June 24, 2017

Posters II

Conference room
Session time
Saturday, June 24, 2017 - 09:00 to 15:45
A reworked SOBI algorithm based on SCHUR Decomposition for EEG data processing
Abstract: 
In brain machine interfaces (BMI) that are used to control motor rehabilitation devices there is the need to process the monitored brain signals with the purpose of recognizing patient’s intentions to move his hands or limbs and reject artifact and noise superimposed on these signals. This kind of processing has to take place within time limits imposed by the on-line control requirements of such devices. A widely used algorithm is the Second Order Blind Identification (SOBI,) independent component analysis (ICA) algorithm.
Gregory Kalogiannis's picture
Gregory Kalogiannis
Aristotle University of Thessaloniki (GR)
Nikolaos Karampelas's picture
Nikolaos Karampelas
George Hassapis's picture
George Hassapis
Relation between fetal HRV and value of umbilical cord artery pH in labor, a study with entropy measures
Abstract: 
The relation between fetal heart rate and the value of umbilical cord artery pH is not something new for the scientific community. However, the problem has not been investigated sufficiently. One reason for that is the lack of open databases with a large number of recordings. Such a database is used here, recently publicly available, with cardiotocographic data recorded approximately two hours before delivery and until the end of the delivery. We use entropy measures to investigate how the value of umbilical cord artery pH is correlated to the variability of the fetal heart rhythm.
George Manis's picture
George Manis
University of Ioannina (GR)
Roberto Sassi's picture
Roberto Sassi
A comparison study on EEG signal processing techniques using motor imagery EEG data
Abstract: 
Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. In this work we provide a review of various existing techniques for the identification of motor imagery (MI) tasks. More specifically we perform a comparison between CSP related features and features based on Power Spectral Density (PSD) techniques.
Vangelis Oikonomou's picture
Vangelis Oikonomou
Centre for Research and Technology Hellas CERTH - ITI (GR)
Kostas Georgiadis's picture
Kostas Georgiadis
Georgios Liaros's picture
Georgios Liaros
Spiros Nikolopoulos's picture
Spiros Nikolopoulos
Centre for Research and Technology Hellas ITI-CERTH (GR)
Ioannis Kompatsiaris's picture
Ioannis Kompatsiaris
Meeting technology and methodology into Health Big Data Analytics scenarios
Abstract: 
Health organizations are collecting more data from a wider array of sources at greater speed every day. The analysis of this vast amount and array of data creates new opportunities to deliver modern personalized health and social care services. Big Data Analytics and underlying technologies have the potential to process and analyze these data to extract meaningful insights for improving quality of care, efficiency and sustainability of health and social care systems.
Pedro Gonzalez Alonso's picture
Pedro Gonzalez Alonso
Universitat Oberta de Catalunya (UOC)
Ruth Vilar's picture
Ruth Vilar
Universitat Oberta de Catalunya (SP)
Francisco Lupianez Villanueva's picture
Francisco Lupianez Villanueva
User and Stakeholder Requirements of eHealth Support Tool Viewed In a Self-Determination Theory Lens
Abstract: 
This paper presents preliminary results of an analysis of user requirements for an eHealth tool supporting chronic patients to use their personal strengths in health management. We conclude that Self-Determination Theory can be applied to view and categorize identified user requirements, and provide a framing for the analysis grounded in motivational theory. The final model will lay the foundation for our future design and implementation of gameful designs in an eHealth tool in order to enhance user engagement, motivation, and adherence.
Stian Jessen's picture
Stian Jessen
Oslo University Hospital (NO)
Jelena Mirkovic's picture
Jelena Mirkovic
Oslo University Hospital (NO)
Cornelia Ruland's picture
Cornelia Ruland
Overlap detection for a genome assembly based on genomic signal processing
Abstract: 
Although the genome sequences of most studied organisms, like human, E. coli, and others are already known, de novo genome sequencing remains popular as a majority of genomes remains unknown. Unfortunately, sequencing machines are able to read only short fragments of DNA. Therefore, one of the basic steps in reconstructing novel genomes lies in putting these pieces of DNA, called ‘reads’, together into complete genome sequences using a process known as genome assembly. Reads joining, however, requires efficient detection of their overlaps.
Robin Jugas's picture
Robin Jugas
Brno University of Technology (CZ)
Karel Sedlar's picture
Karel Sedlar
Helena Skutkova's picture
Helena Skutkova
Martin Vitek's picture
Martin Vitek
Versatile cloud collaboration services for device-transparent medical imaging teleconsultations
Abstract: 
"In this work, we present a novel web-based platform for real-time teleconsultation services on medical imaging. The introduced platform encompasses the principles of heterogeneous Workflow Management Systems (WFMSs) and the peer-to-peer paradigm to enhance collaboration among healthcare professionals.
Georgios Rassias's picture
Georgios Rassias
Christos O. Andrikos's picture
Christos O. Andrikos
Panayiotis Tsanakas's picture
Panayiotis Tsanakas
Ilias Maglogiannis's picture
Ilias Maglogiannis
University of Piraeus (GR)
Z-Box merging: Ultra-fast computation of Fractal Dimension and Lacunarity
Abstract: 
"In [1], a reasonably fast method of Fractal Dimension estimation was presented. However, it has a weak point: After each subdivision of the partition table, pixels that might have been quite far apart originally might end up needing to be merged. Thus, after each subdivision, the partition table needs to be re-sorted, which is a computationally expensive operation. The ideal solution would be to order the pixels of the image in such a way, that a) pixels that are in the same cube are consecutive and b) they remain in that order even after each subdivision.
Elias Aifantis's picture
Elias Aifantis
Using Prevalence Patterns to Discover Un-Mapped Flowsheet Data in an Electronic Health Record Data Warehouse
Abstract: 
We have developed a data summarization tool called Chi2notype which leverages the star schema of the Integrating Informatics from Bench to Bedside (i2b2) vendor-neutral data-warehouse platform to characterize a patient-cohort of interest. Chi2notype calculates a chi-squared statistic for every one of the tens of thousands of facts in an Electronic Medical Record System (EMR) and uses it to rank them from most over-represented in the cohort to most under-represented.
Alex Bokov's picture
Alex Bokov
UT Health San Antonio (USA)
Angela B. Bos's picture
Angela B. Bos
Laura S. Manuel's picture
Laura S. Manuel
Alfredo Tirado-Ramos's picture
Alfredo Tirado-Ramos
University of Texas Health at San Antonio (USA)
Pamela Kittrell's picture
Pamela Kittrell
Carlayne Jackson's picture
Carlayne Jackson
Gail P. Olin's picture
Gail P. Olin
ACESO: Analysis of Cervical cancer: an Evidence-based treatmentS Optimization
Abstract: 
Deciding for Cervical Cancer (CxCa) treatment is not a simple task. There are several competing factors that arise from the perspective of survival, treatment, toxicity, quality of patient’s life, as well as the geographic location of the patient, which indicates access to specific healthcare resources. All of these factors play a significant role in the ultimate decision to pursue surgery, chemotherapy, and radiation therapy.
Panagiotis Katrakazas's picture
Panagiotis Katrakazas
Marilena Tarousi's picture
Marilena Tarousi
National Technical University of Athens (GR)
Kostas Giokas's picture
Kostas Giokas
AiM Research Biomedical Engineering Lab NTUA (GR)
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Automatic pigment network classification using a combination of classic texture descriptors and CNN features
Abstract: 
The presence of atypical (irregular) pigment networks can be a symptom of melanoma malignum in skin lesions, thus, their proper recognition is a critical task. For object classification problems, the application of deep convolutional neural nets (CNN) receives priority attention in these days for their high recognition rate. The descriptive features found by CNNs are more hidden than the classically applied ones for texture recognition. In this paper, we investigate whether CNN features outperform the classic texture descriptors in the classification of typical/atypical pigment network.
Melinda Pap's picture
Melinda Pap
Balazs Harangi's picture
Balazs Harangi
University of Debrecen (HU)
Andras Hajdu's picture
Andras Hajdu
University of Debrecen (HU)
Anomaly detection through temporal abstractions on intensive care data: position paper
Abstract: 
A large amount of data is continuously generated in intensive health care. An analysis of these streaming data can provide important information to improve the monitoring of the health conditions of patients. The volume, velocity and complexity of these data, which come unlabeled, make their analysis a challenging task. Machine learning techniques have been successfully used for data stream mining.
Giovana Jaskulski Gelatti's picture
Giovana Jaskulski Gelatti
University of Sao Paulo (BR)
André Carlos Ponce de Leon Ferreira de_Carvalho's picture
André Carlos Ponce de Leon Ferreira de_Carvalho
Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Extracting disease-phenotype relations from text with disease-phenotype concept recognisers and association rule mining
Abstract: 
Automatically extracting phenotypes (i.e., the composite of one’s observable characteristics/traits) from free text such as scientific literature or clinical notes and associating phenotypes with diseases is an important task. Such associations can be used in, for example, recommending candidate genes for diseases, investigating drug targets, or performing differential diagnosis. In this paper we focus on extracting disease-phenotype relations with association rule mining techniques and compare results with two other methods.
Simon Kocbek's picture
Simon Kocbek
Garvan Institute of Medical Research Sydney (AU)
Tudor Groza's picture
Tudor Groza
An Object Oriented Approach to Model Reusability
Abstract: 
Sexual contact networks for disease transmission have been used extensively with HIV and provide valuable insight into the way the disease spreads through a population. These computationally intensive models often suffer from lack of reusability which makes them expensive to create and use. We take an innovated approach to build an HIV transmission model designed for expansion and reusability, and add additional functionality for model realism.
Laura Manuel's picture
Laura Manuel
University of Texas Health Science Center (USA)
Alfredo Tirado-Ramos's picture
Alfredo Tirado-Ramos
University of Texas Health at San Antonio (USA)
Manuel Castanon Puga's picture
Manuel Castanon Puga
Bringing Bayesian networks to bedside: a web-based framework
Abstract: 
Bayesian networks are one of the most intuitive statistical models for both estimation, classification and prediction of patients’ outcomes. However, the availability of inference software in clinical settings is still limited. This work presents preliminary steps towards the creation of simple web-based forms that can access a powerful Bayesian network inference engine, making the derived models usable at bedside by both the clinicians and the patients themselves.
Raphael Oliveira's picture
Raphael Oliveira
Joana Ferreira's picture
Joana Ferreira
CINTESIS - FMUP (PT)
Diogo Libanio's picture
Diogo Libanio
Claudia Camila Dias's picture
Claudia Camila Dias
Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Modeling and Integrating Human Interaction Assumptions in Medical Cyber-Physical System Design
Abstract: 
For a cyber-physical system, its execution behaviors are often impacted by human interactive behaviors. However, the assumptions about a cyber-physical system’s expected human interactive behaviors are often informally documented, or even left implicit and unspecified in system design. Unfortunately, such implicit human interaction assumptions made by safety critical cyber-physical systems, such as medical cyber-physical systems (M-CPS), can lead to catastrophes. Several recent U.S. Food and Drug Administration (FDA) medical device recalls are due to implicit human interaction assumptions.
Zhicheng Fu's picture
Zhicheng Fu
Chunhui Guo's picture
Chunhui Guo
Illinois Institute of Technology (USA)
Shangping Ren's picture
Shangping Ren
Yi-Zong Ou's picture
Yi-Zong Ou
Lui Sha's picture
Lui Sha
A Cloud based Big Data Based Online Health Analytics for Rural NICUs and PICUs in India: Opportunities and Challenges
Abstract: 
High frequency physiological data has great potential to provide new insights for many conditions patients can develop in critical care when utilized by Big Data Analytics based Clinical Decision Support Systems, such as Artemis. Artemis was deployed in NICU at SickKids Hospital in Toronto in August 2009. It employs all the potentiality of big data. Both original data together with newly generated analytics is stored to the data persistence component of Artemis. Real-time analytics is performed in the Online Analytics component.
S. Balaji's picture
S. Balaji
Meghana Bastwadkar's picture
Meghana Bastwadkar
Carolyn Mcgregor's picture
Carolyn Mcgregor
University of Ontario (CA)
Visual Scanning Behaviour during a visual search task: an objective indicator for white matter integrity in patients with post-concussion syndrome
Abstract: 
"Post-concussion syndrome (PCS) is associated with incomplete recovery following a mild traumatic brain injury (mTBI). Often, PCS is characterised by microstructural damage to white matter tracts in the brain. Currently, there is no biomarker to screen for such damage. In this paper we present preliminary result for a novel and simple to administer paradigm that can be used to test the microstructural integrity of the corpus callosum. The novel paradigm is based on the Matching Familiar Figures Test (MFFT).
Jonathan Chung's picture
Jonathan Chung
Foad Taghdiri's picture
Foad Taghdiri
Samantha Irwin's picture
Samantha Irwin
Namita Multani's picture
Namita Multani
Apameh Tarazi's picture
Apameh Tarazi
Ahmed Ebraheem's picture
Ahmed Ebraheem
Mozhgan Khodadadi's picture
Mozhgan Khodadadi
Ruma Goswami's picture
Ruma Goswami
Richard Wennberg's picture
Richard Wennberg
Robin Green's picture
Robin Green
David Mikulis's picture
David Mikulis
Karen Davis's picture
Karen Davis
Charles Tator's picture
Charles Tator
Carmela Tartaglia's picture
Carmela Tartaglia
Moshe Eizenman's picture
Moshe Eizenman
University of Toronto (CA)
Level set based on brain radiological densities for stroke segmentation in CT images
Abstract: 
Cardiovascular diseases (CVD) are the leading cause of death worldwide, and every year more people die of these diseases. Aiming to assist medical diagnoses through Computerized Tomography (CT) scans, this work proposes a new approach to segment CT images of the brain damaged by stroke. The proposed method takes into account two improvements of the level set method based on the likelihood of Normal distribution.
Elizangela De Souza Rebouças's picture
Elizangela De Souza Rebouças
Alan M. Braga's picture
Alan M. Braga
Roger Moura Sarmento's picture
Roger Moura Sarmento
Segmentation and visualization of the lungs in three dimensions using 3D Region Growing and Visualization Toolkit in CT examinations of the chest
Abstract: 
Computed tomography (CT) stands out among the exams used by computer-aided diagnosis in medical imaging as it provides the visualization of internal organs such as the lungs and their structures. This paper focuses on the segmentation of lungs using a three-dimensional region growing (3D RG) method and the registration toolkit ITK library. To evaluate the proposed segmentation method, we used 30 exams from the LAPISCO image database.
Raul Victor Medeiros Nobrega's picture
Raul Victor Medeiros Nobrega
Murillo Barata Rodrigues's picture
Murillo Barata Rodrigues
Application of affinity analysis techniques on diagnosis and prescription data
Abstract: 
"This study performs an Affinity Analysis on diagnosis and prescription data in order to discover co-occurrence relationships among diagnosis and pharmaceutical active ingredients prescribed to different patient groups. The analysis data collected during consecutive visits of 4,473 patients in a 3 years period, focused on patients suffering by hypertension and/or hypercholesterolemia and applied association rule and sequential rule mining techniques.
Theodora Sanida's picture
Theodora Sanida
Harokopio University of Athens (GR)
Iraklis Varlamis's picture
Iraklis Varlamis
An Automatic EEG Based System For The Recognition of Math Anxiety
Abstract: 
Mathematical Anxiety is the feeling of fear or dislike when dealing with mathematically rich situations. Although MA seems innocent in general, it can seriously compromise math performance, lead to avoidance, affect the learning procedure, as well as influence future career choices and directions. The accurate recognition of MA, apart from diagnostic purposes, is considered to be very important for e-learning systems as well. This work presents an automatic system for the detection of MA based on electroencephalographic (EEG) signals.
Manousos A. Klados's picture
Manousos A. Klados
Aston University (UK)
Niki Pandria's picture
Niki Pandria
Aristotle University of Thessaloniki (GR)
Alkinoos Athanasiou's picture
Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Automatic Sleep Stage Classification Applying Machine Learning Algorithms on EEG Recordings
Abstract: 
This paper focuses on developing a novel approach to automatic sleep stage classification based on electroencephalographic (EEG) data. The proposed methodology employs contemporary mathematical tools such as the synchronization likelihood and graph theory metrics applied on sleep EEG data. The derived features are then fitted into three different machine learning techniques, namely k-nearest neighbors, support vector machines and neural networks. The evaluation of their comparative performance is investigated according to their accuracy.
Panteleimon Chriskos's picture
Panteleimon Chriskos
Dimitra Kaitalidou's picture
Dimitra Kaitalidou
Georgios Karakasis's picture
Georgios Karakasis
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Polyxeni Gkivogkli's picture
Polyxeni Gkivogkli
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Chrysoula Kourtidou-Papadeli's picture
Chrysoula Kourtidou-Papadeli
Visual Versus Kinesthetic Motor Imagery For BCI Control Of Robotic Arms (Mercury 2.0)
Abstract: 
Motor Imagery (MI), the mental execution of an action, is widely applied as a control modality for electroencephalography (EEG) based Brain-Computer Interfaces (BCIs). Different approaches to MI have been implemented, namely visual observation (VMI) or kinesthetic rehearsal (KMI) of movements. Although differences in brain activity during VMI or KMI have been studied, no investigation with regards to their suitability for BCI applications has been made.
George Arfaras's picture
George Arfaras
Alkinoos Athanasiou's picture
Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)
Niki Pandria's picture
Niki Pandria
Aristotle University of Thessaloniki (GR)
Kyriaki Rafailia Kavazidi's picture
Kyriaki Rafailia Kavazidi
Lab of Medical Physics The Medical School Aristotle University of Thessaloniki
Panagiotis Kartsidis's picture
Panagiotis Kartsidis
Aristotle University of Thessaloniki (GR)
Alexander Astaras's picture
Alexander Astaras
American College of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Visual Role in the Dynamic Postural Balance due to Aging – Assessment by using an Augmented Reality Purterbation System
Abstract: 
Sensorimotor modulation is degenerated due to aging, and it may cause the elderly falling in a daily activity. To evaluate the postural balance ability, the clinical functional balance assessments, somatosensory test, muscle strength and the joint motion measurement are usually applied to evaluate the falling potential in the aged population. However, the ceiling effect is found among the sub-healthy elderly with high functional mobility but has a potential risk of falling.
Chun-Ju Chang's picture
Chun-Ju Chang
Sai-Wei Yang's picture
Sai-Wei Yang
National Yang-Ming University (TW)
Jen-Suh Chern's picture
Jen-Suh Chern
Tsui-Fen Yang's picture
Tsui-Fen Yang
Are elderly less responsive to emotional stimuli? An EEG-based study across pleasant, unpleasant and neutral Greek words
Abstract: 
A plethora of studies has shown that working memory, processing speed and flowing intelligence are diminished with aging. However, emotional processing remains relatively stable even though young and old seem to process emotions differently. Neurophysiological studies have employed emotional stimuli to investigate age differences through Event Related Potentials (ERPs). The present approach used affective visual word stimuli derived from the Greek language. Healthy young and elderly volunteers passively viewed the stimuli which were divided into pleasant, unpleasant and neutral.
Ioanna Tepelena's picture
Ioanna Tepelena
Aristotle University of Thessaloniki (GR)
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Vasiliki Salvari's picture
Vasiliki Salvari
Leontios Hadjileontiadis's picture
Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Rehabilitation Biofeedback Using EMG Signal Based on Android Platform
Abstract: 
The aim of this paper is to introduce an integrated system for accurate and easy rehabilitation process. A small gadget and an installed android application for on line follow up and continuous enhancement with affordable cost. The gadget consists of instrumentation amplifier, filtration process and rectifier. The gadget is communicating with android application via Bluetooth device which send the signal to patient application. Acquiring and signal processing is implemented by application for both patient and doctor.
Mazen Yassin's picture
Mazen Yassin
Minia university (EG)
Hussein Abdallah's picture
Hussein Abdallah
Amr Anwer's picture
Amr Anwer
Abubakr Mustafa's picture
Abubakr Mustafa
Ashraf Mahroos's picture
Ashraf Mahroos
Evaluating the AffectLecture mobile app within an elementary school class teaching process
Abstract: 
Elementary school students experience significant personal changes; their bodies change, as well as their inner selves. Their emotional status can be affected by both intristic and extrinsic factors; therefore, it is vital to highlight the emotional factors that influence the learning process, as a negative emotional status may lead to reduced motivation and low school performance, whereas a positive one may bring the opposite results. The aim of this study is to evaluate the effects of the teaching process onto the students’ emotional status, and how it affects their academic performance.
Styliani Siouli's picture
Styliani Siouli
Aristotle University of Thessaloniki (GR)
Ioanna Dratsiou's picture
Ioanna Dratsiou
Aristotle University of Thessaloniki (GR)
Meni Tsitouridou's picture
Meni Tsitouridou
Aristotle University of Thessaloniki (GR)
Panagiotis Kartsidis's picture
Panagiotis Kartsidis
Aristotle University of Thessaloniki (GR)
dspachos's picture
dspachos
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Assessing Emotional Impact of Biofeedback and Neurofeedback Training in Smokers during a Smoking Cessation Project
Abstract: 
This pilot study was conducted in the framework of SmokeFreeBrain project and it aimed at assessing the subjective emotional impact of skin temperature training and neurofeedback training on smokers by means of the AffectLecture application. The current paper constitutes a proof-of-concept, exploring the case of a single participant. The intervention consists of 5 sessions of biofeedback followed by 20 sessions of neurofeedback. Both pre- and post- biofeedback and neurofeedback training subjective scores of the participant’s mood were collected through the application.
Niki Pandria's picture
Niki Pandria
Aristotle University of Thessaloniki (GR)
dspachos's picture
dspachos
Aristotle University of Thessaloniki (GR)
Alkinoos Athanasiou's picture
Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Conditional Entropy Based Retrieval Model in Patient-Carer Conversational Cases
Abstract: 
Assistant Robots can be an efficient and low-cost solution to Patient-Care. One important aspect of Assistant Bots is succesful as well as Socially Intelligent Communication with the Patient. A new Conditional Entropy Retrieval Based model is proposed and also an Attitude Modeling based on Popitz Powers. The Conditional Entropy Model and the Attitude Model are combined in order to record Attitude Changes in Dialogue Interactions between Patients and Doctors.
Antonis Billis's picture
Antonis Billis
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Ioannis Antoniou's picture
Ioannis Antoniou
Nikolaos Hasanagas's picture
Nikolaos Hasanagas
Charalampos Bratsas's picture
Charalampos Bratsas
Open Knowledge Greece (GR)
Provisioning and Delivering Sepsis Data Supported by an Enhanced SDN Environment
Abstract: 
"Medical applications, along with Information and Communication Technology (ICT), have contributed with many solutions to supporting the treatment of SEPSIS. However, there are few solutions for the transport of sepsis data with QoS (Quality of Service). Using SDN (Software-Defined Networking), in this paper we propose a self-manageable architecture for the provision and delivery of sepsis data. To evaluate our proposal, we conducted our experiments in the laboratory.
Felipe Volpato's picture
Felipe Volpato
Universidade Federal de Santa Catarina (BR)
Madalena Pereira Da Silva's picture
Madalena Pereira Da Silva
Alexandre Leopoldo Gonçalves's picture
Alexandre Leopoldo Gonçalves
Marcio Castro's picture
Marcio Castro
Mario A. R. Dantas's picture
Mario A. R. Dantas
Memorandum: An Android App for Efficient Note Keeping in Concurrent Multi-Participant Human Subject Studies
Abstract: 
Note keeping is an indispensable ingredient of successful research. Although traditionally performed on paper, recently the task is increasingly facilitated by Electronic Lab Notebooks, i.e., ICT programs that allow their users to make electronic observation in laboratory contexts. Owing to recent advances in mobile technologies, Smartphones and Tablets have emerged as promising alternatives for electronic note keeping.
Leandros Stefanopoulos's picture
Leandros Stefanopoulos
Aristotle University Thessaloniki (GR)
Christos Maramis's picture
Christos Maramis
Ioannis Moulos's picture
Ioannis Moulos
Ioannis Ioakimidis's picture
Ioannis Ioakimidis
Nicos Maglaveras's picture
Nicos Maglaveras
Methods for enhancing the reproducibility of observational research using electronic health records: preliminary findings from the CALIBER resource
Abstract: 
The ability of external investigators to reproduce published scientific findings is critical for the evaluation and validation of health research by the wider community. However, a substantial proportion of health research using electronic health records, data collected and generated during routine clinical care, cannot be reproduced. With the complexity, volume and variety of electronic health records made available for research steadily increasing, it is critical to ensure that findings from such data are reproducible and replicable by researchers.
Spiros Denaxas's picture
Spiros Denaxas
University College London (UK)
Arturo Gonzalez-Izquierdo's picture
Arturo Gonzalez-Izquierdo
Maria Pikoula's picture
Maria Pikoula
Kenan Direk's picture
Kenan Direk
Natalie Fitzpatrick's picture
Natalie Fitzpatrick
Harry Hemingway's picture
Harry Hemingway
Liam Smeeth's picture
Liam Smeeth
Evaluating openEHR for storing computable representations of electronic health record-driven phenotyping algorithms
Abstract: 
Electronic Health Records (EHR) are structured and unstructured data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of translation and enable precision medicine at scale. One of the main use-cases of EHR is creating algorithms to define disease status, onset and severity from diagnoses, prescriptions, laboratory tests, symptoms and other EHR elements - a process known as phenotyping.
Vaclav Papez's picture
Vaclav Papez
University College London (UK)
Spiros Denaxas's picture
Spiros Denaxas
University College London (UK)
Probabilistic integration of large Brazilian socioeconomic and clinical databases
Abstract: 
The integration of disparate large and heterogeneous socioeconomic and clinical databases is now considered essential to capture and model longitudinal and social aspects of diseases. However, such integration has significant challenges associated with it. Databases are often stored in disparate locations, make use of different identifiers, have variable data quality, record information in bespoke purpose-specific formats and have different levels of associated metadata.
Marcos Barreto's picture
Marcos Barreto
University College London (UK)
Clicia Pinto's picture
Clicia Pinto
Robespierre Pita's picture
Robespierre Pita
George Barbosa's picture
George Barbosa
Samila Sena's picture
Samila Sena
Rosemeire Fiaccone's picture
Rosemeire Fiaccone
Leila D. A. F. Amorim's picture
Leila D. A. F. Amorim
Maria Yuri Ichihara's picture
Maria Yuri Ichihara
Mauricio Barreto's picture
Mauricio Barreto
Spiros Denaxas's picture
Spiros Denaxas
University College London (UK)
Sandra Reis's picture
Sandra Reis
Bruno Araujo's picture
Bruno Araujo
Juracy Bertoldo's picture
Juracy Bertoldo
A Three-phase Motor Relearning: error-potentials, motor correction and neurofeedback for memory-consolidation
Abstract: 
Three components are embedded in relearning of motor skills: error detection, training to reduce errors, and memory consolidation of the improved motor execution. We integrate here three components that we previously reported to suggest a three stage paradigm for error detection via EEG, correction through BCI and memory consolidation of the corrected motor motion through neurofeedback memory consolidation. In our earlier work we that the characteristics of the error potential is associated with the type of error, rather than being generic, by that providing a BCI system for error correction.
Miriam Reiner's picture
Miriam Reiner
Predicting Cognitive Recovery of Stroke Patients from the Structural MRI Connectome using a Naïve Bayesian Tree Classifier
Abstract: 
Successful post-stroke prognosis and recovery strategies are heavily dependent on our understanding about how the damage to one specific region may impact to other remote regions, as well as the various functional networks involved in efficient cognitive function. In this study, 27 consecutive ischemic stroke patients were recruited. Stroke patients underwent two complete neuropsychological assessments between the first 72 hours after stroke arrival and three months later. They were further evaluated with a MRI protocol at 3 months.
Rosalia Dacosta-Aguayo's picture
Rosalia Dacosta-Aguayo
Christian Stephan-Otto's picture
Christian Stephan-Otto
Tibor Auer's picture
Tibor Auer
Ic Clemente's picture
Ic Clemente
Antoni Davalos's picture
Antoni Davalos
Nuria Bargallo's picture
Nuria Bargallo
Maria Mataro's picture
Maria Mataro
Manousos A. Klados's picture
Manousos A. Klados
Aston University (UK)

ST3 - Immersive Personalized Patient Engagement; Computer Based Medical Intervention Systems

Panagiotis Antoniou's picture
Panagiotis Antoniou
Aristotle University of Thessaloniki (GR)
Daphne Economou's picture
Daphne Economou
University of Westminster (UK)
Conference room
Session time
Saturday, June 24, 2017 - 09:00 to 11:00
Detecting Gamification in Breast Cancer Apps: an automatic methodology for screening purposes
Abstract: 
Breast cancer is the most common cancer in women both in developed and developing countries. More than half of all cancer mobile application concern breast cancer. Gamification is widely used in mobile software applications created for health-related services. Current prevalence of gamification in breast cancer apps is unknown and detection must be manually performed. The purpose of this study is to describe and produce a tool allowing automatic detection of apps which contain gamification elements and thus empowering researchers to study gamification using large data samples.
Guido Giunti's picture
Guido Giunti
Salumedia Tecnologias (SP)
Diego Giunta's picture
Diego Giunta
Santiago Hors-Fraile's picture
Santiago Hors-Fraile
Minna Isomursu's picture
Minna Isomursu
Diana Karoseviciute's picture
Diana Karoseviciute
Connected Health in Multiple Sclerosis: a mobile applications review
Abstract: 
Multiple Sclerosis (MS) is an unpredictable, often disabling disease that can adversely affect any body function; this often requires persons with MS to be active patients who are able to self-manage. There are currently thousands of health applications available but it is unknown how many concern MS. We conducted a systematic review of all MS apps present in the most popular app stores (iTunes and Google Play store) on June 2016 to identify all relevant MS apps. After discarding non-MS related apps and duplicates, only a total of 25 MS apps were identified.
Guido Giunti's picture
Guido Giunti
Salumedia Tecnologias (SP)
Estefania Guisado-Fernandez's picture
Estefania Guisado-Fernandez
Brian Caulfield's picture
Brian Caulfield
A Framework for Morphological Feature Extraction of Organs from MR Images for Detection and Classification of Abnormalities
Abstract: 
In clinical practice, a misdiagnosis can lead to incorrect treatment, delayed treatment, or in some cases, no treatment at all; consequently, the condition of a patient may worsen to varying degrees, in some cases proving fatal. The accurate 3D reconstruction of organs, which is a pioneering tool of medical image computing (MIC) technology, plays a key role in computer aided diagnosis (CADx), whereby enabling medical professionals to perform enhanced analysis on a region of interest.
Barbara Villarini's picture
Barbara Villarini
University of Westminster (UK)
Hykoush Asaturyan's picture
Hykoush Asaturyan
E. Louise Thomas's picture
E. Louise Thomas
Rhys Mould's picture
Rhys Mould
Jimmy D Bell's picture
Jimmy D Bell
Visualization of Wearable Data and Biometrics for Analysis and Recommendations in Childhood Obesity
Abstract: 
Obesity is one of the major health risk factors behind the rise of non-communicable conditions. Understanding the factors influencing obesity is very complex since there are many variables that can affect the health behaviors leading to it. Nowadays, multiple data sources can be used to study health behaviors, such as wearable sensors for physical activity and sleep, social media, mobile and health data. In this paper we describe our experiences with the design of a dashboard for the visualization of actigraphy and biometric data from a childhood obesity camp in Qatar.
Michael Aupetit's picture
Michael Aupetit
Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
Meghna Singh's picture
Meghna Singh
Qatar Computing Research Institute (QA)
Jaideep Srivastava's picture
Jaideep Srivastava
The 360QS Toolkit for Sleep and Physical Activity Analysis based on Wearables
Abstract: 
Sleep and physical activity are human behaviors that play a major role in our health. Poor sleep or lack of physical activity have been found to increase health risks and reduce quality of life. The rapid adoption and evolution of pervasive computing systems, both in the health and wellness domain, are creating a new data-intensive context in which we can learn about the sleep and physical activity patterns of individuals. In this paper we provide an overview of the toolkit we have developed to conduct research on personal health data about sleep and physical activity.
Meghna Singh's picture
Meghna Singh
Qatar Computing Research Institute (QA)
Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
Jaideep Srivastava's picture
Jaideep Srivastava
PhytoCloud: A Gamified Mobile Web Application to Modulate Diet and Physical Activity of Women with Breast Cancer
Abstract: 
Breast cancer incidence and mortality rates vary geographically reflecting factors including regional and cultural differences in diet and lifestyle. There are numerous successful commercial mobile apps to help dieters control their diet and manage weight. However, such products are not suitable for people with special medical conditions that may require targeted dietary as well as motivational support.
Daphne Economou's picture
Daphne Economou
University of Westminster (UK)
Miriam Dwek's picture
Miriam Dwek
Claire Roberston's picture
Claire Roberston
Elliott Bradley's picture
Elliott Bradley
Thanos Kounenis's picture
Thanos Kounenis
Mohammad Ramezanian's picture
Mohammad Ramezanian
Nathan Bell's picture
Nathan Bell
Tayebeh Azimi's picture
Tayebeh Azimi
Towards evidence based m-health application design in cancer patient healthy lifestyle interventions.
Abstract: 
Cancer is one of the most prevalent diseases in Europe and the world. Significant correlations between dietary habits and cancer incidence and mortality have been confirmed by the literature. Physical activity habits are also directly implicated in the incidence of cancer. Lifestyle behaviour change may be benefited by using mobile technology to deliver health behaviour interventions. M-Health offers a promising cost-efficient approach to deliver en-masse interventions.
Panagiotis Antoniou's picture
Panagiotis Antoniou
Aristotle University of Thessaloniki (GR)
Octavio Rivera-Romero's picture
Octavio Rivera-Romero
Maria Karagianni's picture
Maria Karagianni
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Virtual scenarios for stealth assessment of the elderly; Perceptions and acceptance of technology-based health and wellness interventions.
Abstract: 
As adults get older, the risks are increasing on their health, such as chronic diseases, functional decline and geriatric syndromes which threaten their well-being. Technology has many features to support aging wellness enabling older people maintaining healthy and sociable as they grow. However, the needs of the elderly of the population are not always the same. Stealth assessment, coming from the educational domain, can assess such needs.
Panagiotis Antoniou's picture
Panagiotis Antoniou
Aristotle University of Thessaloniki (GR)
Vasiliki Zilidou's picture
Vasiliki Zilidou
Aristotle University of Thessaloniki (GR)
Anastasios Siountas's picture
Anastasios Siountas
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

ST2 - Social data and medical data analytics

Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Athena Vakali's picture
Athena Vakali
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Saturday, June 24, 2017 - 09:00 to 11:00
Mobile Crowdsensing for the Juxtaposition of Realtime Assessments and Retrospective Reporting for Neuropsychiatric Symptoms
Abstract: 
Many symptoms of neuropsychiatric disorders such as tinnitus are subjective and variable over time. Typically, patients are asked to report symptoms, their severity, and duration retrospectively, e.g., in interviews or self-report questionnaires. However, little is known on how well such retrospective reports correspond with the experience of the symptoms at the moment they occurred in daily life. Mobile technologies can help in that end: mobile self-help services allow patients to record their symptoms prospectively, when (or short after) they occur in daily life.
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Thomas Probst's picture
Thomas Probst
Winfried Schlee's picture
Winfried Schlee
Johannes Schobel's picture
Johannes Schobel
Berthold Langguth's picture
Berthold Langguth
Patrick Neff's picture
Patrick Neff
Myra Spiliopoulou's picture
Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)
Manfred Reichert's picture
Manfred Reichert
Mining Facebook data of people with rare diseases
Abstract: 
"This research is concerned with the study of Spanish Facebook pages that deal with rare diseases. The objectives of this research are to characterise these pages and to compare them with the priorities of the Decalogue of the Spanish Federation of Rare Diseases (FEDER). This research uses Netvizz to download the data, word clouds in R to perform text mining, TextBlob in Python to perform sentiment analysis, and log-likelihood in R to compare Facebook and Decalogue words.
Natalia Reguera's picture
Natalia Reguera
Laia Subirats's picture
Laia Subirats
Eurecat & Open University of Catalonia (SP)
Manuel Armayones's picture
Manuel Armayones
OncoCall: analyzing the outcomes of the oncology telephone patient assistance
Abstract: 
Hospital Puerta del Hierro in Madrid, Spain, implemented in November 2011 a new service that aims to aid the patients of the oncology service with their doubts during their treatments through the use of a centralized call center. This service was created with the aim of provide a more personalized patient attention as well as to try to reduce the number of re-entries in the hospital in the emergencies.
Ernestina Menasalvas's picture
Ernestina Menasalvas
Consuelo Gonzalo's picture
Consuelo Gonzalo
Universidad Politécnica de Madrid (SP)
Juan Manuel Tunas's picture
Juan Manuel Tunas
Universidad Politécnica de Madrid
Alejandro Rodriguez Gonzalez's picture
Alejandro Rodriguez Gonzalez
Mariano Provencio's picture
Mariano Provencio
Cristina Gonzalez de Pedro's picture
Cristina Gonzalez de Pedro
Marta Mendez's picture
Marta Mendez
Olga Zaretskaia's picture
Olga Zaretskaia
Juan Luis Cruz's picture
Juan Luis Cruz
Jesus Rey's picture
Jesus Rey
Consuelo Parejo's picture
Consuelo Parejo
Reciprocity and Its Association with Treatment Adherence in an Online Breast Cancer Forum
Abstract: 
Online health communities (OHCs) are increasingly relied upon by individuals exchanging social support for diagnoses and treatment regimens. It has been shown that social support from close relationships (e.g., family and friends) positively influence treatment adherence in offline environments, but much less is known about the online setting.
Zhijun Yin's picture
Zhijun Yin
Vanderbilt University (USA)
Lijun Song's picture
Lijun Song
Bradley Malin's picture
Bradley Malin
Combining Subgroup Discovery and Clustering to Identify Diverse Subpopulations in Cohort Study Data
Abstract: 
Subgroup discovery (SD) exploits its full value in applications where the goal is to generate understandable models. Epidemiologists search for statistically significant relationships between risk factors and outcome in large and heterogeneous datasets encompassing information about the participants' health status gathered from questionnaires, medical examinations and image acquisition. SD algorithms can help epidemiologists by automatically detecting such relationships presented as comprehensible rules, aiming to ultimately improve prevention, diagnosis and treatment of diseases.
Uli Niemann's picture
Uli Niemann
University of Magdeburg (DE)
Myra Spiliopoulou's picture
Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)
Bernhard Preim's picture
Bernhard Preim
Till Ittermann's picture
Till Ittermann
Henry VOlzke's picture
Henry VOlzke
Discovering data source stability patterns in biomedical repositories based on simplicial projections from probability distribution distances
Abstract: 
The degree of homogeneity of statistical distributions among data sources is a critical issue when reusing data of Integrated Data Repositories (IDR). Evaluating this data source stability is of utmost importance in order to ensure a confident data reuse. This work tackles the task of discovering and classifying patterns among the statistical distributions of multiple sources in IDRs, by means of a novel approach based on simplicial projections from probability distribution distances, combined with Density-based spatial clustering of applications with noise (DBSCAN).
Pablo Ferri-Borredà's picture
Pablo Ferri-Borredà
Universitat Politècnica de València (SP)
Carlos Saez's picture
Carlos Saez
Juan Miguel Garcia-Gomez's picture
Juan Miguel Garcia-Gomez
Improving diagnosis in Obstructive Sleep Apnea with clinical data: a Bayesian network approach
Abstract: 
In Obstructive Sleep Apnea, respiratory effort is maintained but ventilation decreases/disappears because of the partial/total occlusion in the upper airway. It affects about 4% of men and 2% of women in the world population. The aim was to define an auxiliary diagnostic method that can support the decision to perform polysomnography (standard test), based on risk and diagnostic factors. Our sample performed polysomnography between January and May of 2015.
Daniela Ferreira-Santos's picture
Daniela Ferreira-Santos
CINTESIS (PT)
Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Disease-Based Clustering of Hospital Admission: Disease Network of Hospital Networks Approach
Abstract: 
To improve the quality of healthcare planning, healthcare systems face challenges in identifying clusters of similar hospitals while considering varying factors. Clustering hospitals based on their admission behavior would be helpful whereas diagnosis of patients is vital in understanding variation in admission. Therefore, grouping hospitals that show similar behavior on their admission distribution while considering similarity among disease symptoms in admission is the objective of our study.
Nouf Albarakati's picture
Nouf Albarakati
Zoran Obradovic's picture
Zoran Obradovic
Temple University (USA)

UNCAP Workshop

Conference room
Session time
Saturday, June 24, 2017 - 09:00 to 12:30
Trilogis SRL
Abstract: 
Beyond UNCAP: A vision for the future
Aristotle University of Thessaloniki & Hololamp
Abstract: 
CAPTAIN: Coach Assistant via Projected and Tangible Interfaces
Evdokimos Konstantinidis's picture
Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)
Nively & Trilogis
Abstract: 
MentorAge
Attikon Hospital and Medical School, University of Athens, Greece
Abstract: 
Cognitive impairment, technology and the need for respecting the individual. A global and Greek perspective
Sokratis G Papageorgiou's picture
Sokratis G Papageorgiou
Ion Beratis's picture
Ion Beratis
Attikon Hospital
LLM Care & ThessAHALL
Abstract: 
LLM Care ecosystem & Thessaloniki Active and Healthy Ageing Living Lab
Evangelia Romanopoulou's picture
Evangelia Romanopoulou
Aristotle University of Thessaloniki (GR)
Institute of Informatics and Telecommunications, NCSR "Demokritos
Abstract: 
The RADIO Ecosystem: unobtrusive clinical monitoring with robots in AAL environments
Vangelis Karkaletsis's picture
Vangelis Karkaletsis
NCSR Demokritos (GR)
Salumedia & Neurofeedback Centre of Northern Greece
Abstract: 
SmokeFreeBrain: Smoking Cessation with mobile technologies and neurofeedback
Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
Stathis Sidiropoulos's picture
Stathis Sidiropoulos
Aristotle University of Thessaloniki (GR)
Bioassist
Abstract: 
The heartaround platform: Providing homecare and assisted living services
Ilias Maglogiannis's picture
Ilias Maglogiannis
University of Piraeus (GR)
ATOS Research & Innovation
Abstract: 
Complex Event Processing in Health Care
Aristotle University of Thessaloniki & Parkinson’s Patient Association of Northern Greece
Abstract: 
i-Prognosis: systems for early detection of Parkinson’s symptoms
Leontios Hadjileontiadis's picture
Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)
Sevasti Bostantjopoulou's picture
Sevasti Bostantjopoulou
Aristotle University of Thessaloniki
SmartCardia SA
Abstract: 
Low-Power Wearable System for Real-Time Screening of Obstructive Sleep Apnea
GiStandards
Abstract: 
UNCAP: Ubiquitous iNteroperable Care for Ageing People
Martin Ford's picture
Martin Ford
-
WAVES partnership and St George’s University of London
Abstract: 
WAVES: “Widening Access to Virtual Educational Scenarios for Care providers”
Sheetal Kavia's picture
Sheetal Kavia
St George's University of London (UK)

GT7 Medical Imaging I

Christos P Loizou's picture
Christos P Loizou
Cyprus University of Technology (CY)
Daniela Giordano's picture
Daniela Giordano
Universita Degli Studi di Catania (IT)
Conference room
Session time
Saturday, June 24, 2017 - 11:30 to 12:30
Illumination correction by dehazing for retinal vessel segmentation
Abstract: 
"Assessment of retinal vessels is fundamental for the diagnosis of many disorders such as heart diseases, diabetes and hypertension. The imaging of retina using advanced fundus camera has become a standard in computer-assisted diagnosis of opthalmic disorders. Modern cameras produce high quality color digital images, but during the acquisition process the light reflected by the retinal surface generates a luminosity and contrast variation.
Benedetta Savelli's picture
Benedetta Savelli
Alessandro Bria's picture
Alessandro Bria
Claudio Marrocco's picture
Claudio Marrocco
Mario Molinara's picture
Mario Molinara
Francesco Tortorella's picture
Francesco Tortorella
University of Cassino and South Lazio (IT)
Adrian Galdran's picture
Adrian Galdran
Aurelio Campilho's picture
Aurelio Campilho
A differential geometry approach for change detection in medical images.
Abstract: 
Change detection is of paramount importance in medical imaging, serving as a non-invasive quantifiable powerful tool in diagnosis and in assessment of the outcome of treatment of tumors. We present a new quantitative method for detecting changes in volumetric medical data and in clustering of anatomical structures, based on assessment of volumetric distortions that are required in order to deform a test three-dimensional medical dataset segment onto its previously-acquired reference, or a given prototype in the case clustering.
Alexander Naitsat's picture
Alexander Naitsat
Technion – Israel Institute of Technology (IL)
Emil Saucan's picture
Emil Saucan
Yehoshua Zeevi's picture
Yehoshua Zeevi
Brain Image and Lesions Registration and 3D Reconstruction in Dicom MRI Images
Abstract: 
During a human brain MRI acquisition the resulting image is formed out of 2D slices. The slices must then be aligned and reconstructed to provide a 3-dimensional (3D) visualization of the brain volume. We propose in this work, an integrated system for the register ion and 3D reconstruction of DICOM MRI images and lesions of the brain acquired from multiple sclerosis (MS) subjects at two different time intervals (time 0 (T0) and time 1 (T1)). The system facilitates the follow up of the MS disease development and will aid the doctor to accurately manage the follow up of the disease.
Christos P Loizou's picture
Christos P Loizou
Cyprus University of Technology (CY)
Christos Papacharalambous's picture
Christos Papacharalambous
Giorgos Samaras's picture
Giorgos Samaras
Efthivoulos Kyriakou's picture
Efthivoulos Kyriakou
Frederick University (CY)
Takis Kasparis's picture
Takis Kasparis
Cyprus University of Technology (CY)
Marios Pantziaris's picture
Marios Pantziaris
Eleni Eracleous's picture
Eleni Eracleous
Constantinos Pattichis's picture
Constantinos Pattichis
University of Cyprus (CY)
An Eye Tracker–based Computer System to Support Oculomotor and Attention Deficit Investigations
Abstract: 
Eye tracking is a non-invasive procedure to acquire eye-gaze data. The accuracy offered by the new eye tracking technologies gives to physicians and scientists a great opportunity to employ eye trackers to perform quantitative assessment of eye movements for diagnostic and rehabilitation purposes. However, eye trackers do not support physicians in their analysis, as they typically lack specific software solutions tailored to the diseases under investigation.
Daniela Giordano's picture
Daniela Giordano
Universita Degli Studi di Catania (IT)
Carmelo Pino's picture
Carmelo Pino
Isaak Kavasidis's picture
Isaak Kavasidis
Concetto Spampinato's picture
Concetto Spampinato
Massimo Di Pietro's picture
Massimo Di Pietro
Renata Rizzo's picture
Renata Rizzo
Anna Scuderi's picture
Anna Scuderi
Rita Barone's picture
Rita Barone

GT8: Ask the Editor

Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)
Conference room
Session time
Saturday, June 24, 2017 - 11:30 to 12:30

Editors-In-Chief of three PubMed journals publishing research on Healthcare Technology present their journals' interests. The audience may interact to obtain guidelines and best recipies for papers to be prepared for acceptance!

 

  • Dimitris Fotiadis
  • Luis Kun
  • Stefanos Triaridis

Keynote Speech

Luis Kun's picture
Luis Kun
Editor in Chief - Springer (USA)
Conference room
Session time
Saturday, June 24, 2017 - 12:30 to 13:15

Most medical treatments are designed for a specific disease and for the “average patient".  This "one-size-fits-all-approach," may be successful for some patients but not for others. Precision medicine, sometimes known as "personalized medicine" is an innovative approach to disease prevention and treatment that takes into account differences in people’s genes, environments and lifestyles.  In January 2015, President Obama launched the Precision Medicine Initiative (PMI), a bold new research effort to revolutionize how we improve health and treat disease, empowering health care providers to tailor treatment and prevention strategies to individuals’ unique characteristics.  Advances in precision medicine have already led to powerful new discoveries and several new FDA-approved treatments that are tailored to specific characteristics of individuals, such as a person’s genetic makeup, or the genetic profile of an individual’s tumor. Patients with a variety of cancers routinely undergo molecular testing as part of patient care, enabling physicians to select treatments that improve chances of survival and reduce exposure to adverse effects.  The PMI seeks to identify genetically-based drivers of disease in order to develop new, more effective treatments.  There is a component in this equation that relates to Public Health that is very seldom discussed or incorporated into an individual’s health record: Geomedicine.  This presentation will discuss how this field will change the future model of Healthcare.

GT9: Games Robotics and smart technologies

Efthyvoulos Kyriacou's picture
Efthyvoulos Kyriacou
Evdokimos Konstantinidis's picture
Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Saturday, June 24, 2017 - 14:45 to 15:45
On supporting Parkinson's Disease patients: The i-PROGNOSIS Personalized Game Suite design approach
Abstract: 
The use of serious games in health care interventions sector has grown rapidly in the last years, however, there is still a gap in the understanding on how these types of interventions are used for the management of the Parkinson Disease (PD), in particular. Targeting intelligent early detection and intervention in PD area, the Personalized Game Suite (PGS) design process approach is presented as part of the H2020 i-PROGNOSIS project that introduces the integration of different serious games in a unified platform (i.e., ExerGames, DietaryGames, EmoGames, and Handwriting/Voice Games).
S. B. Dias's picture
S. B. Dias
Evdokimos Konstantinidis's picture
Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)
J. A. Diniz's picture
J. A. Diniz
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
V Charisis's picture
V Charisis
S Hadjidimitriou's picture
S Hadjidimitriou
M. Stadtschnitzer's picture
M. Stadtschnitzer
P. Fagerberg's picture
P. Fagerberg
Ioannis Ioakimidis's picture
Ioannis Ioakimidis
K Dimitropoulos's picture
K Dimitropoulos
N Grammalidis's picture
N Grammalidis
Leontios Hadjileontiadis's picture
Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)
Carotid Bifurcation Plaque Stability Estimation based on Motion Analysis
Abstract: 
"Through this study we are presenting the initial steps towards a real time motion analysis system to predict the stability of carotid bifurcation plaques. The analysis is performed on B-mode video loops. Loops are analyzed in order to follow systoly and diastoly sections of the cardiac cycle and trace the motion of plaques during these periods. We had created a system that applies Farneback’s optical flow estimation method in order to estimate the flow between consecutive frames or frames at a predefined interval.
Efthyvoulos Kyriacou's picture
Efthyvoulos Kyriacou
Andrew Nicolaides's picture
Andrew Nicolaides
Alexandra Constantinou's picture
Alexandra Constantinou
Maura Griffin's picture
Maura Griffin
Christos P Loizou's picture
Christos P Loizou
Cyprus University of Technology (CY)
Marios S. Pattichis's picture
Marios S. Pattichis
Hamed Nasrabadi's picture
Hamed Nasrabadi
Constantinos Pattichis's picture
Constantinos Pattichis
University of Cyprus (CY)
A Versatile Architecture for Building IoT Quantified-Self Applications
Abstract: 
The abundance of activity trackers and biosignal sensors as well as the evolution of IoT and communication technologies have considerably advanced the concept of Quantified-Self. Nowadays there are several frameworks and applications that realize the concept, focusing though strictly on specific areas, from daily use to professional activities such as sport and healthcare. This work proposes a versatile, cross-domain solution for building quantified-self applications exploiting the capacities for open-design, modularity and extensibility of the AGILE IoT gateway.
Charalampos Doukas's picture
Charalampos Doukas
Panayiotis Tsanakas's picture
Panayiotis Tsanakas
Ilias Maglogiannis's picture
Ilias Maglogiannis
University of Piraeus (GR)
Commercial BCI Control And Functional Brain Networks in Spinal Cord Injury: A Proof-of-Concept.
Abstract: 
Spinal Cord Injury (SCI), along with disability, results in changes of brain organization and structure. While sensorimotor networks of patients and healthy individuals share similar patterns, unique functional interactions have been identified in SCI networks. Brain-Computer Interfaces (BCIs) have emerged as a promising technology for movement restoration and rehabilitation of SCI patients. We describe an experimental methodology to combine high-resolution electroencephalography (EEG) for investigation of functional connectivity following SCI and non-invasive BCI control of robotic arms.
Alkinoos Athanasiou's picture
Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)
George Arfaras's picture
George Arfaras
Ioannis Xygonakis's picture
Ioannis Xygonakis
Panagiotis Kartsidis's picture
Panagiotis Kartsidis
Aristotle University of Thessaloniki (GR)
Niki Pandria's picture
Niki Pandria
Aristotle University of Thessaloniki (GR)
Kyriaki Rafailia Kavazidi's picture
Kyriaki Rafailia Kavazidi
Lab of Medical Physics The Medical School Aristotle University of Thessaloniki
Alexander Astaras's picture
Alexander Astaras
American College of Thessaloniki (GR)
Nicolas Foroglou's picture
Nicolas Foroglou
Konstantinos Polyzoidis's picture
Konstantinos Polyzoidis
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

GT10 Medical Imaging II

Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Ilias Maglogiannis's picture
Ilias Maglogiannis
University of Piraeus (GR)
Conference room
Session time
Saturday, June 24, 2017 - 14:45 to 15:45
A comparative study of cell nuclei attributed relational graphs for knowledge description and categorization in histopathological gastric cancer whole slide images
Abstract: 
In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations, followed by hierarchical ensemble learning and classification.
Harshita Sharma's picture
Harshita Sharma
Technical University Berlin (DE)
Norman Zerbe's picture
Norman Zerbe
Christine BOger's picture
Christine BOger
Stephan Wienert's picture
Stephan Wienert
Olaf Hellwich's picture
Olaf Hellwich
Peter Hufnagl's picture
Peter Hufnagl
Non-Invasive Assessment of Coronary Stenoses and Comparison to Invasive Techniques: a proof-of-concept study
Abstract: 
Coronary Computed Tomography Angiography (CCTA) has gained substantial ground in everyday clinical practice due to its non-invasive nature. In this work we present a noninvasive method to assess the hemodynamic significance of coronary stenoses using only CCTA images. Two female patients were subjected to Invasive Coronary Angiography, Virtual Histology IVUS and CCTA. The same arterial segment was reconstructed in 3D using the proposed method as well as two already validated 3D reconstruction methods using the aforementioned invasive techniques.
Panagiota Tsompou's picture
Panagiota Tsompou
Panagiotis Siogkas's picture
Panagiotis Siogkas
University of Ioannina (GR)
Antonis Sakellarios's picture
Antonis Sakellarios
Pedro Lemos's picture
Pedro Lemos
Lampros Michalis's picture
Lampros Michalis
Dimitris Fotiadis's picture
Dimitris Fotiadis
University of Ioannina (GR)
Automated collagen proportional area extraction in liver biopsy images using a novel classification via clustering algorithm
Abstract: 
Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity. In current work a novel modification of K-means clustering is proposed for image segmentation of liver biopsies. More specifically, supervised restriction of centroids movement is utilized.
Dimosthenis C. Tsouros's picture
Dimosthenis C. Tsouros
Panagiotis N. Smyrlis's picture
Panagiotis N. Smyrlis
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Pinelopi Manousou's picture
Pinelopi Manousou
Dimitrios G. Tsalikakis's picture
Dimitrios G. Tsalikakis
Markos G. Tsipouras's picture
Markos G. Tsipouras
University of Western Macedonia
A Deconstructed Replication of Time of Test Using the AGIS Metric (Skyline paper)
Abstract: 
In medical practice, glaucoma severity is usually measured using the Advanced Glaucoma Intervention Studies (AGIS) metric. In a previous study, we replicated the work of Montolio et al.,and demonstrated that, for a larger dataset, time of day of test using the AGIS metric did make a difference to the measurement of glaucoma, supporting Montolio et al’s work. However, in our earlier study, we used the AGIS scores for both eyes combined. In this paper, we use the measurement from just one eye at a time.
Steve Counsell's picture
Steve Counsell
Stephen Swift's picture
Stephen Swift
Brunel University (UK)
Allan Tucker's picture
Allan Tucker
Brunel University London (UK)