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.

本文探索运用深层潜变量模型解决与生理监测相关的高维问题，包括利用心电波形生成模型进行心脏疾病诊断、结合概率图模型和深度对抗学习的大脑信号建模方案以及生理测量和行为联合建模的框架。这些创新性计算方法的成功应用能够在生物学研究、临床诊断和新兴消费应用中实现生物标记发现的跨物种翻译，并为神经认知分析提供洞察力。