  A deep latent variable model is a powerful method for capturing complex
distributions. These models assume that underlying structures, but unobserved,
are present within the data. In this dissertation, we explore high-dimensional
problems related to physiological monitoring using latent variable models.
First, we present a novel deep state-space model to generate electrical
waveforms of the heart using optically obtained signals as inputs. This can
bring about clinical diagnoses of heart disease via simple assessment through
wearable devices. Second, we present a brain signal modeling scheme that
combines the strengths of probabilistic graphical models and deep adversarial
learning. The structured representations can provide interpretability and
encode inductive biases to reduce the data complexity of neural oscillations.
The efficacy of the learned representations is further studied in epilepsy
seizure detection formulated as an unsupervised learning problem. Third, we
propose a framework for the joint modeling of physiological measures and
behavior. Existing methods to combine multiple sources of brain data provided
are limited. Direct analysis of the relationship between different types of
physiological measures usually does not involve behavioral data. Our method can
identify the unique and shared contributions of brain regions to behavior and
can be used to discover new functions of brain regions. The success of these
innovative computational methods would allow the translation of biomarker
findings across species and provide insight into neurocognitive analysis in
numerous biological studies and clinical diagnoses, as well as emerging
consumer applications.
