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We have developed a novel method for noncontact measurement of breathing function. The method is based on statistical modeling of dynamic thermal data captured through an infrared imaging system. The expired air has higher temperature than the typical background of indoor environments (e.g., walls). Therefore, the particles of the expired air emit at a higher power than the background, a phenomenon which is captured as a distinct thermal signature in the infrared imagery. There is significant technical difficulty in computing this signature, however, because the phenomenon is of very low intensity and transient nature. We use an advanced statistical algorithm based on the method of moments and the Jeffrey's divergence measure to address the problem. So far, we were able to compute correctly the breathing waveforms for ten (10) subjects at distances ranging from 6-8 feet. The results were checked against concomitant ground-truth data collected with a traditional contact sensor. The technology is expected to find applications in the next generation of touchless polygraphy and in preventive health care.

The strong correlation between the CVs for tidal breathing, FEV1, and FVC, and the statistically significant ability of CV for tidal breathing to distinguish between healthy subjects and CF patients, and between the studied CF disease states suggests that the CV may be useful for measuring the extent and severity of structural lung disease.

The results indicate the potential use of EIT-derived ventilation-perfusion index maps as a non-invasive method for identifying regions of air trapping.

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Electrical impedance tomography (EIT) is an emerging non-invasive medical imaging modality. It is based on feeding electrical currents into the patient, measuring the resulting voltages at the skin, and recovering the internal conductivity distribution. The mathematical task of EIT image reconstruction is a nonlinear and ill-posed inverse problem. Therefore any EIT image reconstruction method needs to be regularized, typically resulting in blurred images. One promising application is stroke-EIT, or classification of stroke into either ischemic or hemorrhagic. Ischemic stroke involves a blood clot, preventing blood flow to a part of the brain causing a low-conductivity region. Hemorrhagic stroke means bleeding in the brain causing a high-conductivity region. In both cases the symptoms are identical, so a cost-effective and portable classification device is needed. Typical EIT images are not optimal for stroke-EIT because of blurriness. This paper explores the possibilities of machine learning in improving the classification results. Two paradigms are compared: (a) learning from the EIT data, that is Dirichlet-to-Neumann maps and (b) extracting robust features from data and learning from them. The features of choice are virtual hybrid edge detection (VHED) functions (Greenleaf et al 2018 Anal. PDE 11) that have a geometric interpretation and whose computation from EIT data does not involve calculating a full image of the conductivity. We report the measures of accuracy, sensitivity and specificity of the networks trained with EIT data and VHED functions separately. Computational evidence based on simulated noisy EIT data suggests that the regularized grey-box paradigm (b) leads to significantly better classification results than the black-box paradigm (a).

LNTRODUCTIONUltrasonie nondestructive testing oflarge grained materials is limited by the ability of the detection process to distinguish the flaw signals from the backscattered grain boundary echoes. This coherent grain noise often masks the echo from inhomogeneities and defects in the material. Absorption and scattering effects further reduce the ultrasound energy leading to poor signal-to-noise ratio in the received signal. It is not possible to reduce the grain clutter by conventional time averaging techniques due to its coherent nature. Different algorithms utilizing the principles of frequency diversity and spatial diversity have been used in the past for signal-to-noise ratio enhancement. In NDE applications where the noise is primarily due to Rayleigh scattering, it can be shown that flaw detection can be improved significantly by merely bandpass filtering the lower part of the received wideband echo spectrum. Both theoretical and experimental results are presented to support this conclusion. The filtering technique is successfully tested on materials with different grain sizes. The main advantage of this method is its relative simplicity, which eliminates the need for sophisticated and computationally intensive signal processing algorithms. Furthermore, this technique allows simple hardware implementation for real-time applications. The optimal parameters, i.e., the center frequency and bandwidth of the bandpass filter are experimentally determined.

In ultrasonic flaw detection in large grained materials, backscattered grain noise often masks the flaw signal. To enhance the flaw visibility, a frequency diverse statistical filtering technique known as split-spectrum processing has been developed. This technique splits the received wideband signal into an ensemble of narrowband signals exhibiting different signal-to-noise ratios (SNR). Using a minimization algorithm, SNR enhancement can be obtained at the output. The nonlinear properties of the frequency diverse statistic filter are characterized based on the spectral histogram, which is the statistical distribution of the spectral windows selected by the minimization algorithm. The theoretical analysis indicates that the spectral histogram is similar in nature to the Wiener filter transfer function. Therefore, the optimal filter frequency region can be determined adaptively based on the spectral histogram without prior knowledge of the signal and noise spectra.