Towards an Accessible Personal Health Record

Patient empowerment frameworks, including personal health records (PHR), actively engage technology empowered citizens in their healthcare. Particularly today, with the current increase of chronic diseases, the high growth rate of the elderly and disabled populations and at the same time the much higher cross-border patient mobility,such systems may prove to be lifesaving, cost effective and time saving. Currently, there are many different online applications promoted as being functional, user-friendly and detailed enough to provide a complete and accurate summary of an individual’s medical history. However, it seems that most of the Web services available do not fully adhere to well known accessibility standards, such as those promoted by the W3C, thus turning them away from people with disability and elderly people, who most probably need them most. Additionally, support for mobile devices introduces additional obstacles to users with disability when trying to operate such services. This paper presents fundamental (design for all) guidelines for the successful implementation of an accessible ePHR service that can be operated by any patient including people with disabilities irrespective of the device they use to access this service

Comparative analysis of time-frequency methods estimating the time-varying microstructure of sleep EEG spindles

Proceedings of the Information Technology Applications in Biomedicine, Ioannina - Epirus, Greece, October 26-28, 2006Parameter estimation for an assumed sleep EEG spindle model (AM-FM signal) is performed by using four time-frequency analysis methods. Results from simulated as well as from real data are presented. In simulated data, the Hilbert Transform-based method has the lowest average percentage error but produces considerable signal distortion. The Complex Demodulation and the Matching Pursuit-based methods have error rates below 10%, but the Matching Pursuit-based method produces considerable signal distortion as well. The Wavelet Transform-based method has the poorest performance. In real data, all methods produce reasonable parameter values. However, the Hilbert Transform and the Matching Pursuitbased methods may not be applicable for sleep spindles shorter than about 0.8 sec. Matching Pursuit-based curve fitting is utilized as part of the parameter estimation process

Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert

A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs' monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient's condition on the most suited device within the physician's reach

Dealing with diversity in computational cancer modeling.

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology

Periodogram Connectivity of EEG Signals for the Detection of Dyslexia

Electroencephalography (EEG) signals provide an important source of information of brain activity at different areas. This information can be used to diagnose brain disorders according to different activation patterns found in controls and patients. This acquisition technology can be also used to explore the neural basis of less evident learning disabilities such as Developmental Dyslexia (DD). DD is a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling, whose prevalent is estimated between 5% and 12% of the population. In this paper we propose a method to extract discriminative features from EEG signals based on the relationship among the spectral density at each channel. This relationship is computed by means of different correlation measures, inferring connectivity-like markers that are eventually selected and classified by a linear support vector machine. The experiments performed shown AUC values up to 0.7, demonstrating the applicability of the proposed approach for objective DD diagnosis

Computational horizons in cancer (CHIC) : developing meta- and hyper-multiscale models and repositories for in Silico Oncology - a brief technical outline of the project

This paper briefly outlines the aim, the objectives, the architecture and the main building blocks of the ongoing large scale integrating transatlantic research project CHIC (http://chic-vph.eu/)

Electrophysiological assessment methodology of sensory processing dysfunction in schizophrenia and dementia of the Alzheimer type

Schizophrenia and Alzheimer’s disease impacts on various sensory processings are extensively reviewed in the present publication. This article describes aspects of a research project whose aim is to delineate the neurobiology that may underlie Social Withdrawal in Alzheimer’s disease, Schizophrenia and Major Depression. This is a European-funded IMI 2 project, identified as PRISM (Psychiatric Ratings using Intermediate Stratified Markers). This paper focuses specifically on the selected electrophysiological paradigms chosen based on a comprehensive review of all relevant literature and practical constraints. The choice of the electrophysiological biomarkers were fundamentality based their metrics and capacity to discriminate between populations. The selected electrophysiological paradigms are resting state EEG, auditory mismatch negativity, auditory and visual based oddball paradigms, facial emotion processing ERP’s and auditory steady-state response. The primary objective is to study the effect of social withdrawal on various biomarkers and endophenotypes found altered in the target populations. This has never been studied in relationship to social withdrawal, an important component of CNS diseases