Franco Chiarugi

TL;DR: Specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety.

TL;DR: The method has shown good results and seems to be suitable for clinical application, although a larger dataset would be very useful for improvement and validation of the algorithms and the development of an earlier predictor of paroxysmal AF spontaneous termination time.

TL;DR: It is concluded that researchers and medical practitioners should take in account that some limitations undermine the applicability of risk assessment models and that the most common predictions for long term diabetes complications are related to cardiovascular diseases and diabetic retinopathy.

TL;DR: Data-mining analyses of the DCCT/EDIC data allow the identification of accurate predictive models for diabetes-related complications and initial evidences that these models can be applied on a more recent, European population are presented.

TL;DR: The results have been very satisfying on all the annotated QRSs and, with the inclusion of an automatic criterion for ventricular flutter detection, a sensitivity =99.76% and a positive predictive value=99.81% have been obtained.