Predicting impact of Deep Brain Stimulation on Non-motor symptoms of Parkinson's disease.

This is the largest study on Radiomics analysis looking into the impact of Deep Brain Stimulation on Non-Motor Symptoms (NMS) of Parkinson's disease. Preoperative brain white matter radiomics of 120 patients integrated with clinical variables were used to predict the DBS effect on NMS after 1 year from the surgery. Patients were classified "suboptimal" vs "good" based on a 10% or more improvement in NMS score.

Augmented Reality Driven Steady-State Visual Evoked Potentials for Wheelchair Navigation.

Medically oriented Brain Computer Interfaces (BCIs) have been proposed as a promising approach addressed to individuals suffering from severe paralysis. Steady-State Visual Evoked Potentials (SSVEPs) in particular have been proven successful in many different applications, achieving high information throughput with short or even no training. However, efficient electric wheelchair navigation combining high accuracy and comfort is still not demonstrated.

Cuff-less blood pressure estimation using wrist photoplethysmography.

One of the most promising and at the same time rapidly growing sectors in healthcare is that of wearable medical devices. Population ageing constantly shifts towards a higher number of senior and elderly people with increased prevalence of chronic diseases often requiring long-term care and a need to decrease hospitalization time and cost. However, today most of the devices entering the market are not standardized nor medically approved, and they are highly inaccurate.

SSVEP based Wheelchair Navigation in Outdoor Environments.

A promising application of Brain Computer Interfaces (BCIs), and in particular of Steady-State Visually Evoked Potentials (SSVEP) is wheelchair navigation which can facilitate the daily life of patients suffering from severe paralysis. However, the outdoor performance of such a system is highly affected by uncontrolled environmental factors. In this paper, we present an SSVEP-based wheelchair navigation system and propose incremental learning as a method of adapting the system to changing environmental conditions.

Machine Learning Techniques for Personalized Detection of Epileptic Events in Clinical Video Recordings.

Continuous patient monitoring is essential to achieve an effective and optimal patient treatment in the intensive care unit. In the specific case of epilepsy it is the only way to achieve a correct diagnosis and a subsequent optimal medication plan if possible. In addition to automatic vital sign monitoring, epilepsy patients need manual monitoring by trained personnel, a task that is very difficult to be performed continuously for each patient.