an integrated data analysis environment that supports anatomically based mapping of experiment-derived measures that are directly and not directly observable. Here, we present a description of system components and fabrication, an overview of the analysis environment, and findings from a representative study that document the ability to experimentally validate effective connectivity models based on NIRS tomography.

Do Low-Density Cerebral Oximetry Measures Accurately Detect Variability of Cerebral Perfusion during Cardiac Surgery?

Neurocognitive deficits due to inadequate cerebral perfusion are prevalent sequelae of cardiac surgery. FDA approved non-invasive cerebral oximetry devices based on low-density arrays, are unlikely to yield accurate representation of complex heterogeneous cerebral perfusion.