Smoothed-NUV Priors for Imaging.

Variations of L1 -regularization including, in particular, total variation regularization, have hugely improved computational imaging. However, sharper edges and fewer staircase artifacts can be achieved with convex-concave regularizers. We present a new class of such regularizers using normal priors with unknown variance (NUV), which include smoothed versions of the logarithm function and smoothed versions of Lp norms with p ≤ 1 .

Model-Based Separation, Detection, and Classification of Eye Movements.

Objective: We present a physiologically motivated eye movement analysis framework for model-based separation, detection, and classification (MBSDC) of eye movements. By estimating kinematic and neural controller signals for saccades, smooth pursuit, and fixational eye movements in a mechanistic model of the oculomotor system we are able to separate and analyze these eye movements independently.

Methods: We extended an established oculomotor model for horizontal eye movements by neural controller signals and by a blink artifact model.

Estimation of Neural Inputs and Detection of Saccades and Smooth Pursuit Eye Movements by Sparse Bayesian Learning.

Eye movements reveal a great wealth of information about the visual system and the brain. Therefore, eye movements can serve as diagnostic markers for various neurological disorders. For an objective analysis, it is crucial to have an automatic and robust procedure to extract relevant eye movement parameters.

Estimation of the Cardiac Field in the Esophagus Using a Multipolar Esophageal Catheter.

The rapid progress of invasive therapeutic options for cardiac arrhythmias increases the need for accurate diagnostics. The surface electrocardiogram (ECG) is still the standard of noninvasive diagnostics but lacks atrial signal resolution. By contrast, esophageal electrocardiography (EECG) yields atrial signals of high amplitude and with a high signal-to-noise ratio.

Estimation of heart rate and heart rate variability from pulse oximeter recordings using localized model fitting.

Heart rate variability is one of the key parameters for assessing the health status of a subject's cardiovascular system. This paper presents a local model fitting algorithm used for finding single heart beats in photoplethysmogram recordings. The local fit of exponentially decaying cosines of frequencies within the physiological range is used to detect the presence of a heart beat.

Resolution of superpositions in EMG signals using belief propagation: results for the known constituent problem.

The problem of resolving superpositions in electromyographic (EMG) signals is considered. The shapes of the motor unit action potentials that make up each superposition are assumed to be known a-priori (known constituent problem). Two different and novel belief propagation algorithms have been developed to solve this problem.