Lithium-induced EEG changes in patients with affective disorders

In 12 patients with affective disorders (ICD-10: F31, F32, F33), EEGs were recorded before and after 4.4 months of lithium treatment. Effects of lithium on the EEG were analyzed by power spectral analysis controlled for vigilance. We found (1) an increase in relative power in both delta and theta band which was related to the lithium plasma level, (2) a decrease in relative alpha power especially at occipital leads and (3) a reduction of the dominant alpha frequency. The changes in relative power were more pronounced in the right hemisphere, which is in contrast to the hypothesis of a site-specific localization of lithium effects only in left anterior regions. Copyright (C) 2000 S. Karger AG,Basel

Design and implementation of prototype tour guide application for mountainous and provincial areas: The case of Paramythia

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Περιβάλλον και Ανάπτυξη" 2η Κατεύθυνση Σπουδών "Περιβάλλον και Ανάπτυξη των Ορεινών Περιοχών

A Comparative Study of Bluetooth SPP, PAN and GOEP for Efficient Exchange of Healthcare Data

Objectives: Current research aims to address the challenges of exchanging healthcare information, since when this information has to be shared, this happens by specifically designed medical applications or even by the patients themselves. Among the problems that the Health Information Exchange (HIE) initiative is facing are that (i) third party health data cannot be accessed without internet, (ii) there exist crucial delays in accessing citizens’ data, (iii) the direct HIE can only happen among Healthcare Institutions. Methods: Towards the solution of these issues, a Device-to-Device (D2D) protocol has been specified, running on top of the Bluetooth protocol for efficient data exchange. This research is focused on this D2D protocol, by comparing the different Bluetooth profiles that can be used for transmitting this data, based on specific metrics considering the probabilities of transferring erroneous data. Findings: An evaluation of three Bluetooth profiles takes place, concluding that two of the three profiles must be used to respect the D2D protocol nature and be fully supported by the main market vendors’ operating systems. Novelty:Based on this evaluation, the specified D2D protocol has been built on top of state-of-the-art short-range distance communication technologies, fully supporting the healthcare ecosystem towards the HIE paradigm. Doi: 10.28991/esj-2021-01276 Full Text: PD

Facial expressions and personality: A kinematical investigation during an emotion induction experiment

Background/Aims: In order to elucidate the relationship between personality traits and expression of positive emotions in healthy volunteers, standardized personality inventories and kinematical analysis of facial expressions can be helpful and were applied in the present study. Methods: Markers fixed at distinct points of the face emitting ultrasonic signals at high frequency gave a direct measure of facial movements with high spatial-temporal resolution. Forty-six healthy participants (mean age: 40.7 years; 20 males, 26 females) watching a witty movie ('Mr. Bean') were investigated. Results: Speed of `laughing' was associated with higher scores on Zuckerman's Sensation Seeking Scale and NEO-FFI (Openness to Experience). Conclusion: Kinematical analysis of facial expressions seems to reflect sensation seeking and related personality styles. Higher speed of facial movements in sensation seekers suggests lowered serotonergic function. Copyright (c) 2006 S. Karger AG, Basel

5G & SLAs: Automated proposition and management of agreements towards QoS enforcement

Efficient Service Level Agreements (SLA) management and anticipation of Service Level Objectives (SLO) breaches become mandatory to guarantee the required service quality in software- defined and 5G networks. To create an operational Network Service, it is highly envisaged to associate it with their network-related parameters that reflect the corresponding quality levels. These are included in policies but while SLAs target usually business users, there is a challenge for mechanisms that bridge this abstraction gap. In this paper, a generic black box approach is used to map high-level requirements expressed by users in SLAs to low-level network parameters included in policies, enabling Quality of Service (QoS) enforcement by triggering the required policies and manage the infrastructure accordingly. In addition, a mechanism for determining the importance of different QoS parameters is presented, mainly used for “relevant” QoS metrics recommendation in the SLA template

A Comparative Study of Collaborative Filtering in Product Recommendation

Product recommendation is considered a well-known technique for bringing customers and products together. With applications in music, electronic shops, or almost any platform the user daily deals with, the recommendation system’s sole scope is to help customers and attract new ones to discover new products. Through product recommendation, transaction costs can also be decreased, improving overall decision-making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor in choosing the appropriate recommendation method and drawing specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithms’ metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of this transactional data, concluding that LightFM provides more efficient recommendation results following the evaluation’s precision and recall outcomes. Nevertheless, even though the Surprise library outperforms, it should be used when constructing user-friendly models, requiring low code and low technicalities. Doi: 10.28991/ESJ-2023-07-01-01 Full Text: PD

Batch and Streaming Data Ingestion towards Creating Holistic Health Records

The healthcare sector has been moving toward Electronic Health Record (EHR) systems that produce enormous amounts of healthcare data due to the increased emphasis on getting the appropriate information to the right person, wherever they are, at any time. This highlights the need for a holistic approach to ingest, exploit, and manage these huge amounts of data for achieving better health management and promotion in general. This manuscript proposes such an approach, providing a mechanism allowing all health ecosystem entities to obtain actionable knowledge from heterogeneous data in a multimodal way. The mechanism includes diverse techniques for automatically ingesting healthcare-related information from heterogeneous sources that produce batch/streaming data, managing, fusing, and aggregating this data into new data structures (i.e., Holistic Health Records (HHRs)). The latter enable the aggregation of data coming from different sources, such as Internet of Medical Things (IoMT) devices, online/offline platforms, while to effectively construct the HHRs, the mechanism develops various data management techniques covering the overall data path, from data acquisition and cleaning to data integration, modelling, and interpretation. The mechanism has been evaluated upon different healthcare scenarios, ranging from hospital-retrieved data to patient platforms, combined with data obtained from IoMT devices, having produced useful insights towards its successful and wide adaptation in this domain. In order to implement a paradigm shift from heterogeneous and independent data sources, limited data exploitation, and health records, the mechanism has combined multidisciplinary technologies toward HHRs. Doi: 10.28991/ESJ-2023-07-02-03 Full Text: PD

Kinematical analysis of emotionally induced facial expressions in patients with obsessive–compulsive disorder

Background: Motor function is deficient in many patients with obsessive–compulsive disorder (OCD), especially in the face. To investigate subtle motor dysfunction, kinematical analysis of emotional facial expressions can be used. Our aim was to investigate facial movements in response to humorous film stimuli in OCD patients.; Method: Kinematical analysis of facial movements was performed. Ultrasound markers at defined points of the face provided exact measurement of facial movements, while subjects watched a humorous movie (‘Mr Bean’). Thirty-four OCD patients (19 male, 15 female; mean (S.D.) age: 35·8 (11·5) years; mean (S.D.) total Y-BOCS score: 25·5 (5·9)) were studied in unmedicated state and after a 10-week treatment with the SSRI sertraline. Thirty-four healthy controls (19 male, 15 female; mean (S.D.) age: 37·5 (13·1) years) were also investigated.; Results: At baseline, OCD patients showed significantly slower velocity at the beginning of laughing than healthy controls and a reduced laughing frequency. There was a significant negative correlation between laughing frequency and severity of OCD symptoms. Ten weeks later a significant increase of laughing frequency and initial velocity during laughing was found.; Conclusions: Execution of adequate facial reactions to humour is abnormally slow in OCD patients. Susceptibility of OCD patients with regard to emotional stimuli is less pronounced than in healthy subjects. This phenomenon is closely correlated to OCD symptoms and is state-dependent.Peer Reviewe

Internet of Medical Things (IoMT): Acquiring and Transforming Data into HL7 FHIR through 5G Network Slicing

The Healthcare 4.0 era is surrounded by challenges varying from the Internet of Medical Things (IoMT) devices’ data collection, integration and interpretation. Several techniques have been developed that however do not propose solutions that can be applied to different scenarios or domains. When dealing with healthcare data, based on the severity and the application of their results, they should be provided almost in real-time, without any errors, inconsistencies or misunderstandings. Henceforth, in this manuscript a platform is proposed for efficiently managing healthcare data, by taking advantage of the latest techniques in Data Acquisition, 5G Network Slicing and Data Interoperability. In this platform, IoMT devices’ data and network specifications can be acquired and segmented in different 5G network slices according to the severity and the computation requirements of different medical scenarios. In sequel, transformations are performed on the data of each network slice to address data heterogeneity issues, and provide the data of the same network slices into HL7 FHIR-compliant format, for further analysis