Bell's palsy is the most common condition involving a rapid and unilateral onset of peripheral paresis/paralysis of the seventh cranial nerve. It affects 11.5-53.3 per 100,000 individuals a year across different populations. Bell's palsy is a health issue causing concern and has an extremely negative effect on both patients and their families. Therefore, diagnosis and prompt cause determination are key for early treatment. However, the etiology of Bell's palsy is unclear, and this affects its treatment. Thus, it is critical to determine the causes of Bell's palsy so that targeted treatment approaches can be developed and employed. This article reviews the literature on the diagnosis of Bell's palsy and examines possible etiologies of the disorder. It also suggests that the diagnosis of idiopathic facial palsy is based on exclusion and is most often made based on five factors including anatomical structure, viral infection, ischemia, inflammation, and cold stimulation responsivity.

Network modeling of high-dimensional time series data is a key learning task due to its widespread use in a number of application areas, including macroeconomics, finance and neuroscience. While the problem of sparse modeling based on vector autoregressive models (VAR) has been investigated in depth in the literature, more complex network structures that involve low rank and group sparse components have received considerably less attention, despite their presence in data. Failure to account for low-rank structures results in spurious connectivity among the observed time series, which may lead practitioners to draw incorrect conclusions about pertinent scientific or policy questions. In order to accurately estimate a network of Granger causal interactions after accounting for latent effects, we introduce a novel approach for estimating low-rank and structured sparse highdimensional VAR models. We introduce a regularized framework involving a combination of nuclear norm and lasso (or group lasso) penalty. Further, and subsequently establish nonasymptotic upper bounds on the estimation error rates of the low-rank and the structured sparse components. We also introduce a fast estimation algorithm and finally demonstrate the performance of the proposed modeling framework over standard sparse VAR estimates through numerical experiments on synthetic and real data.