Abstract: The area of affective computing has received significant attention by the research community over the last few years. In this paper we review the underlying principles in the field, in an effort to draw threads for possible future development within medical informatics. The approach is lead by considering the three main affective channels, namely, visual, audio/speech, and physiological in relation to e-health, emotional intelligence and e-learning. A discussion on the importance of past and present applications together with a prediction on future literature output is also provided.

Abstract: In this paper, an ontology-based system (KnowBaSICS-M) is presented for the semantic management of Medical Computational Problems (MCPs), i.e., medical problems and computerised algorithmic solutions. The system provides an open environment, which: (1) allows clinicians and researchers to retrieve potential algorithmic solutions pertinent to a medical problem and (2) enables incorporation of new MCPs into its underlying Knowledge Base (KB). KnowBaSICS-M is a modular system for MCP acquisition and discovery that relies on an innovative ontology-based model incorporating concepts from the Unified Medical Language System (UMLS). Information retrieval (IR) is based on an ontology-based Vector Space Model (VSM) that estimates the similarity among user-defined MCP search criteria and registered MCP solutions in the KB. The results of a preliminary evaluation and specific examples of use are presented to illustrate the benefits of the system. KnowBaSICS-M constitutes an approach towards the construction of an integrated and manageable MCP repository for the biomedical research community.

Abstract: OBJECTIVE: The objective of this study is the development and evaluation of efficient neurophysiological signal statistics, which may assess the driver's alertness level and serve as potential indicators of sleepiness in the design of an on-board countermeasure system. METHODS: Multichannel EEG, EOG, EMG, and ECG were recorded from sleep-deprived subjects exposed to real field driving conditions. A number of severe driving errors occurred during the experiments. The analysis was performed in two main dimensions: the macroscopic analysis that estimates the on-going temporal evolution of physiological measurements during the driving task, and the microscopic event analysis that focuses on the physiological measurements' alterations just before, during, and after the driving errors. Two independent neurophysiologists visually interpreted the measurements. The EEG data were analyzed by using both linear and non-linear analysis tools. RESULTS: We observed the occurrence of brief paroxysmal bursts of alpha activity and an increased synchrony among EEG channels before the driving errors. The alpha relative band ratio (RBR) significantly increased, and the Cross Approximate Entropy that quantifies the synchrony among channels also significantly decreased before the driving errors. Quantitative EEG analysis revealed significant variations of RBR by driving time in the frequency bands of delta, alpha, beta, and gamma. Most of the estimated EEG statistics, such as the Shannon Entropy, Kullback-Leibler Entropy, Coherence, and Cross-Approximate Entropy, were significantly affected by driving time. We also observed an alteration of eyes blinking duration by increased driving time and a significant increase of eye blinks' number and duration before driving errors. CONCLUSIONS: EEG and EOG are promising neurophysiological indicators of driver sleepiness and have the potential of monitoring sleepiness in occupational settings incorporated in a sleepiness countermeasure device. SIGNIFICANCE: The occurrence of brief paroxysmal bursts of alpha activity before severe driving errors is described in detail for the first time. Clear evidence is presented that eye-blinking statistics are sensitive to the driver's sleepiness and should be considered in the design of an efficient and driver-friendly sleepiness detection countermeasure device.

Abstract: This paper addresses through simulation experiments a number of technical issues which are raised during the development and operation of a National Electronic Health Record System (NEHRS). The simulation experiments represent the NEHRS performance for a variety of technological infrastructures, within the context of a realistic scenario. The scenario includes the estimation of the delays created in queues during the exchange of Electronic Patient Records (EPR) between different health service points. It is essential to clarify the delays derive from LAN and Internet technologies, the EPR encryption/decryption, the HL7 message generation/parsing, and the databases. The results of this study identify how a number of technical aspects influence the NEHRS development and operation.

Abstract: OBJECTIVE: The objective of this study was the development and evaluation of nonlinear electroencephalography parameters which assess hypoxia-induced EEG alterations, and describe the temporal characteristics of different hypoxic levels' residual effect upon the brain electrical activity. METHODS: Multichannel EEG, pO2, pCO2, ECG, and respiration measurements were recorded from 10 subjects exposed to three experimental conditions (100% oxygen, hypoxia, recovery) at three-levels of reduced barometric pressure. The mean spectral power of EEG under each session and altitude were estimated for the standard bands. Approximate Entropy (ApEn) of EEG segments was calculated, and the ApEn's time-courses were smoothed by a moving average filter. On the smoothed diagrams, parameters were defined. RESULTS: A significant increase in total power and power of theta and alpha bands was observed during hypoxia. Visual interpretation of ApEn time-courses revealed a characteristic pattern (decreasing during hypoxia and recovering after oxygen re-administration). The introduced qEEG parameters S1 and K1 distinguished successfully the three hypoxic conditions. CONCLUSIONS: The introduced parameters based on ApEn time-courses are assessing reliably and effectively the different hypoxic levels. ApEn decrease may be explained by neurons' functional isolation due to hypoxia since decreased complexity corresponds to greater autonomy of components, although this interpretation should be further supported by electrocorticographic animal studies. SIGNIFICANCE: The introduced qEEG parameters seem to be appropriate for assessing the hypoxia-related neurophysiological state of patients in the hyperbaric chambers in the treatment of decompression sickness, carbon dioxide poisoning, and mountaineering.

Abstract: 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.

Abstract: The problem addressed in this work is sleepiness during driving, which often leads to accidents in the streets. Experiments with sleepy drivers took place and the EEG data were analysed in terms of non-linear methods. Sample entropy and phase synchronization variations were investigated within the signal sections corresponding to "driving events", i.e. driving mistakes or loss of control, as well as to periods of drowsiness and sleepiness, as compared to the periods of normal driving. Decreased sample entropy, indicating loss of complexity, and an increased phase synchronisation have been found in the preliminary study presented. The results are encouraging towards developing an alerting system for predicting and preventing driving accidents.

Abstract: Driver sleepiness due to sleep deprivation is a causative factor in 1% to 3% of all motor vehicle crashes. In recent studies, the importance of developing driver fatigue countermeasure devices has been stressed, in order to help prevent driving accidents and errors. Although numerous physiological indicators are available to describe an individual's level of alertness, the EEG signal has been shown to be one of the most predictive and reliable, since it is a direct measure of brain activity. In the present study, multichannel EEG data that were collected from 20 sleep-deprived subjects during real environmental conditions of driving are presented for the first time. EEG data's annotation made by two independent Medical Doctors revealed an increase of slowing activity and an acute increase of the alpha waves 5-10 seconds before driving events. From the EEG data that were collected, the Relative Band Ratio (RBR) of the EEG frequency bands, the Shannon Entropy, and the Kullback-Leibler (KL) Entropy were estimated for each one second segment. The mean values of these measurements were estimated for 5 minutes periods. Analysis revealed a significant increase of alpha waves relevant band ratios (RBR), a decrease of gamma waves RBR, and a significant decrease of KL entropy when the first and the last 5-min periods were compared. A rapid decrease of both Shannon and K-L entropies was observed just before the driving events. Conclusively, EEG can assess effectively the brain activity alterations that occur a few seconds before sleeping/drowsiness events in driving, and its quantitative measurements can be used as potential sleepiness indicators for future development of driver fatigue countermeasure devices.

Abstract: Regional Healthcare System Authorities (RHSAs) run under the Ministry of Health and Welfare in Greece, aim is to improve the level of quality that health care organizations offer as well as to control the expenditure of health care services provided by the health care organizations. In this article we present the considerations taken during the establishment of the first Regional Contact & Service Center for Public Health in Greece in two of the RHSAs. In this respect, the current piece of work provides an up-to-date experience in establishing and setting the RCSC in its organizational context, an outline of its conceptual model and design, an outlook of the first quarterly results of its use, and a discussion of its potential impact.

Abstract: OBJECTIVES: Contemporary literature illustrates an abundance of adaptive algorithms for mining association rules. However, most literature is unable to deal with the peculiarities, such as missing values and dynamic data creation, that are frequently encountered in fields like medicine. This paper proposes an uncertainty rule method that uses an adaptive threshold for filling missing values in newly added records. A new approach for mining uncertainty rules and filling missing values is proposed, which is in turn particularly suitable for dynamic databases, like the ones used in home care systems. METHODS: In this study, a new data mining method named FiMV (Filling Missing Values) is illustrated based on the mined uncertainty rules. Uncertainty rules have quite a similar structure to association rules and are extracted by an algorithm proposed in previous work, namely AURG (Adaptive Uncertainty Rule Generation). The main target was to implement an appropriate method for recovering missing values in a dynamic database, where new records are continuously added, without needing to specify any kind of thresholds beforehand. RESULTS: The method was applied to a home care monitoring system database. Randomly, multiple missing values for each record's attributes (rate 5-20% by 5% increments) were introduced in the initial dataset. FiMV demonstrated 100% completion rates with over 90% success in each case, while usual approaches, where all records with missing values are ignored or thresholds are required, experienced significantly reduced completion and success rates. CONCLUSIONS: It is concluded that the proposed method is appropriate for the data-cleaning step of the Knowledge Discovery process in databases. The latter, containing much significance for the output efficiency of any data mining technique, can improve the quality of the mined information.

Abstract: The 1st and 2nd Regional Health Care System Authority of Central Macedonia (1st and 2nd PeSY) are two of the seventeen Regional Healthcare System Authorities in Greece. Every single PeSY aims to improve the level of quality that health care organisations offer as well as to control the expenditure of health care services provided by the health care organisations, Hospitals and Primary Care Health units. There is currently an urgent need for Regional Health Authorities to deploy integrated healthcare information system, based on secure networks. The limited interoperability of current hospital information systems (HIS) poses a risk for the management of patient related information since there is a difficulty to transform processed data into useful information and knowledge. Thus, a pilot system was developed to achieve data integration record synchronisation using the Health Level 7 protocol between the existing HIS of two Hospitals of Thessaloniki and the central Offices of the PeSY. The pilot was funded by the Third Community Support Framework (jointly funded by EU and Greece) funds in order to prepare the forthcoming major healthcare IT projects in Greece. It is shown that such a system is pragmatic, achieves data integration and provides acceptable integration costs.

Abstract: Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures, from large amounts of data stored in databases, data warehouses, or other information repositories. Mining Associations is one of the techniques involved in the process mentioned above and used in this paper. Association is the discovery of association relationships or correlations among a set of items. The algorithm that was implemented is a basic algorithm for mining association rules, known as a priori. In Healthcare, association rules are considered to be quite useful as they offer the possibility to conduct intelligent diagnosis and extract invaluable information and build important knowledge bases quickly and automatically. The problem of identifying new, unexpected and interesting patterns in medical databases in general, and diabetic data repositories in specific, is considered in this paper. We have applied the a priori algorithm to a database containing records of diabetic patients and attempted to extract association rules from the stored real parameters. The results indicate that the methodology followed may be of good value to the diagnostic procedure, especially when large data volumes are involved. The followed process and the implemented system offer an efficient and effective tool in the management of diabetes. Their clinical relevance and utility await the results of prospective clinical studies currently under investigation.

Abstract: Magnetic field tomography (MFT) displays three dimensional estimates of the distribution of the primary current density vector, Jp, as extracted from non-invasive, non-contact, magnetoencephalographic (MEG) measurements. MFT was used to study the spatiotemporal evolution of the interictal activity during single spike events of a patient with complex partial epilepsy. The sequences of events of the interictal spikes were analysed in sagittal sections, particularly at the depth of the temporal lobe. It appeared that the left-sided interictal spikes were usually initiated at the cortical level of the left temporal lobe, the activity then propagating to the left amygdaloid and hippocampal formation. However, some focal deep activity in this region was obviously initiated in the contralateral hemisphere.

Abstract: Magnetic field tomography (MFT) provides 3-dimensional estimates of brain activity, from non-contact, non-invasive measurements of the magnetic field generated by coherent electrical activity in the brain. MFT analysis of averaged auditory "odd-ball" data show cortical and deep activation, presumably from the amygdala and hippocampus. These results are compared with MFT estimates obtained from a patient who had undergone lobectomy which removed these structures. The variability from subject to subject is confounded by variability between trials for the same subject; the relationship between the averaged and single trials is probed by bi-hemispheric simultaneous measurements performed under the same odd-ball paradigm and by MFT analysis of auditory evoked data and interictal epileptic activity.

Abstract: Magnetic field tomography is a technique for extracting 3-dimensional estimates of current density in the brain, from non-contact, non-invasive measurements of the magnetic field generated by the brain. It allows visualisation of both cortical and subcortical focal activation patterns at millisecond intervals, and the relative time difference between active cortical areas. We have used this technique to study the activation history of discrete brain regions associated with the preparation for, initiation and inhibition of movement, and movement itself in a CNV paradigm. The strongest focal activities are found within well defined cortical regions, namely the auditory (A1), sensorimotor (SM1), medial parietal area (MPA) and anterior supplementary motor area (SMA). For the movement condition, activation history differs for the warning stimulus and the stimulus initiating movement.