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.

翻译：深度隐变量模型是捕捉复杂分布的有力方法。这类模型假设数据中存在潜在但未被观测到的底层结构。本论文探索利用隐变量模型处理生理监测相关的高维问题。首先，我们提出一种新颖的深度状态空间模型，以光学采集信号作为输入生成心脏电波形。该方法可通过可穿戴设备进行简易评估，从而实现心脏疾病的临床诊断。其次，我们提出一种脑信号建模方案，结合概率图模型与深度对抗学习的优势。结构化表征可提供可解释性，并通过编码归纳偏置来降低神经振荡的数据复杂度。我们进一步在癫痫发作检测（构建为无监督学习问题）中研究所学表征的有效性。第三，我们提出一个生理指标与行为联合建模的框架。现有整合多源脑数据的方法存在局限，且不同类型生理指标间的直接分析通常不涉及行为数据。我们的方法能识别脑区对行为的独特贡献与共享贡献，并可用于发现脑区的新功能。这些创新计算方法的成功应用，将推动生物标志物发现在跨物种间的转化，为众多生物学研究、临床诊断以及新兴消费应用中的神经认知分析提供新的见解。