Time filter

Source Type

Karvounis E.C.,University of Ioannina | Tsipouras M.G.,University of Ioannina | Papaloukas C.,University of Ioannina | Tsalikakis D.G.,University of Ioannina | And 5 more authors.
Methods of Information in Medicine | Year: 2010

Objectives: This paper describes a methodology for the monitoring of the fetal cardiac health status during pregnancy, through the effective and non-invasive monitoring of the abdominal ECG signals (abdECG) of the mother. Methods: For this purpose, a three-stage methodology has been developed. In the first stage, the fetal heart rate (fHR) is extracted from the abdECG signals, using nonlinear analysis. Also, the eliminated ECG (eECG) is calculated, which is the abdECG after the maternal QRSs elimination. In the second stage, a blind source separation technique is applied to the eECG signals and the fetal ECG (fECG) is obtained. Finally, monitoring of the fetus is implemented using features extracted from the fHR and fECG, such as the T/QRS ratio and the characterization of the fetal ST waveforms. Results: The methodology is evaluated using a dataset of simulated multichannel abdECG signals: 94.79% accuracy for fHR extraction, 92.49% accuracy in T/QRS ratio calculation and 79.87% in ST waveform classification. Conclusions: The novel non-invasive proposed methodology is advantageous since it offers automated identification of fHR and fECG and automated ST waveform analysis, exhibiting a high diagnostic accuracy. © 2010 Schattauer.


Siogkas P.K.,University of Ioannina | Papafaklis M.I.,Harvard University | Sakellarios A.I.,University of Ioannina | Stefanou K.A.,Biomedical Research Institute FORTH | And 8 more authors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2013

Cardiovascular disease is one of the primary causes of morbidity and mortality around the globe. Thus, the diagnosis of critical lesions in coronary arteries is of utmost importance in clinical practice. One useful and efficient method to assess the functional severity of one or multiple lesions in a coronary artery is the calculation of the fractional flow reserve (FFR). In the current work, we present a method which allows the calculation of the FFR value computationally, without the use of a pressure wire and the induction of hyperemia, using intravascular ultrasound (IVUS) and biplane angiography images for three-dimensional (3D) coronary artery reconstruction and measurements of the volumetric flow rate derived from angiographic sequences. The simulated FFR values were compared to the invasively measured FFR values in 7 cases, presenting high correlation (r=0.85) and good agreement (mean difference=0.002). FFR assessment without employing a pressure wire and the induction of hyperemia is feasible using 3D reconstructed coronary artery models from angiographic and IVUS data coupled with computational fluid dynamics. © 2013 IEEE.


Athanasiou L.S.,University of Ioannina | Exarchos T.P.,Biomedical Research Institute FORTH | Naka K.K.,University of Ioannina | Michalis L.K.,University of Ioannina | And 2 more authors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2011

Optical Coherence Tomography (OCT) is a fiber optic imaging modality which produces high resolution tomographic images of the coronary lumen and outer vessel wall. While OCT images present morphological information in highly resolved detail, the characterization of the various plaque components relies on trained readers. The aim of this study is to extract a set of features in grayscale OCT images and to use them in order to classify the atherosclerotic plaque. Intensity and texture based features we used in order to classify the plaque in four plaque types: Calcium (C), Lipid Pool (LP), Fibrous Tissue (FT) and Mixed Plaque (MP). 50 OCT annotated images from 3 patients were used to train and test the proposed plaque characterization method. Using a Random Forests classifier overall classification accuracy 80.41% is reported. © 2011 IEEE.


Athanasiou L.S.,University of Ioannina | Karvelis P.S.,University of Ioannina | Tsakanikas V.D.,Biomedical Research Institute FORTH | Naka K.K.,University of Ioannina | And 3 more authors.
IEEE Transactions on Information Technology in Biomedicine | Year: 2012

Intravascular ultrasound (IVUS) virtual histology (VH-IVUS) is a new technique, which provides automated plaque characterization in IVUS frames, using the ultrasound backscattered RF-signals. However, its computation can only be performed once per cardiac cycle (ECG-gated technique), which significantly decreases the number of characterized IVUS frames. Also atherosclerotic plaques in images that have been acquired by machines, which are not equipped with the VH software, cannot be characterized. To address these limitations, we have developed a plaque characterization technique that can be applied in grayscale IVUS images. Our semiautomated method is based on a three-step approach. In the first step, the plaque area region of interest (ROI) is detected semiautomatically. In the second step, a set of features is extracted for each pixel of the ROI and in the third step, a random forest classifier is used to classify these pixels into four classes: dense calcium, necrotic core, fibrotic tissue, and fibro-fatty tissue. In order to train and validate our method, we used 300 IVUS frames acquired from virtual histology examinations from ten patients. The overall accuracy of the proposed method was 85.65 suggesting that our approach is reliable and may be further investigated in the clinical and research arena. © 2012 IEEE.


Tsipouras M.G.,Biomedical Research Institute FORTH
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2012

The SensorART project focus on the management of heart failure (HF) patients which are treated with implantable ventricular assist devices (VADs). This work presents the way that crisp models are transformed into fuzzy in the weaning module, which is one of the core modules of the specialist's decision support system (DSS) in SensorART. The weaning module is a DSS that supports the medical expert on the weaning and remove VAD from the patient decision. Weaning module has been developed following a "mixture of experts" philosophy, with the experts being fuzzy knowledge-based models, automatically generated from initial crisp knowledge-based set of rules and criteria for weaning.


Karvounis E.C.,Biomedical Research Institute FORTH
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2011

The scope of this paper is to present the Specialist's Decision Support System (SDSS), part of the overall Decision Support Framework that is developed under the SensorART platform. The SensorART platform focuses on the management and remote treatment of patients suffering from end-stage heart failure. The SDSS assists specialists on designing the best treatment plan for their patients before and after VAD implantation, analyzing patients' data, extracting new knowledge, and making informative decisions. It creates a hallmark in the field, supporting medical and VAD experts through the different phases of VAD therapy.


Karvounis E.C.,Biomedical Research Institute FORTH
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2012

In this work, the weaning module of the SensorART specialist decision support system (SDSS) is presented. SensorART focuses on the treatment of patients suffering from end-stage heart failure (HF). The use of a ventricular assist device (VAD) is the main treatment for HF patients. However in certain cases, myocardial function recovers and VADs can be explanted after the patient is weaned. In that framework an efficient module is developed responsible for the selection of the most suitable candidates for VAD weaning. In this study we describe all technical specifications concerning its two main sub-modules of the weaning module, of the Clinical Knowledge Editor and the Knowledge Execution Engine.


Karvounis E.C.,Biomedical Research Institute FORTH | Stefanou K.,Biomedical Research Institute FORTH | Exarchos T.P.,Biomedical Research Institute FORTH | Tzallas A.T.,Biomedical Research Institute FORTH | And 2 more authors.
Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012 | Year: 2012

The platform of SensorART emphases on the controlling and remote treatment of patients suffering from failure of the heart, using an implantable Ventricular Assist Device (VAD). It provides an extendable, interoperable and independent from VAD solution, which includes different software and hardware components in a holistic approach, in order to improve the quality the treatment of the patients and the workflow of the medical experts. In this work, we present one of the core modules of the SensorART platform, the specialist's decision support system (SDSS) which assists health professionals in designing the best treatment strategy. The SDSS supports the specialists on deciding the best treatment strategy for a VAD patient. It has the following functionalities: a) Selection of the most suitable candidates for VAD weaning, b) Decision of the most appropriate treatment strategy for the medication process, c) Analysis of patients' data and the extraction of new knowledge, d) Detection of different pump states and the identification possible issues associated to the suction phenomenon and e) Identification of the most proper pump speed settings. The SDSS combines medical knowledge with efficient data-driven techniques, using a design that is based on approved principles and standards that ensure the avoidance of either business or technical barriers [1]. © 2012 IEEE.


Karvounis E.C.,Biomedical Research Institute FORTH | Katertsidis N.S.,Biomedical Research Institute FORTH | Exarchos T.P.,Biomedical Research Institute FORTH | Fotiadis D.I.,Biomedical Research Institute FORTH
10th International Workshop on Biomedical Engineering, BioEng 2011 | Year: 2011

The scope of this paper is to present in detail the Specialist's main components of the SensorART platform, specifically the Monitoring Application and the Decision Support System (SDSS). The former provides to the specialists tele-monitoring and tele-controlling functionalities, while the latter assists the specialists on deciding the best treatment strategy for a specific patient. © 2011 IEEE.


Karvounis E.C.,Biomedical Research Institute FORTH | Katertsidis N.S.,Biomedical Research Institute FORTH | Exarchos T.P.,Biomedical Research Institute FORTH | Fotiadis D.I.,Biomedical Research Institute FORTH
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2011

The scope of this paper is to present the Specialist's Decision Support System (SDSS), part of the overall Decision Support Framework that is developed under the SensorART platform. The SensorART platform focuses on the management and remote treatment of patients suffering from end-stage heart failure. The SDSS assists specialists on designing the best treatment plan for their patients before and after VAD implantation, analyzing patients' data, extracting new knowledge, and making informative decisions. It creates a hallmark in the field, supporting medical and VAD experts through the different phases of VAD therapy. © 2011 IEEE.

Loading Biomedical Research Institute FORTH collaborators
Loading Biomedical Research Institute FORTH collaborators