Wave Intensity Analysis Combined With Machine Learning can Detect Impaired Stroke Volume in Simulations of Heart Failure

Heart failure is treatable, but in the United Kingdom, the 1-, 5- and 10-year mortality rates are 24.1, 54.5 and 75.5%, respectively. The poor prognosis reflects, in part, the lack of specific, simple and affordable diagnostic techniques; the disease is often advanced by the time a diagnosis is made. Previous studies have demonstrated that certain metrics derived from pressure–velocity-based wave intensity analysis are significantly altered in the presence of impaired heart performance when averaged over groups, but to date, no study has examined the diagnostic potential of wave intensity on an individual basis, and, additionally, the pressure waveform can only be obtained accurately using invasive methods, which has inhibited clinical adoption. Here, we investigate whether a new form of wave intensity based on noninvasive measurements of arterial diameter and velocity can detect impaired heart performance in an individual. To do so, we have generated a virtual population of two-thousand elderly subjects, modelling half as healthy controls and half with an impaired stroke volume. All metrics derived from the diameter–velocity-based wave intensity waveforms in the carotid, brachial and radial arteries showed significant crossover between groups—no one metric in any artery could reliably indicate whether a subject’s stroke volume was normal or impaired. However, after applying machine learning to the metrics, we found that a support vector classifier could simultaneously achieve up to 99% recall and 95% precision. We conclude that noninvasive wave intensity analysis has significant potential to improve heart failure screening and diagnosis.

Resonances of electromagnetic-induced transparency and electromagnetic-induced absorption at the transition with level momenta J=1/2 in unidirectional wave spectroscopy

The physical processes that form the saturated absorption resonance spectra on the atomic transition with level momenta J= 1/2 in the field of unidirectional waves of arbitrary intensities are investigated both analytically and numerically. It is shown that the narrow structures of the nonlinear resonance spectra (resonances of electromagnetic-induced transparency and absorption) and the processes forming them are determined by the direction of the light wave polarizations, degree of openness of the atomic transition, and the saturating wave intensity. The conditions under which the nonlinear resonance is exclusively coherent, due to the magnetic coherence of transition levels, are revealed.

The Use of Maximum Entropy to Enhance Wave Intensity Analysis: An Application to Coronary Arteries in Hypertrophic Obstructive Cardiomyopathy

Background: Wave intensity analysis is useful for analyzing coronary hemodynamics. Much of its clinical application involves the identification of waves indicated by peaks in the wave intensity and relating their presence or absence to different cardiovascular events. However, the analysis of wave intensity peaks can be problematic because of the associated noise in the measurements. This study shows how wave intensity analysis can be enhanced by using a Maximum Entropy Method (MEM).Methods: We introduce a MEM to differentiate between “peaks” and “background” in wave intensity waveforms. We apply the method to the wave intensity waveforms measured in the left anterior descending coronary artery from 10 Hypertrophic Obstructive Cardiomyopathy (HOCM) and 11 Controls with normal cardiac function. We propose a naming convention for the significant waves and compare them across the cohorts.Results: Using a MEM enhances wave intensity analysis by identifying twice as many significant waves as previous studies. The results are robust when MEM is applied to the log transformed wave intensity data and when all of the measured data are used. Comparing waves across cohorts, we suggest that the absence of a forward expansion wave in HOCM can be taken as an indication of HOCM. Our results also indicate that the backward compression waves in HOCM are significantly larger than in Controls; unlike the forward compression waves where the wave energy in Controls is significantly higher than in HOCM. Comparing the smaller secondary waves revealed by MEM, we find some waves that are present in the majority of Controls and absent in almost all HOCM, and other waves that are present in some HOCM patients but entirely absent in Controls. This suggests some diagnostic utility in the clinical measurement of these waves, which can be a positive sign of HOCM or a subgroup with a particular pathology.Conclusion: The MEM enhances wave intensity analysis by identifying many more significant waves. The method is novel and can be applied to wave intensity analysis in all arteries. As an example, we show how it can be useful in the clinical study of hemodynamics in the coronary arteries in HOCM.

Attenuation of plasmaspheric hiss associated with the enhanced magnetospheric electric field

We report an attenuation of hiss wave intensity in the duskside of the outer plasmasphere in response to enhanced convection and a substorm based on Van Allen Probe observations. Using test particle codes, we simulate the dynamics of energetic electron fluxes based on a realistic magnetospheric electric field model driven by solar wind and subauroral polarization stream. We suggest that the enhanced magnetospheric electric field causes the outward and sunward motion of energetic electrons, corresponding to the decrease of energetic electron fluxes on the duskside, leading to the subsequent attenuation of hiss wave intensity. The results indicate that the enhanced electric field can significantly change the energetic electron distributions, which provide free energy for hiss wave amplification. This new finding is critical for understanding the generation of plasmaspheric hiss and its response to solar wind and substorm activity.