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414 results on '"KOUTSOURIS, Dimitrios"'

1. Towards Model-driven Enhancement of Safety in Healthcare Robot Interactions

Author

Koutsopoulos, Georgios, Ioannidou, Penelope, Matsopoulos, George K., Koutsouris, Dimitrios D., van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, Řepa, Václav, editor, Matulevičius, Raimundas, editor, and Laurenzi, Emanuele, editor

Published
2024
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2. CEC-CNN: A Consecutive Expansion-Contraction Convolutional Network for Very Small Resolution Medical Image Classification

Author

Vezakis, Ioannis, Vezakis, Antonios, Gourtsoyianni, Sofia, Koutoulidis, Vassilis, Matsopoulos, George K., and Koutsouris, Dimitrios

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Deep Convolutional Neural Networks (CNNs) for image classification successively alternate convolutions and downsampling operations, such as pooling layers or strided convolutions, resulting in lower resolution features the deeper the network gets. These downsampling operations save computational resources and provide some translational invariance as well as a bigger receptive field at the next layers. However, an inherent side-effect of this is that high-level features, produced at the deep end of the network, are always captured in low resolution feature maps. The inverse is also true, as shallow layers always contain small scale features. In biomedical image analysis engineers are often tasked with classifying very small image patches which carry only a limited amount of information. By their nature, these patches may not even contain objects, with the classification depending instead on the detection of subtle underlying patterns with an unknown scale in the image's texture. In these cases every bit of information is valuable; thus, it is important to extract the maximum number of informative features possible. Driven by these considerations, we introduce a new CNN architecture which preserves multi-scale features from deep, intermediate, and shallow layers by utilizing skip connections along with consecutive contractions and expansions of the feature maps. Using a dataset of very low resolution patches from Pancreatic Ductal Adenocarcinoma (PDAC) CT scans we demonstrate that our network can outperform current state of the art models., Comment: 5 pages, 3 figures

Published
2022

4. The SMART BEAR Project: An Overview of Its Infrastructure

Author

Su, Qiqi, Peretokin, Vadim, Basdekis, Ioannis, Kouris, Ioannis, Maggesi, Jonatan, Sicuranza, Mario, Acebes, Alberto, Bucur, Anca, Mukkala, Vinod Jaswanth Roy, Pozdniakov, Konstantin, Kloukinas, Christos, Koutsouris, Dimitrios D., Iliadou, Elefteria, Leontsinis, Ioannis, Gallo, Luigi, De Pietro, Giuseppe, Spanoudakis, George, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Maciaszek, Leszek A., editor, Mulvenna, Maurice D., editor, and Ziefle, Martina, editor

Published
2023
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5. Sparsely Activated Networks

Author

Bizopoulos, Paschalis and Koutsouris, Dimitrios

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

Previous literature on unsupervised learning focused on designing structural priors with the aim of learning meaningful features. However, this was done without considering the description length of the learned representations which is a direct and unbiased measure of the model complexity. In this paper, first we introduce the $\varphi$ metric that evaluates unsupervised models based on their reconstruction accuracy and the degree of compression of their internal representations. We then present and define two activation functions (Identity, ReLU) as base of reference and three sparse activation functions (top-k absolutes, Extrema-Pool indices, Extrema) as candidate structures that minimize the previously defined $\varphi$. We lastly present Sparsely Activated Networks (SANs) that consist of kernels with shared weights that, during encoding, are convolved with the input and then passed through a sparse activation function. During decoding, the same weights are convolved with the sparse activation map and subsequently the partial reconstructions from each weight are summed to reconstruct the input. We compare SANs using the five previously defined activation functions on a variety of datasets (Physionet, UCI-epilepsy, MNIST, FMNIST) and show that models that are selected using $\varphi$ have small description representation length and consist of interpretable kernels., Comment: 10 pages, 5 figures, 4 algorithms, 4 tables, submission to IEEE Transactions on Neural Networks and Learning Systems

Published
2019
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6. Signal2Image Modules in Deep Neural Networks for EEG Classification

Author

Bizopoulos, Paschalis, Lambrou, George I, and Koutsouris, Dimitrios

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such as graphical processing units. The vast majority of deep learning research is conducted using images as training data, however the biomedical domain is rich in physiological signals that are used for diagnosis and prediction problems. It is still an open research question how to best utilize signals to train deep neural networks. In this paper we define the term Signal2Image (S2Is) as trainable or non-trainable prefix modules that convert signals, such as Electroencephalography (EEG), to image-like representations making them suitable for training image-based deep neural networks defined as `base models'. We compare the accuracy and time performance of four S2Is (`signal as image', spectrogram, one and two layer Convolutional Neural Networks (CNNs)) combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet) along with the depth-wise and 1D variations of the latter. We also provide empirical evidence that the one layer CNN S2I performs better in eleven out of fifteen tested models than non-trainable S2Is for classifying EEG signals and we present visual comparisons of the outputs of the S2Is., Comment: 4 pages, 2 figures, 1 table, EMBC 2019

Published
2019
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7. Deep Learning in Cardiology

Author

Bizopoulos, Paschalis and Koutsouris, Dimitrios

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use., Comment: 27 pages, 2 figures, 10 tables

Published
2019
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8. Diagnostic accuracy and usability of the EMBalance decision support system for vestibular disorders in primary care: proof of concept randomised controlled study results

Author

Bamiou, Doris-Eva, Kikidis, Dimitris, Bibas, Thanos, Koohi, Nehzat, Macdonald, Nora, Maurer, Christoph, Wuyts, Floris L., Ihtijarevic, Berina, Celis, Laura, Mucci, Viviana, Maes, Leen, Van Rompaey, Vincent, Van de Heyning, Paul, Nazareth, Irwin, Exarchos, Themis P., Fotiadis, Dimitrios, Koutsouris, Dimitrios, and Luxon, Linda M.

Published
2022
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9. Using Association Rules in Antimicrobial Resistance in Stone Disease Patients

Author

Manolitsis, Ioannis, primary, Feretzakis, Georgios, additional, Tzelves, Lazaros, additional, Kalles, Dimitris, additional, Loupelis, Evangelos, additional, Katsimperis, Stamatis, additional, Kosmidis, Thanos, additional, Anastasiou, Athanasios, additional, Koutsouris, Dimitrios, additional, Kofopoulou, Stavroula, additional, Verykios, Vassilios S., additional, Skolarikos, Andreas, additional, and Varkarakis, Ioannis, additional

Published
2022
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10. Cluster Analysis Assessment in Proposing a Surgical Technique for Benign Prostatic Enlargement

Author

Tzelves, Lazaros, primary, Feretzakis, Georgios, additional, Kalles, Dimitris, additional, Manolitsis, Ioannis, additional, Katsimperis, Stamatis, additional, Bellos, Themistoklis, additional, Berdempes, Marinos, additional, Anastasiou, Athanasios, additional, Koutsouris, Dimitrios, additional, Kofopoulou, Stavroula, additional, Verykios, Vassilios S., additional, Skolarikos, Andreas, additional, and Varkarakis, Ioannis, additional

Published
2022
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11. A Risk Stratification Model for Early Cognitive Impairment After Diagnosis of Parkinson’s Disease

Author

Tsiouris, Kostas M., Konitsiotis, Spiros, Koutsouris, Dimitrios D., Fotiadis, Dimitrios I., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Henriques, Jorge, editor, Neves, Nuno, editor, and de Carvalho, Paulo, editor

Published
2020
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12. 3-D Registration on Carotid Artery imaging data: MRI for different timesteps

Author

Bizopoulos, Paschalis A., Sakellarios, Antonis, Michalis, Lampros K., Koutsouris, Dimitrios D., and Fotiadis, Dimitrios I.

Subjects
Physics - Medical Physics
Abstract

A common problem which is faced by the researchers when dealing with arterial carotid imaging data is the registration of the geometrical structures between different imaging modalities or different timesteps. The use of the "Patient Position" DICOM field is not adequate to achieve accurate results due to the fact that the carotid artery is a relatively small structure and even imperceptible changes in patient position and/or direction make it difficult. While there is a wide range of simple/advanced registration techniques in the literature, there is a considerable number of studies which address the geometrical structure of the carotid artery without using any registration technique. On the other hand the existence of various registration techniques prohibits an objective comparison of the results using different registration techniques. In this paper we present a method for estimating the statistical significance that the choice of the registration technique has on the carotid geometry. One-Way Analysis of Variance(ANOVA) showed that the p-values were <0.0001 for the distances of the lumen from the centerline for both right and left carotids of the patient case that was studied., Comment: 4 pages, 4 figures, 1 table, preprint submitted to IEEE-EMBC 2016

Published
2016
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13. Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics.

Author

Giannakopoulou, Olympia, Kakkos, Ioannis, Dimitrakopoulos, Georgios N., Tarousi, Marilena, Sun, Yu, Bezerianos, Anastasios, Koutsouris, Dimitrios D., and Matsopoulos, George K.

Subjects
MENTAL fatigue, LARGE-scale brain networks, REGRESSION analysis, MUSCLE fatigue, MOBILE apps, ROAD safety measures
Abstract

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Design and Development of a Low-Budget Infrasonic Detector

Author

Pitsaris, Marios, Katrakazas, Panagiotis, Koutsouris, Dimitrios-Dionysios, Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Eskola, Hannu, editor, Väisänen, Outi, editor, Viik, Jari, editor, and Hyttinen, Jari, editor

Published
2018
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19. List of contributors

Author

Andrés, Jon Bilbatua, primary, Bader, Farah, additional, Berrahal, Sarra, additional, Bhatt, Chintan, additional, Bhavani, N.P.G., additional, Boudriga, Noureddine, additional, Cao, Su-Qun, additional, Cummins, Nicholas, additional, Dumka, Ankur, additional, Durgadevi, G., additional, Fdez-Arroyabe, Pablo, additional, Fernández, Diego Soliño, additional, Fysarakis, Konstantinos, additional, Gehlot, Anita, additional, Hari, Rajeswari, additional, Ioannidis, Sotiris, additional, Jagtap, Sandeep, additional, Jariwala, Vivaksha J., additional, Jinwala, Devesh C., additional, Karthikeyan, V., additional, Khilrani, Jatin Kumar, additional, Kokel, Vidhi, additional, Kothari, Pooja, additional, Koutsouris, Dimitrios, additional, Kumar, K. Senthil, additional, Liyanage, S.R., additional, Lourakis, Manolis, additional, Loutsetis, Evangelos, additional, Maniadakis, Michail, additional, Masrani, Shweta, additional, Mittal, Mamta, additional, Pateraki, Maria, additional, Ponmagal, R.S., additional, Sah, Anushree, additional, Sakkalis, Vangelis, additional, Schuller, Björn, additional, Singh, Rajesh, additional, Spanoudakis, Georgios, additional, Srividhya, V., additional, Sujatha, K., additional, and Varlamis, Iraklis, additional

Published
2020
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20. Biosensors and Internet of Things in smart healthcare applications: challenges and opportunities

Author

Pateraki, Maria, primary, Fysarakis, Konstantinos, additional, Sakkalis, Vangelis, additional, Spanoudakis, Georgios, additional, Varlamis, Iraklis, additional, Maniadakis, Michail, additional, Lourakis, Manolis, additional, Ioannidis, Sotiris, additional, Cummins, Nicholas, additional, Schuller, Björn, additional, Loutsetis, Evangelos, additional, and Koutsouris, Dimitrios, additional

Published
2020
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21. Smart Adaptable System for Older Adults’ Daily Life Activities Management

Author

Giokas, Kostas, Anastasiou, Athanasios, Koutsouris, Dimitrios, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin Sherman, Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert Y., Series editor, Giokas, Kostas, editor, Bokor, Laszlo, editor, and Hopfgartner, Frank, editor

Published
2017
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25. Cloud-Based Information System for Blood Donation

Author

Fotopoulos, Ilias, Palaiologou, Revekka, Kouris, Ioannis, Koutsouris, Dimitrios, Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Kyriacou, Efthyvoulos, editor, Christofides, Stelios, editor, and Pattichis, Constantinos S., editor

Published
2016
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26. Otosclerosis and Tympanosclerosis Modeling Using the Finite Element Method

Author

Plakas, Konstantinos, Katrakazas, Panagiotis, Giokas, K., Kouris, I., Bibas, Athanasios, Koutsouris, Dimitrios-Dionysios, Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Kyriacou, Efthyvoulos, editor, Christofides, Stelios, editor, and Pattichis, Constantinos S., editor

Published
2016
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27. Complex Dynamics in Tumor Gene Regulatory Networks: Oncogenesis Dynamics Driven by 'Genes Gone Crazy'

Author

Lambrou, George I., Braoudaki, Maria, Katrakazas, Panagiotis, Kouris, Ioannis, Iliopoulou, Dimitra, Koutsouri, Tzortzia, Petropoulou, Ourania, Koutsouris, Dimitrios-Dionysios, Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Kyriacou, Efthyvoulos, editor, Christofides, Stelios, editor, and Pattichis, Constantinos S., editor

Published
2016
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28. A Preliminary Study on In-Vivo 3-D Imaging of Bioprosthetic Aortic Valve Deformation

Author

Bizopoulos, Paschalis A., Vavuranakis, Manolis, Papaioannou, Theodoros G., Vrachatis, Dimitrios A., Sakellarios, Antonis I., Iliopoulou, Dimitra, Tousoulis, Dimitris, Koutsouris, Dimitrios D., Fotiadis, Dimitrios I., Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Kyriacou, Efthyvoulos, editor, Christofides, Stelios, editor, and Pattichis, Constantinos S., editor

Published
2016
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29. Performing Intraoperative Computer Assisted Risk Analysis for Oncologic Liver Surgery in Clinical Practice

Author

Zygomalas, Apollon, Karavias, Dionissios, Koutsouris, Dimitrios, Maroulis, Ioannis, Karavias, Dimitrios D., Giokas, Konstantinos, Megalooikonomou, Vasileios, Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Kyriacou, Efthyvoulos, editor, Christofides, Stelios, editor, and Pattichis, Constantinos S., editor

Published
2016
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30. Σkynet: A Novel Biologically Inspired Near Extinction Reconstruction Model

Author

Lambrou, George I., Katrakazas, Panagiotis, Iliopoulou, Dimitra, Kouris, Ioannis, Giokas, Kostas, Petropoulou, Ourania, Koutsouris, Dimitrios-Dionysios, Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Kyriacou, Efthyvoulos, editor, Christofides, Stelios, editor, and Pattichis, Constantinos S., editor

Published
2016
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32. Time-Dependent Adaptations of Brain Networks in Driving Fatigue †.

Author

Giannakopoulou, Olympia, Kakkos, Ioannis, Dimitrakopoulos, Georgios N., Sun, Yu, Matsopoulos, George K., and Koutsouris, Dimitrios D.

Subjects
LARGE-scale brain networks, FATIGUE (Physiology), AUTOMOBILE driving, TRAFFIC accidents, ELECTROENCEPHALOGRAPHY
Abstract

Driving with fatigue is a major contributor to traffic accidents and is closely linked to central nervous system functions. To investigate the evolution of brain dynamics during simulated driving under different EEG rhythms, we conducted an experiment in which participants performed a 1 h driving task while their EEG signals were recorded. We used the complex network theory to analyze data derived from the driving stimulation and found that as fatigue deepened, small-world metrics, namely the path lengths, clustering coefficients, and measures of efficiency (global, local, nodal), showed alterations against the driving time. Additionally, a major correlation (corr = 0.98) was observed between the cluster coefficient with local efficiency in all frequency bands (theta, alpha, beta). Our findings suggest that driving fatigue can cause significant trends in brain network characteristics, such as path length (m = −103 to −93), (m = 98) for specific rhythms (beta, alpha, theta band, respectively) and their related brain functions, which could serve as objective indicators when evaluating the fatigue level and in the future, preventing driving fatigue and its consequences. [ABSTRACT FROM AUTHOR]

Published
2023
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35. Information Extraction, Matching and Reporting for Medical Devices Vigilance

Author

Anastasiou, Athanasios, Bliznakov, Zhivko B., Deligiannakis, Antonios, Giatrakos, Nikos, Haritou, Maria, Ioannou, Ekaterini, Koutsouris, Dimitrios, Malataras, Panagiotis G., Stavrianou, Kallirroi, Tsirmpas, Charalampos, Pallikarakis, Nicolas E., MAGJAREVIC, Ratko, Editor-in-chief, Ładyzynsk, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, and Vasic, Darko, editor

Published
2015
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37. Semantic Interoperability Between Clinical Research and Healthcare: The PONTE Approach

Author

Tagaris, Anastasios, Chondrogiannis, Efthymios, Andronikou, Vassiliki, Tsatsaronis, George, Mourtzoukos, Konstantinos, Roumier, Joseph, Matskanis, Nikolaos, Schroeder, Michael, Massonet, Philippe, Koutsouris, Dimitrios, Varvarigou, Theodora, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Simperl, Elena, editor, Norton, Barry, editor, Mladenic, Dunja, editor, Della Valle, Emanuele, editor, Fundulaki, Irini, editor, Passant, Alexandre, editor, and Troncy, Raphaël, editor

Published
2015
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40. An Adaptive Home Environment Supporting People with Balance Disorders

Author

Bakola, Iliana, Bellos, Christos, Tripoliti, Evanthia E., Bibas, Athanasios, Koutsouris, Dimitrios, Fotiadis, Dimitrios I., Magjarevic, Ratko, Editor-in-chief, Ładyzynsk, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, and Roa Romero, Laura M., editor

Published
2014
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43. Early Diagnosis of Alzheimer’s Type Dementia Using Continuous Speech Recognition

Author

Baldas, Vassilis, Lampiris, Charalampos, Capsalis, Christos, Koutsouris, Dimitrios, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Coulson, Geoffrey, Series editor, Lin, James C., editor, and Nikita, Konstantina S., editor

Published
2011
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44. Measuring the impact of anonymization on real-world consolidated health datasets engineered for secondary research use: Experiments in the context of MODELHealth project

Author

Pitoglou, Stavros, Filntisi, Arianna, Anastasiou, Athanasios, Matsopoulos, George K., and Koutsouris, Dimitrios

Subjects
General Medicine
Abstract

IntroductionElectronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research.ObjectivesThis study aims to present the main elements of the MODELHealth project implementation and the evaluation method that was followed to assess the efficiency of its mechanism.MethodsThe MODELHealth project was implemented as an Extract-Transform-Load system that collects data from the hospital databases, performs harmonization to the HL7 FHIR standard and anonymization using the k-anonymity method, before loading the transformed data to a central repository. The integrity of the anonymization process was validated by developing a database query tool. The information loss occurring due to the anonymization was estimated with the metrics of generalized information loss, discernibility and average equivalence class size for various values of k.ResultsThe average values of generalized information loss, discernibility and average equivalence class size obtained across all tested datasets and k values were 0.008473 ± 0.006216252886, 115,145,464.3 ± 79,724,196.11 and 12.1346 ± 6.76096647, correspondingly. The values of those metrics appear correlated with factors such as the k value and the dataset characteristics, as expected.ConclusionThe experimental results of the study demonstrate that it is feasible to perform effective harmonization and anonymization on EHR data while preserving essential patient information.

Published
2022

47. Telematics Healthcare Through Digital Terrestrial Television Networks: Applications and Perspectives

Author

Koutsouris Dimitrios, I. Lambrou George, and Kardaras Konstantinos

Subjects
Control and Optimization, Computer Networks and Communications, business.industry, Computer science, Health care, Telematics, Electrical and Electronic Engineering, Telecommunications, business, Digital terrestrial television, Computer Science Applications
Abstract

Background: The trend nowadays is the interconnection of rural health centers with a specialized monitoring medical center. Aim: The paper investigates the qualitative and quantitative characteristics that degrade the perceived video picture quality for a variety of short MPEG-4 video clips. Methods: In the present work our approach was considered in three branches; first the customization of video quality in terms of video and data compression, so maximum quality is achieved, second the use of the optimized video data transfer for emergency situations and third the subsequent creation of a “virtual doctor”. Results: Further on, novel architectures will be suggested in order to achieve the creation of a broadcasting network for healthcare telematics. Remote medical supervision and healthcare treatment will be provided to remote patients in rural health centers and understaffed areas that lack telemetry infrastructures and medical expertise personnel. Conclusions: The proposed architecture, along with the optimized data transmission, demonstrates the integration of present and forthcoming telecommunication network technologies enabling remote interactivity for fixed, portable and mobile users.

Published
2021

50. Development of Machine-Learning Models for Tinnitus-Related Distress Classification Using Wavelet-Transformed Auditory Evoked Potential Signals and Clinical Data.

Author

Manta, Ourania, Sarafidis, Michail, Schlee, Winfried, Mazurek, Birgit, Matsopoulos, George K., and Koutsouris, Dimitrios D.

Subjects
AUDITORY evoked response, MACHINE learning, FISHER discriminant analysis, SUPPORT vector machines, STIMULUS & response (Psychology)
Abstract

Tinnitus is a highly prevalent condition, affecting more than 1 in 7 adults in the EU and causing negative effects on sufferers' quality of life. In this study, we utilised data collected within the "UNITI" project, the largest EU tinnitus-related research programme. Initially, we extracted characteristics from both auditory brainstem response (ABR) and auditory middle latency response (AMLR) signals, which were derived from tinnitus patients. We then combined these features with the patients' clinical data, and integrated them to build machine learning models for the classification of individuals and their ears according to their level of tinnitus-related distress. Several models were developed and tested on different datasets to determine the most relevant features and achieve high performances. Specifically, seven widely used classifiers were utilised on all generated datasets: random forest (RF), linear, radial, and polynomial support vector machines (SVM), naive bayes (NB), neural networks (NN), and linear discriminant analysis (LDA). Results showed that features extracted from the wavelet-scattering transformed AMLR signals were the most informative data. In combination with the 15 LASSO-selected clinical features, the SVM classifier achieved optimal performance with an AUC value, sensitivity, and specificity of 92.53%, 84.84%, and 83.04%, respectively, indicating high discrimination performance between the two groups. [ABSTRACT FROM AUTHOR]

Published
2023
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