A Platform for Health Record Management of the Conscripts in the Hellenic Navy.

eMass project aims to digitalize the medical examination procedure of recruitment phase of conscripts in the Hellenic Navy. eMass integrates recruits' Electronic Health Record (EHR), while allows a pre-screening test, through portable telemedicine equipment. The data will be exploited to assess the individual's cardiovascular risk through appropriate digital tools and algorithms. The eMass digital platform, will be accessible to health experts involved in the recruitment procedure for further assessment and processing. Recruits' personal data is stored in the database encrypted using Advanced Encryption Standard (AES). eMass solution contributes to beneficial management and medical data analysis, preventing inessential physical or medical examinations minimizing danger of possible errors and reducing time-consuming processes. Moreover, eMass exploits Electronic Health Record data through a machine-learning based cardiovascular risk assessment tool.

Point-of-Care Testing Devices for Heart Failure Analyzing Blood and Saliva Samples.

Heart failure (HF) is the most rapidly growing cardiovascular condition with an estimated prevalence of >37.7 million individuals globally. HF is associated with increased mortality and morbidity and confers a substantial burden, in terms of cost and quality of life, for the individuals and the healthcare systems, highlighting thus the need for early and accurate diagnosis of HF. The accuracy of HF diagnosis, severity estimation, and prediction of adverse events has improved by the utilization of blood tests measuring biomarkers. The contribution of biomarkers for HF management is intensified by the fact that they can be measured in short time at the point-of-care. This is allowed by the development of portable analytical devices, commonly known as point-of-care testing (POCT) devices, which exploit the advancements in the area of microfluidics and nanotechnology. The aim of this review paper is to present a review of POCT devices used for the measurement of biomarkers facilitating decision making when managing HF patients. The devices are either commercially available or in the form of prototypes under development. Both blood and saliva samples are considered. The challenges concerning the implementation of POCT devices and the barriers for their adoption in clinical practice are discussed.

Assessment of arterial baroreflex sensitivity by different computational analyses of pressure wave signals alone.

BACKGROUND AND OBJECTIVE: Baroreflex sensitivity (BRS) is an important indicator of the functionality of the arterial baroreceptors, and its assessment may have major research and clinical implications. An important requirement for its quantification is the continuous recording of electrocardiography (ECG) signal, so as to extract the RR interval, in parallel with continuous beat-to-beat blood pressure recording. We aimed to accurately calculate the RR Interval from pressure wave recordings per se, namely, the Pulse Interval (PI) using various arterial pulse wave analysis algorithms and to evaluate the precision and accuracy of BRS values calculated with the PI compared to BRS values calculated with the RR Interval. METHODS: We analyzed the open access data of the Eurobavar study, which contains a set of ECG and arterial blood pressure (BP) wave signals recorded at 11 European centers. Pressure waveforms were continuously recorded by the Finapres apparatus which uses a finger cuff. The cuff pressure around the finger is dynamically adjusted by a servo-system to equal intra-arterial pressure, thus allowing the continuous recording of beat-to-beat BP waves. RR Interval was calculated from the ECG, whereas, PI was extracted from the arterial pulse waveforms, using 4 different methods (minimum, maximum, maximum 1st derivative and intersecting tangents method). BRS values were estimated by time domain and frequency domain methods. In order to compare agreement, accuracy, precision, variability, and the association between the reference BRS using the RR Interval and the BRS values using PI, standard statistical methods (i.e. intraclass correlation coefficients, RMSE, regression analysis) and Bland-Altman methods were performed. RESULTS: We found that analysis of pressure waves alone by frequency-based (i.e. spectral) methods, provides the most accurate results of BRS estimation compared to time-domain methods (ICC > 0.9, R > 0.9, RMSE > 0.8 ms/mmHg). Concerning the spectral method, any algorithm for PI calculation is sufficient, as all show excellent agreement with the respective RR-intervals determined by ECG time series. Only the intersecting tangents and the maximum 1st derivative methods for PI calculation produce the most accurate results in time domain BRS estimation. CONCLUSION: BRS estimation by proper analysis of pressure wave signals alone is feasible and accurate. Further studies are needed to investigate the clinical validity and relevance of the different BRS estimations in diagnostic, prognostic and therapeutic levels.

Simulation of a Cellular Automaton with Markov Chains: Applications in Self-Organized Dynamical Systems.

over the past years, technology has allowed information technology to contemplate complex events as well as complex semantic features to predict what types of "thoughts" are being conceptualized. The introduction of the neuro-robotics field allows a mix of different disciplines to inter-collate and produce actual results that could be considered outputs of a science-fiction novel 20 twenty years ago. In the present work, we attempted to present an example of how an automaton can move in an environment with obstacles, by regulating its behavior so as to allow a decision based on rewards and penalties. Examples of the robotic behavior, running on a virtual environment are presented, along with a discussion of its different possibilities expressed as a penalty function for the behavior of the robot.

KardiaSoft Architecture - A Software Supporting Diagnosis and Therapy Monitoring of Heart Failure Patients Exploiting Saliva Biomarkers.

The aim of this work is to present the architecture of the KardiaSoft software, a clinical decision support tool allowing the healthcare professionals to monitor patients with heart failure by providing useful information and suggestions in terms of the estimation of the presence of heart failure (heart failure diagnosis), stratification-patient profiling, long term patient condition evaluation and therapy response monitoring. KardiaSoft is based on predictive modeling techniques that analyze data that correspond to four saliva biomarkers, measured by a point-of-care device, along with other patient's data. The KardiaSoft is designed based on the results of a user requirements elicitation process. A small clinical scale study with 135 subjects and an early clinical study with 90 subjects will take place in order to build and validate the predictive models, respectively.

A Novel Real-time Automatic Angioectasia Detection Method in Wireless Capsule Endoscopy Video Feed.

The development of Wireless Capsule Endoscopy (WCE) revolutionized the examination of the small bowel for diseases. Upon swallowing a capsule (a microscopic camera that resembles an ordinary pill in both shape and size), images of the patient's gastrointestinal (GI) tract are wirelessly transmitted from it to an external recorder. The inspection of these images is, to this day, still manually performed by medical professionals - a lengthy, and especially prone to errors, process. One of the most common diagnoses is the presence of angioectasias, i.e. ectatic vessels on the GI tract that are predisposed to bleeding. In this paper, a novel method for automatic detection of these lesions is proposed, using a combination of low-level image processing, feature detection and machine learning, that can run in real-time without the need for specialized hardware or graphics cards, achieving 92.7% sensitivity and 99.5% specificity to angioectasias. This method can also be expanded to include more pathologies.

KardiaTool: An Integrated POC Solution for Non-invasive Diagnosis and Therapy Monitoring of Heart Failure Patients.

The aim of this work is to present KardiaTool platform, an integrated Point of Care (POC) solution for noninvasive diagnosis and therapy monitoring of Heart Failure (HF) patients. The KardiaTool platform consists of two components, KardiaPOC and KardiaSoft. KardiaPOC is an easy to use portable device with a disposable Lab-on-Chip (LOC) for the rapid, accurate, non-invasive and simultaneous quantitative assessment of four HF related biomarkers, from saliva samples. KardiaSoft is a decision support software based on predictive modeling techniques that analyzes the POC data and other patient's data, and delivers information related to HF diagnosis and therapy monitoring. It is expected that identifying a source comparable to blood, for biomarker information extraction, such as saliva, that is cost-effective, less invasive, more convenient and acceptable for both patients and healthcare professionals would be beneficial for the healthcare community. In this work the architecture and the functionalities of the KardiaTool platform are presented.

Evaluation for the Necessity of Medical Imaging Tests Prescription in Neurology.

The evaluation and control algorithms for the necessity of medical prescription testing, comprises useful tool for health professionals. It is beyond doubt that a connection between illness, symptoms, medical tests and prescriptions is essential and thus algorithms facilitating such approaches should be available to health professionals. Such informatics tools require the implementation of smart, interactive tools and not just linear, information storing websites. Such algorithms should be dynamic, that is their output should change based on the input as for example, in the serial input of symptoms to clinical examination to subsequent diagnosis. Slight variations in symptomatology can greatly alter diagnosis and subsequent physical testing and prescription. The present work presents a novel algorithm for the control of medical prescription testing in neurology, by utilizing decision trees for the connection of symptomatology to diagnosis and prescription for neurological conditions and disease. To the best of our knowledge this is the first time that such an approach is proposed.

Modeling and simulation of right ventricular volume measurement system during right heart catheterization.

Haemodynamic monitoring is necessary for the effective management of critically ill cardiac patients. Pulmonary artery catheterization has been used for monitoring the circulation, for measurement of intracardiac pressures and to estimate preload and afterload. However, pressures may not be accurate reflection of the circulation and simultaneous measurement of volumes would improve patient treatment. However, measurement of cardiac volumes especially of the right ventricle is difficult in everyday clinical practice In this work we propose the use of pulmonary artery catheter (PAC) with ultrasonic sensors built on it, to calculate the right ventricular end-diastolic (RVEDV) and end-systolic volume (RVESV). This is achieved by using the Ultrasonic (US) beam, to measure the distances between the transducers on the catheter and the RV walls. These distances, will be used as an input to a Volume calculating algorithm, which finally provides the RVEDV and RVESV, using a Neural Network (NN). For that reason, we have used cardiac Magnetic Resonance Imaging (MRI) and have modeled the catheter and the US transducers, to get as input the distances to the surface of the cavity. With these distances, and the known cardiac volumes (calculated using MR images) we trained and validated a NN for volume calculation. The results show that the algorithm accurately calculates the RVEDV. For the RVESV, greater deviations are observed between values calculated with our algorithm and cardiac MRI.