False Alarm Reduction in Self-Care by Personalized Automatic Detection of ECG Electrode Cable Interchanges.

Introduction: False alarm reduction is an important challenge in self-care, whereas one of the most important false alarm causes in the cardiology domain is electrodes misplacements in ECG recordings, the main investigations to perform for early and pervasive detection of cardiovascular diseases. In this context, we present and assess a new method for electrode reversals identification for Mason-Likar based 3D ECG recording systems which are especially convenient to use in self-care and allow to achieve, as previously reported, high computerized ischemia detection accuracy.

Methods: We mathematically simulate the effect of the six pairwise reversals of the LA, RA, LL, and C2 electrodes on the three ECG leads I, II, and V2.

Wearable sensor systems: the challenges.

Given the soaring costs associated with the treatment of ever more prevalent chronic disease, it is widely agreed that a revolution is required in health care provision. It is often thought that the necessary technology already exists for the home-based monitoring of such patients and that it is other factors which are holding back the more widespread clinical uptake of these new tools. The authors suggest that the necessary sensor-related technologies are often not as advanced as may first appear; certainly they are generally not adequate for the robust, long-term monitoring of patients under real-life conditions.

A novel neural-network model for deriving standard 12-lead ECGs from serial three-lead ECGs: application to self-care.

Synthesis of the 12-lead ECG has been investigated in the past decade as a method to improve patient monitoring in situations where the acquisition of the 12-lead ECG is cumbersome and time consuming. This paper presents and assesses a novel approach for deriving 12-lead ECGs from a pseudoorthogonal three-lead subset via generic and patient-specific nonlinear reconstruction methods based on the use of artificial neural-networks (ANNs) committees. We train and test the ANN on a set of serial ECGs from 120 cardiac inpatients from the intensive care unit of the Cardiology Hospital of Lyon.

Toward a personal health society in cardiology.

In this paper, we present a new generation of health services that has emerged due to the development of advanced information and communication technology (ICT) solutions, like the Enhanced Personal, Intelligent, and Mobile system for Early Detection and Interpretation of Cardiac Syndromes (EPI-MEDICS). It is a personal self-care system that allows any citizen to self-record high-quality ECGs on demand with a smart portable device, which is endowed with powerful ICT capabilities: self-adaptive embedded intelligence, mobile health record management support on SmartMedia card, embedded Web server, and wireless communication. The EPI-MEDICS solution design also provides ambient, intelligent, and pervasive computing services offering any citizen a ubiquitous, reliable, and efficient management of his/her own cardiac status.

A knowledge model driven solution for web-based telemedicine applications.

Building knowledge-based telemedicine systems to deliver high quality services is still a challenge. The availability and capability of different human, communication and material resources play an important role in the telemedical task management process especially in emergency scenarios. In this paper we propose a knowledge model enabling intelligent, ubiquitous telemedicine tasks management.