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We experimentally demonstrate nearly ideal liquid crystal (LC) polymer Bragg polarization gratings (PGs) operating at a visible wavelength of 450 nm and with a sub-wavelength period of 335 nm. Bragg PGs employ the geometric (Pancharatnam-Berry) phase, and have many properties fundamentally different than their isotropic analog. However, until now Bragg PGs with nanoscale periods (e.g., < 800 nm) have not been realized. Using photo-alignment polymers and high-birefringence LC materials, we employ multiple thin sublayers to overcome the critical thickness threshold, and use chiral dopants to induce a helical twist that effectively generates a slanted grating. These LC polymer Bragg PGs manifest 85-99% first-order efficiency, 19-29° field-of-view, Q ≈ 17, 200 nm spectral bandwidth, 84° deflection angle in air (in one case), and efficient waveguide-coupling (in another case). Compared to surface-relief and volume-holographic gratings, they show high efficiency with larger angular/spectral bandwidths and potentially simpler fabrication. These nanoscale Bragg PGs manifest a 6π rad/μm phase gradient, the largest reported for a geometric-phase hologram while maintaining a first-order efficiency near 100%.

While detection of acute cardiac disorders such as myocardial infarction (MI) from electrocardiogram (ECG) and vectorcardiogram (VCG) has been widely reported, identification of MI locations from these signals, pivotal for timely therapeutic and prognostic interventions, remains a standing issue. We present an approach for MI localization based on representing complex spatiotemporal patterns of cardiac dynamics as a random-walk network reconstructed from the evolution of VCG signals across a 3-D state space. Extensive tests with signals from the PTB database of the PhysioNet databank suggest that locations of MI can be determined accurately (sensitivity of ∼88% and specificity of ∼92%) from tracking certain consistently estimated invariants of this random-walk representation.

We present an approach to deriving a real-time, lumped parameter cardiovascular dynamics model that uses features extracted from online electrocardiogram (ECG) signal recordings to generate certain surrogate hemodynamic signals. The model represents the coupled dynamics of the heart chambers, valves, and pulmonary and systemic blood circulation loops in the form of nonlinear differential equations. The features extracted from ECG signals were used to estimate the timings and amplitudes of the atrioventricular activation input functions as well as other model parameters that capture the effect of cardiac morphological and physiological characteristics. The model was tested using hemodynamic signals from the PhysioNet MGH/MF Waveform database. The results suggest that the model can capture the salient time and frequency patterns of the measured central venous pressure, pulmonary arterial pressure, and respiratory impedance signals (R(2) > 0.65). We have developed a method based on Anderson-Darling statistic and Kullback-Leibler divergence to compare the clinical measures (i.e., systolic and diastolic pressures) estimated from model waveform-extrema with those from actual measurements. The test statistics of the model waveform-extrema were statistically indistinguishable from the measured values with beat-to-beat rejection rates of 10%. The results indicate the potential of a virtual instrument that uses the model-derived signals for clinical diagnosis in lieu of expensive instrumentation.

Background: Vectorcardiogram (VCG) signals monitor both spatial and temporal cardiac electrical activities along three orthogonal planes of the body. However, the absence of spatiotemporal resolution in conventional VCG representations is a major impediment for medical interpretation and clinical usage of VCG. This is especially so because time-domain features of 12-lead ECG, instead of both spatial and temporal characteristics of VCG, are widely used for the automatic assessment of cardiac pathological patterns., Materials and Methods: We present a novel representation approach that captures critical spatiotemporal heart dynamics by displaying the real time motion of VCG cardiac vectors in a 3D space. Such a dynamic display can also be realized with only one lead ECG signal (e.g., ambulatory ECG) through an alternative lag-reconstructed ECG representation from nonlinear dynamics principles. Furthermore, the trajectories are color coded with additional dynamical properties of space-time VCG signals, e.g., the curvature, speed, octant and phase angles to enhance the information visibility., Results: In this investigation, spatiotemporal VCG signal representation is used to characterize various spatiotemporal pathological patterns for healthy control (HC), myocardial infarction (MI), atrial fibrillation (AF) and bundle branch block (BBB). The proposed color coding scheme revealed that the spatial locations of the peak of T waves are in the Octant 6 for the majority (i.e., 74 out of 80) of healthy recordings in the PhysioNet PTB database. In contrast, the peak of T waves from 31.79% (117/368) of MI subjects are found to remain in Octant 6 and the rest (68.21%) spread over all other octants. The spatiotemporal VCG signal representation is shown to capture the same important heart characteristics as the 12-lead ECG plots and more., Conclusions: Spatiotemporal VCG signal representation is shown to facilitate the characterization of space-time cardiac pathological patterns and enhance the automatic assessment of cardiovascular diseases.

This paper describes a practical framework for using multilayer feedforward neural networks to simultaneously fit both a function and its first derivatives. This framework involves two steps. The first step is to train the network to optimize a performance index, which includes both the error in fitting the function and the error in fitting the derivatives. The second step is to prune the network by removing neurons that cause overfitting and then to retrain it. This paper describes two novel types of overfitting that are only observed when simultaneously fitting both a function and its first derivatives. A new pruning algorithm is proposed to eliminate these types of overfitting. Experimental results show that the pruning algorithm successfully eliminates the overfitting and produces the smoothest responses and the best generalization among all the training algorithms that we have tested.