Summary of Deep Latent Variable Modeling Of Physiological Signals, by Khuong Vo
Deep Latent Variable Modeling of Physiological Signals
by Khuong Vo
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This dissertation explores novel deep latent variable models for physiological monitoring and brain signal modeling. The first contribution is a deep state-space model that generates electrical heart waveforms using optically obtained signals as inputs, enabling clinical diagnoses of heart disease via wearable devices. The second contribution combines probabilistic graphical models and deep adversarial learning to provide structured representations of neural oscillations, reducing data complexity for epilepsy seizure detection. The third contribution proposes a framework for joint modeling of physiological measures and behavior, analyzing the relationship between different brain regions and behavioral data. These innovative computational methods have the potential to translate biomarker findings across species, providing insights into neurocognitive analysis in various biological studies and clinical diagnoses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores new ways to analyze heart and brain signals using computers. It develops special models that can help doctors diagnose certain medical conditions, like heart disease, just by looking at data from wearable devices. The researchers also create a system that can detect seizures in people with epilepsy. Additionally, they develop a method to combine different types of brain signal data with behavioral data to better understand how our brains work and how we behave. These new methods have the potential to help doctors make more accurate diagnoses and could even lead to new treatments for certain medical conditions. |
