Monthly Archives: December 2023

New Paper: “Capturing the Pulse: A State-of-the-Art Review on Camera-Based Jugular Vein Assessment” led by Ph.D. Candidate Coen Arrow in Biomedical Optics Express

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See the full paper here.

Abstract

Heart failure is associated with a rehospitalisation rate of up to 50% within six months. Elevated central venous pressure may serve as an early warning sign. While invasive procedures are used to measure central venous pressure for guiding treatment in hospital, this becomes impractical upon discharge. A non-invasive estimation technique exists, where the clinician visually inspects the pulsation of the jugular veins in the neck, but it is less reliable due to human limitations. Video and signal processing technologies may offer a high-fidelity alternative. This state-of-the-art review analyses existing literature on camera-based methods for jugular vein assessment. We summarize key design considerations and suggest avenues for future research. Our review highlights the neck as a rich imaging target beyond the jugular veins, capturing comprehensive cardiac signals, and outlines factors affecting signal quality and measurement accuracy. Addressing an often quoted limitation in the field, we also propose minimum reporting standards for future studies.

Invited Talk: Kraw Lecture Series “Bridging the Gap Between Artificial Intelligence and Natural Intelligence” by Prof. Jason Eshraghian

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See the recording here.

The Kraw Lecture Series in Silicon Valley is made possible by a generous gift from UC Santa Cruz alumnus George Kraw (Cowell ‘71, history and Russian literature) and Raphael Shannon Kraw. The lecture series features acclaimed UC Santa Cruz scientists and technologists who are grappling with some of the biggest questions of our time.

Abstract: The brain is the perfect place to look for inspiration to develop more efficient neural networks. Indeed, the inner workings of our synapses and neurons offer a glimpse at what the future of deep learning might look like. Our brains are constantly adapting, our neurons processing all that we know, mistakes we’ve made, failed predictions—all working to anticipate what will happen next with incredible speed. Our brains are also amazingly efficient. Training large-scale neural networks can cost more than $10 million in energy expense, yet the human brain does remarkably well on a power budget of 20 watts.

We can apply the computational principles that underpin the brain, and use them to engineer more efficient systems that adapt to ever changing environments. There is an interplay between neural inspired algorithms, how they can be deployed on low-power microelectronics, and how the brain provides a blueprint for this process.