Predictive and diagnosis models of stroke from hemodynamic signal monitoring.

This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 min of monitoring, to predict the exitus during the first 3 h of monitoring, and to predict the stroke recurrence in just 15 min of monitoring.

Somnolence and stuttering as the primary manifestations of a midbrain stroke.

Background: Stroke can occasionally manifest with non-lateralizing findings such as somnolence and stuttering. We describe a case and discuss the anatomical and physiological implications of this rare combination of symptoms.

Case Report: A 51-year-old woman presented with 3 days of "feeling drunk".