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. Our approach then consists in performing serial comparisons of the newly recorded 3D ECG and of the six derived ECGs simulating an electrode reversal with a standard, 12-lead reference ECG by means of the CAVIAR software. We further use a scoring method to compare these analysis results and then apply a decision tree model to extract the most relevant measurements in a learning set of 121 patients recorded in ICU. RESULTS: The comparison of the seven sets of serial analysis results from the learning set resulted in the determination of a composite criteria involving four measurements of spatial orientation changes of QRS and T and providing a reversal identification accuracy of 100%. Almost the same results, with 99.99% of sensitivity and 100% of specificity, were obtained in two test sets from 90 patients, composed of 2098 and 2036 representative ECG beats respectively recorded during PTCA balloon inflation, a procedure which mimics ischemia, and before PTCA for control. CONCLUSION: Personalized automatic detection of ECG electrode cable interchanges can reach almost the maximal accuracy of 100% in self-care, and can be performed in almost real time.

Personalized Message Routing Policy in Pervasive eHealth Applications Based on HL7 RIM.

The design of relevant message routing strategies that provide reliable communications between different healthcare actors is deemed among one of the most challenging requirements to enhance QoS in eHealth applications. In this paper, we propose a data and context-driven model that aims to support the design of personalized, intelligent and adaptive eHealth message routing policies. Our model is suitably complying with HL7 v3, an international standard for healthcare information exchange. In the proposed model, we highlight the sensitivity of routing services applications to message, source and target ecosystems contextual information. We point out the relevant HL7 Reference Information Model (RIM) elements that are related to our research objective which is to deliver the message to the right healthcare provider with respect to the required delays for message reception, reading and reply, according to the content of the message and to the clinical and social situation of the patient. Moreover, we include AmI to make the routing policy intelligently adaptive to context changes at run-time.

Data privacy preservation in telemedicine: the PAIRSE project.

The preservation of medical data privacy and confidentiality is a major challenge in eHealth systems and applications. A technological solution based on advanced information and communication systems architectures is needed in order to retrieve and exchange the patient's data in a secure and reliable manner. In this paper, we introduce the project PAIRSE, Preserving Privacy in Peer to Peer (P2P) environments, which proposes an original web service oriented framework preserving the privacy and confidentiality of shared or exchanged medical data.

A classification tree approach for cardiac ischemia detection using spatiotemporal information from three standard ECG leads.

The accurate noninvasive diagnosis of cardiac ischemia remains a great challenge. To this end, the ECG is the main source of information, and personal health systems may now embed intelligence for enabling any citizen to self-record an ECG anywhere at any time. Our objective is to find a decision-support approach that makes best use of these resources. A new classification tree based on conditions combinations competition (T-3C) is proposed for building a multibranch tree of combined decision rules, and its performance is compared to usual methods based either on discriminant analysis or on classification trees. Moreover, we assessed with these methods, the diagnosis content for ischemia detection of the spatiotemporal ECG information that can be retrieved either from the standard 12-lead ECG or from only the three orthogonal leads subset (I, II, and V2), easy to set-up in self-care. The diagnostic accuracy of 14 decision-making strategies was compared for ischemia detection induced by angioplasty on a test set from a study population of 90 patients. The best performance is obtained with the T-3C algorithm on three-lead ECG, reaching 98% of sensitivity and of specificity, thus exceeding 23% of the diagnostic accuracy of the recommended and currently used standard ECG criteria.

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. We then assess the similarity between the synthesized ECGs and the original ECGs at the quantitative level in comparison with generic and patient-specific multiple-regression-based methods. The ANN achieved accurate reconstruction of the 12-lead ECGs of the study population using both generic and patient-specific ANN transforms, showing significant improvements over generic (p -value < or = 0.05) and patient-specific ( p-value < or = 0.01) multiple-linear-regression-based models. Consequently, our neural-network-based approach has proven to be sufficiently accurate to be deployed in home care as well as in ambulatory situations to synthesize a standard 12-lead ECG from a reduced lead-set ECG recording.

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 multicentric evaluation performed in Europe with a series of device prototypes and the performance assessment of the original methods of signal synthesis that were designed to guarantee a high interoperability level of the recorded data within the clinical practice, as well as of the decision-support methodologies that were developed for an early detection of life-threatening myocardial ischemia and arrhythmia, at home or anywhere, demonstrate the pertinence of going toward a personal health society in cardiology, which still yields the highest mortality rate in industrialized countries.

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. The objective of this model is to support the quality of telemedical services delivered by web-based telemedicine applications. The methodology is based on a telemedicine tasks ontology representing the concepts and their interrelations, and on a set of rules that shall be applied by a Reasoner for decision making. This architecture design shall optimize the messages exchange among the different actors in the telemedicine systems, consequently providing more rapid and reliable telemedicine assistance.

An ontology-based telemedicine tasks management system architecture.

The recent developments in ambient intelligence and ubiquitous computing offer new opportunities for the design of advanced Telemedicine systems providing high quality services, anywhere, anytime. In this paper we present an approach for building an ontology-based task-driven telemedicine system. The architecture is composed of a task management server, a communication server and a knowledge base for enabling decision makings taking account of different telemedical concepts such as actors, resources, services and the Electronic Health Record. The final objective is to provide an intelligent management of the different types of available human, material and communication resources.

A generic task-driven multi-agent telemedicine system.

Pervasive Telemedicine is an emerging research discipline, which focuses on the development and the application of ubiquitous computing technology for healthcare purposes. However, the current telemedicine systems lack to be self adaptable to handle different types of data such as vital biosignals, images, video and textual data. In addition, they do not use the full capabilities of the computing devices on which they run. Unfortunately, the existing telemedicine systems do not pay enough attention to the quality level of their offered services nor offer adequate resources management for meeting bandwidth and end-to-end communication delays. In this paper we propose an information and communication architecture of a generic telemedicine system based on a knowledge base and intelligent agents interacting each with the other in a synergetic way to perform several medical tasks for a high level of quality of service (QoS). The medical assistance to skiers and high mountains resorts residents will be used in particular as an example of applicability scenario and models personalization.

Spatiotemporal electrocardiographic characterization of ventricular depolarization and repolarization abnormalities in long QT syndrome.

BACKGROUND AND PURPOSE: If only a standard electrocardiogram (ECG) is available, at least 25% of patients with long QT syndrome (LQTS) may be missed. Our goal is to quantify abnormal electrical activity and to develop an ECG decision rule for the patients with LQTS. METHODS: One hundred forty-one subjects were included in this study (71 patients with LQTS and 70 healthy subjects). A 12-lead digital ECG was recorded for each subject and analyzed using the CAVIAR (comparative analysis of ECG-VCG and their interpretation with auto-reference to the patient) method. RESULTS: A decision tree involving criteria based on 3 spatiotemporal ECG measurements-the QT interval and the maximum amplitude of the T wave, both corrected from heart rate, and the loss of planarity of the end of QRS-identified patients with LQTS from healthy subjects with a sensitivity of 89%, a specificity of 96%, and a total accuracy of 92%. CONCLUSIONS: This study suggests that 3-dimensional ECG analysis may improve the detection of patients with LQTS.

Improvement of the detection of myocardial ischemia thanks to information technologies.

BACKGROUND: The standard 12-lead ECG remains one of the basic investigations for the early detection and assessment of acute coronary syndromes. It is easy to perform, anywhere and anytime, and can be digitally transmitted within minutes to an emergency medical service for remote advice and triage. But the conventional ST-segment deviation criteria are of limited diagnostic accuracy. The purpose of this study is to investigate how much the use of computerized ECG techniques based on the measurement of the serial spatiotemporal ECG changes could improve the detection accuracy of transmural myocardial ischemia. METHODS: We considered the serial changes of continuous 12-lead ECGs of 90 patients undergoing elective percutaneous coronary angioplasty (PTCA) recorded during balloon inflation as an experimental model of ECG changes induced by coronary artery occlusion. The spatiotemporal ECG changes were measured according to the CAVIAR method and assessed by multivariate discriminant analysis in reference to serial changes of control recordings and standard ECG criteria. RESULTS: The diagnostic accuracy of the CAVIAR criteria for ischemia detection was 97%, with sensitivity of 98% and specificity of 96%, whereas the diagnostic accuracy of the conventional ST-segment criteria was 74%, with sensitivity of 60% and specificity of 88%. The increase of overall performance was obtained for all the occlusion locations. CONCLUSIONS: Computer-assisted quantitative serial ECG analysis, taking into account the spatiotemporal changes of the QRS and T waves, would provide the physician with additional information for significantly improving the detection of transmural myocardial ischemia.

Toward personal eHealth in cardiology. Results from the EPI-MEDICS telemedicine project.

Despite many attempts to improve the management of acute myocardial infarction, only small trends to shorter time intervals before treatment have been reported. The self-care solution developed by the European EPI-MEDICS project (2001-2004) is a novel, very affordable, easy-to-use, portable, and intelligent Personal ECG Monitor (PEM) for the early detection of cardiac ischemia and arrhythmia that is able to record a professional-quality, 3-lead electrocardiogram (ECG) based on leads I, II, and V2; derive the missing leads of the standard 12-lead ECG (thanks to either a generic or a patient-specific transform), compare each ECG with a reference ECG by means of advanced neural network-based decision-making methods taking into account the serial ECG measurements and the patient risk factors and clinical data; and generate different levels of alarms and forward the alarm messages with the recorded ECGs and the patient's Personal electronic Health Record (PHR) to the relevant health care providers by means of a standard Bluetooth-enabled, GSM/GPRS-compatible mobile phone. The ECG records are SCP-ECG encoded and stored with the PHR on a secure personal SD Card embedded in the PEM device. The alarm messages and the PHR are XML encoded. Major alarm messages are automatically transmitted to the nearest emergency call center. Medium or minor alarms are sent on demand to a central PEM Alarm Web Server. Health professionals are informed by a Short Message Service. The PEM embeds itself a Web server to facilitate the reviewing and/or update of the PHR during a routine visit at the office of the general physician or cardiologist. Eighty PEM prototypes have been finalized and tested for several weeks on 697 citizens/patients in different clinical and self-care situations involving end users (188 patients), general physicians (10), and cardiologists (9). The clinical evaluation indicates that the EPI-MEDICS concept may save lives and is very valuable for prehospitalization triage.

New paradigms in telemedicine: ambient intelligence, wearable, pervasive and personalized.

After decades of development of information systems dedicated to health professionals, there is an increasing demand for personalized and non-hospital based care. An especially critical domain is cardiology: almost two third of cardiac deaths occur out of hospital, and victims do not survive long enough to benefit from in-hospital treatments. We need to reduce the time before treatment. But symptoms are often interpreted wrongly. The only immediate diagnostic tool to assess the possibility of a cardiac event is the electrocardiogram (ECG). Event and transtelephonic ECG recorders are used to improve decision making but require setting up new infrastructures. The European EPI-MEDICS project has developed an intelligent Personal ECG Monitor (PEM) for the early detection of cardiac events. The PEM embeds advanced decision making techniques, generates different alarm levels and forwards alarm messages to the relevant care providers by means of new generation wireless communication. It is cost saving, involving care provider only if necessary and requiring no specific infrastructure. This solution is a typical example of pervasive computing and ambient intelligence that demonstrates how personalized, wearable, ubiquitous devices could improve healthcare.

Pervasive self-care solutions in telecardiology. Typical use cases from the EPI-MEDICS project.

In western countries, heart disease is the main cause of premature death. Most of cardiac deaths occur out of hospital. Because of a continuously growing elderly population, the number of heart attacks is steadily increasing. Symptoms are often interpreted incorrectly. Victims do not survive long enough to benefit from inhospital treatments. To reduce the time before treatment, the only useful diagnostic tool to assess the presence of a cardiac event is the electrocardiogram (ECG). Event and transtelephonic ECG recorders are used to improve decision-making but require setting up new infrastructures. The pervasive solution proposed by the European EPI-MEDICS project is an intelligent Personal ECG Monitor for the early detection of cardiac events. It includes part of the patient electronic health record (EHR), embeds a web server and decision-making techniques, generates different alarm levels and forwards alarm messages to the relevant care providers by means of new generation wireless communication. It is cost saving, involving care providers only if necessary, without requiring to set-up specific infrastructures. Healthcare becomes personalized, wearable and ubiquitous.

Adaptive user interface customization through browsing knowledge capitalization.

Hypermedia data browsing is a mean for improving information access. However, the overload and the heterogeneity of medical information, as well as the multitude of possible navigational paths, turn the consultation of data into a difficult task. We present in this paper a solution for the development of adaptive user interfaces in a hypermedia data browsing environment. It is based on the capitalization of the users knowledge in the decision-making process, expressed in terms of navigational paths and of data presentation modes that are customized to the user's preferences and practice. This capitalization offers the user a way to automatically store and reuse the experience accumulated in browsing through patient records. We illustrate our approach with the implementation of HEMA, a clinical workstation prototype that we have specialized for the cardiology domain.