Intracranial pressure wave morphological classification: automated analysis and clinical validation.

Background: Recently, different software has been developed to automatically analyze multiple intracranial pressure (ICP) parameters, but the suggested methods are frequently complex and have no clinical correlation. The objective of this study was to assess the clinical value of a new morphological classification of the cerebrospinal fluid pulse pressure waveform (CSFPPW), comparing it to the elastance index (EI) and CSF-outflow resistance (Rout), and to test the efficacy of an automatic ICP analysis.

Methods: An artificial neural network (ANN) was trained to classify 60 CSFPPWs in four different classes, according to their morphology, and its efficacy was compared to an expert examiner's classification.

The MOnitoring Resynchronization dEvices and CARdiac patiEnts (MORE-CARE) randomized controlled trial: phase 1 results on dynamics of early intervention with remote monitoring.

Background: Remote monitoring (RM) in patients with advanced heart failure and cardiac resynchronization therapy defibrillators (CRT-D) may reduce delays in clinical decisions by transmitting automatic alerts. However, this strategy has never been tested specifically in this patient population, with alerts for lung fluid overload, and in a European setting.

Objective: The main objective of Phase 1 (presented here) is to evaluate if RM strategy is able to reduce time from device-detected events to clinical decisions.

Efficacy of a tool combining guide-wire and stylet for the left ventricular lead positioning.

Aims: The left ventricular (LV) lead for cardiac resynchronization therapy (CRT) is usually positioned in the coronary sinus via a stylet-guided or an 'over-the-wire' approach. Recently, a new tool has been developed, the Medtronic Attain Hybrid, that combines guide-wire and stylet features. We assessed its safety and efficacy in comparison with standard tools currently used in clinical practice.

A convergent hybrid decomposition algorithm model for SVM training.

Training of support vector machines (SVMs) requires to solve a linearly constrained convex quadratic problem. In real applications, the number of training data may be very huge and the Hessian matrix cannot be stored. In order to take into account this issue, a common strategy consists in using decomposition algorithms which at each iteration operate only on a small subset of variables, usually referred to as the working set.