Alcohol affects the brain's resting-state network in social drinkers.

Acute alcohol intake is known to enhance inhibition through facilitation of GABA(A) receptors, which are present in 40% of the synapses all over the brain. Evidence suggests that enhanced GABAergic transmission leads to increased large-scale brain connectivity. Our hypothesis is that acute alcohol intake would increase the functional connectivity of the human brain resting-state network (RSN). To test our hypothesis, electroencephalographic (EEG) measurements were recorded from healthy social drinkers at rest, during eyes-open and eyes-closed sessions, after administering to them an alcoholic beverage or placebo respectively. Salivary alcohol and cortisol served to measure the inebriation and stress levels. By calculating Magnitude Square Coherence (MSC) on standardized Low Resolution Electromagnetic Tomography (sLORETA) solutions, we formed cortical networks over several frequency bands, which were then analyzed in the context of functional connectivity and graph theory. MSC was increased (p<0.05, corrected with False Discovery Rate, FDR corrected) in alpha, beta (eyes-open) and theta bands (eyes-closed) following acute alcohol intake. Graph parameters were accordingly altered in these bands quantifying the effect of alcohol on the structure of brain networks; global efficiency and density were higher and path length was lower during alcohol (vs. placebo, p<0.05). Salivary alcohol concentration was positively correlated with the density of the network in beta band. The degree of specific nodes was elevated following alcohol (vs. placebo). Our findings support the hypothesis that short-term inebriation considerably increases large-scale connectivity in the RSN. The increased baseline functional connectivity can -at least partially- be attributed to the alcohol-induced disruption of the delicate balance between inhibitory and excitatory neurotransmission in favor of inhibitory influences. Thus, it is suggested that short-term inebriation is associated, as expected, to increased GABA transmission and functional connectivity, while long-term alcohol consumption may be linked to exactly the opposite effect.

Real time emotion aware applications: a case study employing emotion evocative pictures and neuro-physiological sensing enhanced by Graphic Processor Units.

In this paper the feasibility of adopting Graphic Processor Units towards real-time emotion aware computing is investigated for boosting the time consuming computations employed in such applications. The proposed methodology was employed in analysis of encephalographic and electrodermal data gathered when participants passively viewed emotional evocative stimuli. The GPU effectiveness when processing electroencephalographic and electrodermal recordings is demonstrated by comparing the execution time of chaos/complexity analysis through nonlinear dynamics (multi-channel correlation dimension/D2) and signal processing algorithms (computation of skin conductance level/SCL) into various popular programming environments. Apart from the beneficial role of parallel programming, the adoption of special design techniques regarding memory management may further enhance the time minimization which approximates a factor of 30 in comparison with ANSI C language (single-core sequential execution). Therefore, the use of GPU parallel capabilities offers a reliable and robust solution for real-time sensing the user's affective state.

Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli.

This paper proposes a methodology for the robust classification of neurophysiological data into four emotional states collected during passive viewing of emotional evocative pictures selected from the International Affective Picture System. The proposed classification model is formed according to the current neuroscience trends, since it adopts the independency of two emotional dimensions, namely arousal and valence, as dictated by the bidirectional emotion theory, whereas it is gender-specific. A two-step classification procedure is proposed for the discrimination of emotional states between EEG signals evoked by pleasant and unpleasant stimuli, which also vary in their arousal/intensity levels. The first classification level involves the arousal discrimination. The valence discrimination is then performed. The Mahalanobis (MD) distance-based classifier and support vector machines (SVMs) were used for the discrimination of emotions. The achieved overall classification rates were 79.5% and 81.3% for the MD and SVM, respectively, significantly higher than in previous studies. The robust classification of objective emotional measures is the first step toward numerous applications within the sphere of human-computer interaction.

On the classification of emotional biosignals evoked while viewing affective pictures: an integrated data-mining-based approach for healthcare applications.

Recent neuroscience findings demonstrate the fundamental role of emotion in the maintenance of physical and mental health. In the present study, a novel architecture is proposed for the robust discrimination of emotional physiological signals evoked upon viewing pictures selected from the International Affective Picture System (IAPS). Biosignals are multichannel recordings from both the central and the autonomic nervous systems. Following the bidirectional emotion theory model, IAPS pictures are rated along two dimensions, namely, their valence and arousal. Following this model, biosignals in this paper are initially differentiated according to their valence dimension by means of a data mining approach, which is the C4.5 decision tree algorithm. Then, the valence and the gender information serve as an input to a Mahalanobis distance classifier, which dissects the data into high and low arousing. Results are described in Extensible Markup Language (XML) format, thereby accounting for platform independency, easy interconnectivity, and information exchange. The average recognition (success) rate was 77.68% for the discrimination of four emotional states, differing both in their arousal and valence dimension. It is, therefore, envisaged that the proposed approach holds promise for the efficient discrimination of negative and positive emotions, and it is hereby discussed how future developments may be steered to serve for affective healthcare applications, such as the monitoring of the elderly or chronically ill people.

Towards a semantic framework for an integrative description of neuroscience patterns and studies: a case for emotion-related data.

The continuously increasing number of neuroscience studies and the difficulties associated with searching for related information and properly tracking neuroscience findings makes it imperative that one may be lead to isolated theories and findings which may be incompatible to each other or partially occluded. Semantically describing several aspects of studies in this field, such as, research groups attributes, aims of studies, experimental procedures followed, hardware and software tools utilised, acquisition systems used, as well as, the emerging neuro-physiological patterns found, may facilitate an integrative view of neuroscience theories. To this end, the current piece of work aims to provide a global theoretical framework using ontologies and semantic rules to describe neuroscience studies. Implementation details and applicability of the proof of concept are illustrated by means of an example targeting the semantic description of an emotion related study. The importance of the proposed framework in facilitating the envisaged personalised healthcare of the information society is discussed.

An automated system for processing electrodermal activity.

A new approach is presented in this paper for the display and processing of electrodermal activity. It offers a fully automated interface for the pre-processing and scoring individual skin conductance responses (SCRs). The application supports parallel processing by means of multiple threads. Batch processing is also available. The XML format is used to describe the derived features. The system is employed to analyze emotion-related data.

A framework combining delta Event-Related Oscillations (EROs) and Synchronisation Effects (ERD/ERS) to study emotional processing.

Event-Related Potentials (ERPs) or Event-Related Oscillations (EROs) have been widely used to study emotional processing, mainly on the theta and gamma frequency bands. However, the role of the slow (delta) waves has been largely ignored. The aim of this study is to provide a framework that combines EROs with Event-Related Desynchronization (ERD)/Event-Related Synchronization (ERS), and peak amplitude analysis of delta activity, evoked by the passive viewing of emotionally evocative pictures. Results showed that this kind of approach is sensitive to the effects of gender, valence, and arousal, as well as, the study of interhemispherical disparity, as the two-brain hemispheres interplay roles in the detailed discrimination of gender. Valence effects are recovered in both the central electrodes as well as in the hemisphere interactions. These findings suggest that the temporal patterns of delta activity and the alterations of delta energy may contribute to the study of emotional processing. Finally the results depict the improved sensitivity of the proposed framework in comparison to the traditional ERP techniques, thereby delineating the need for further development of new methodologies to study slow brain frequencies.

A methodology for reliability analysis in health networks.

A reliability model for a health care domain based on requirement analysis at the early stage of design of regional health network (RHN) is introduced. RHNs are considered as systems supporting the services provided by health units, hospitals, and the regional authority. Reliability assessment in health care domain constitutes a field-of-quality assessment for RHN. A novel approach for predicting system reliability in the early stage of designing RHN systems is presented in this paper. The uppermost scope is to identify the critical processes of an RHN system prior to its implementation. In the methodology, Unified Modeling Language activity diagrams are used to identify megaprocesses at regional level and the customer behavior model graph (CBMG) to describe the states transitions of the processes. CBMG is annotated with: 1) the reliability of each component state and 2) the transition probabilities between states within the scope of the life cycle of the process. A stochastic reliability model (Markov model) is applied to predict the reliability of the business process as well as to identify the critical states and compare them with other processes to reveal the most critical ones. The ultimate benefit of the applied methodology is the design of more reliable components in an RHN system. The innovation of the approach of reliability modeling lies with the analysis of severity classes of failures and the application of stochastic modeling using discrete-time Markov chain in RHNs.

Reliability assessment of home health care services.

In this paper, a model of reliability assessment of services in Home Health Care Delivery is presented. Reliability is an important quality dimension for services and is included in non-functional requirements of a system. A stochastic Markov model for reliability assessment is applied to patient communication services, in the field of home health care delivery. The methodology includes the specification of scenarios, the definition of failures in scenarios as well as the application of the analytical model. The results of the methodology reveal the critical states of the Home Health Care System and recommendations for improvement of the services are proposed. The model gives valuable results in predicting service reliability and, independently of the error types, it can be applied to all fields of Regional Health Network (RHN).

Multi-channel physiological sensing of human emotion: insights into emotion-aware computing using affective protocols, avatars and emotion specifications.

This paper introduces a methodology for combining multi-channel psycho-physiological recordings of affective paradigms into a framework where the scientific results of such experiments are utilized in the human computer interaction context to model the computer's response based on the emotional context of the user and the situation. An affective protocol is described the results of which are expected to be combined with anthropomorphic avatars that enhance the man-machine interaction. The technological infrastructure of the later component is provided by means of XML specifications of signal descriptions and emotion recognition, as well as avatar behavior generator descriptions.

Enhancing the quality of phylogenetic analysis using fuzzy hidden Markov model alignments.

Any effective phylogeny inference based on molecular data begins by performing efficient multiple sequence alignments. So far, the Hidden Markov Model (HMM) method for multiple sequence alignment has been proved competitive to the classical deterministic algorithms with respect to phylogenetic analysis; nevertheless, its stochastic nature does not help it cope with the existing dependence among the sequence elements. This paper deals with phylogenetic analysis of protein and gene data using multiple sequence alignments produced by fuzzy profile Hidden Markov Models. Fuzzy profile HMMs are a novel type of profile HMMs based on fuzzy sets and fuzzy integrals, which generalize the classical stochastic HMM by relaxing its independence assumptions. In this paper, alignments produced by the fuzzy HMM model are used in phylogenetic analysis of protein data, enhancing the quality of phylogenetic trees. The new methodology is implemented in HPV virus phylogenetic inference. The results of the analysis are compared against those obtained by the classical profile HMM model and depict the superiority of the fuzzy profile HMM in this field.

Fuzzy Hidden Markov Models: a new approach in multiple sequence alignment.

This paper proposes a novel method for aligning multiple genomic or proteomic sequences using a fuzzyfied Hidden Markov Model (HMM). HMMs are known to provide compelling performance among multiple sequence alignment (MSA) algorithms, yet their stochastic nature does not help them cope with the existing dependence among the sequence elements. Fuzzy HMMs are a novel type of HMMs based on fuzzy sets and fuzzy integrals which generalizes the classical stochastic HMM, by relaxing its independence assumptions. In this paper, the fuzzy HMM model for MSA is mathematically defined. New fuzzy algorithms are described for building and training fuzzy HMMs, as well as for their use in aligning multiple sequences. Fuzzy HMMs can also increase the model capability of aligning multiple sequences mainly in terms of computation time. Modeling the multiple sequence alignment procedure with fuzzy HMMs can yield a robust and time-effective solution that can be widely used in bioinformatics in various applications, such as protein classification, phylogenetic analysis and gene prediction, among others.

Model-based processing scheme for quantitative 4-D cardiac MRI analysis.

In this paper, we present an integrated model-based processing scheme for cardiac magnetic resonance imaging (MRI), embedded in an interactive computing environment suitable for quantitative cardiac analysis, which provides a set of functions for the extraction, modeling, and visualization of cardiac shape and deformation. The methods apply four-dimensional (4-D) processing (three spatial and one temporal) to multiphase multislice MRI acquisitions and produce a continuous 4-D model of the myocardial surface deformation. The model is used to measure diagnostically useful parameters, such as wall motion, myocardial thicking, and myocardial mass measurements. The proposed model-based shape extraction method has the advantage of integrating local information into an overall representation and produces a robust description of cardiac cavities. A learning segmentation process that incorporates a generating-shrinking neural network is combined with a spatiotemporal parametric modeling method through functional basis decomposition. A multiscale approach is adopted, which uses at each step a coarse-scale model defined at the previous step in order to constrain the boundary detection. The representation accuracy starts from a coarse but smooth estimation of the approximate cardiac shape and is gradually increased to the desired detail. The main advantages of the proposed methods are efficiency, lack of uncertainty about convergence, and robustness to image artifacts. Experimental results obtained from application to clinical multislice multiphase MRI examinations of normal volunteers and patients with medical record of myocardial infarction were satisfactory in terms of accuracy and robustness.

DIABCARD CCMIS--a portable and scalable CPR for diabetes care.

The aim of this paper is to introduce the main software module of the DIABCARD Chip Card Medical Information System (DIABCARD CCMIS) that provides an online, portable diabetes medical record information system based on a high performance object-oriented rapid application development language such as Borland Delphi. A chip card based medical information system was developed as a good possibility to create a portable electronic patient record. In particular the patient data card makes the up-to-date patient's record available whenever needed. The developed DIABCARD Core System, described in this paper, includes a patient record management system that has the ability to handle topics such as administrative and medical data, medical anamnesis, and physical examination data. Issues tackled were simplicity, data security and reporting, customization, and internationalization. Especially for the two last issues (customization and internationalization) a novel approach based on using native initialization table files is presented. Proper care has been addressed during the development of the software modules for matters of security, data integrity and confidentiality.