Lionel Tarassenko

TL;DR: In this paper, a multi-centre, retrospective, observational cohort study was carried out in five hospitals from two UK NHS Trusts to evaluate the performance of NEWS-FiO2 against NEWS when predicting in-hospital death and unplanned intensive care unit (ICU) admission.

TL;DR: The results demonstrated the benefits of estimating heart rate combined from multiple regions of interest using data fusion and the convolutional neural network can be used to detect the presence of a patient and segment the patient’s skin area for vital-sign estimation, thus enabling the automatic continuous monitoring of vital signs in a hospital environment.

TL;DR: In this article, a wearable chest patch (VitalPatch®, VitalConnect, United States of America, USA) and finger-worn pulse oximeter (WristOx2® 3150, Nonin, USA), were used to estimate and transmit continuous Heart Rate (HR), Respiratory Rate (RR), and peripheral blood Oxygen Saturation (SpO2) data from ambulatory patients on these isolation wards to nurse bays remote from these patients, with a view to minimising the risk of infection for nursing staff.

TL;DR: Visensia as mentioned in this paper is a real-time continuous vital sign acquisition system, using data fusion in order to predict patient deterioration, which can prevent adverse events such as cardiac arrest, unscheduled admission to ICU, or death.

TL;DR: The paper considers the application of neural network techniques to the task of speaker identification by focusing on modular extensions to the well known multilayer perceptron (MLP) and radial basis function (RBF) architectures.