975 resultados para Electroencephalogram(ECG)
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Background Atrial fibrillation (AF) patients with a high risk of stroke are recommended anticoagulation with warfarin. However, the benefit of warfarin is dependent upon time spent within the target therapeutic range (TTR) of their international normalised ratio (INR) (2.0 to 3.0). AF patients possess limited knowledge of their disease and warfarin treatment and this can impact on INR control. Education can improve patients' understanding of warfarin therapy and factors which affect INR control. Methods/Design Randomised controlled trial of an intensive educational intervention will consist of group sessions (between 2-8 patients) containing standardised information about the risks and benefits associated with OAC therapy, lifestyle interactions and the importance of monitoring and control of their International Normalised Ratio (INR). Information will be presented within an 'expert-patient' focussed DVD, revised educational booklet and patient worksheets. 200 warfarin-naïve patients who are eligible for warfarin will be randomised to either the intervention or usual care groups. All patients must have ECG-documented AF and be eligible for warfarin (according to the NICE AF guidelines). Exclusion criteria include: aged < 18 years old, contraindication(s) to warfarin, history of warfarin USE, valvular heart disease, cognitive impairment, are unable to speak/read English and disease likely to cause death within 12 months. Primary endpoint is time spent in TTR. Secondary endpoints include measures of quality of life (AF-QoL-18), anxiety and depression (HADS), knowledge of AF and anticoagulation, beliefs about medication (BMQ) and illness representations (IPQ-R). Clinical outcomes, including bleeding, stroke and interruption to anticoagulation will be recorded. All outcome measures will be assessed at baseline and 1, 2, 6 and 12 months post-intervention. Discussion More data is needed on the clinical benefit of educational intervention with AF patients receiving warfarin. Trial registration ISRCTN93952605
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This Thesis addresses the problem of automated false-positive free detection of epileptic events by the fusion of information extracted from simultaneously recorded electro-encephalographic (EEG) and the electrocardiographic (ECG) time-series. The approach relies on a biomedical case for the coupling of the Brain and Heart systems through the central autonomic network during temporal lobe epileptic events: neurovegetative manifestations associated with temporal lobe epileptic events consist of alterations to the cardiac rhythm. From a neurophysiological perspective, epileptic episodes are characterised by a loss of complexity of the state of the brain. The description of arrhythmias, from a probabilistic perspective, observed during temporal lobe epileptic events and the description of the complexity of the state of the brain, from an information theory perspective, are integrated in a fusion-of-information framework towards temporal lobe epileptic seizure detection. The main contributions of the Thesis include the introduction of a biomedical case for the coupling of the Brain and Heart systems during temporal lobe epileptic seizures, partially reported in the clinical literature; the investigation of measures for the characterisation of ictal events from the EEG time series towards their integration in a fusion-of-knowledge framework; the probabilistic description of arrhythmias observed during temporal lobe epileptic events towards their integration in a fusion-of-knowledge framework; and the investigation of the different levels of the fusion-of-information architecture at which to perform the combination of information extracted from the EEG and ECG time-series. The performance of the method designed in the Thesis for the false-positive free automated detection of epileptic events achieved a false-positives rate of zero on the dataset of long-term recordings used in the Thesis.
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This thesis addresses the problem of information hiding in low dimensional digital data focussing on issues of privacy and security in Electronic Patient Health Records (EPHRs). The thesis proposes a new security protocol based on data hiding techniques for EPHRs. This thesis contends that embedding of sensitive patient information inside the EPHR is the most appropriate solution currently available to resolve the issues of security in EPHRs. Watermarking techniques are applied to one-dimensional time series data such as the electroencephalogram (EEG) to show that they add a level of confidence (in terms of privacy and security) in an individual’s diverse bio-profile (the digital fingerprint of an individual’s medical history), ensure belief that the data being analysed does indeed belong to the correct person, and also that it is not being accessed by unauthorised personnel. Embedding information inside single channel biomedical time series data is more difficult than the standard application for images due to the reduced redundancy. A data hiding approach which has an in built capability to protect against illegal data snooping is developed. The capability of this secure method is enhanced by embedding not just a single message but multiple messages into an example one-dimensional EEG signal. Embedding multiple messages of similar characteristics, for example identities of clinicians accessing the medical record helps in creating a log of access while embedding multiple messages of dissimilar characteristics into an EPHR enhances confidence in the use of the EPHR. The novel method of embedding multiple messages of both similar and dissimilar characteristics into a single channel EEG demonstrated in this thesis shows how this embedding of data boosts the implementation and use of the EPHR securely.
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In this paper new architectural approaches that improve the energy efficiency of a cellular radio access network (RAN) are investigated. The aim of the paper is to characterize both the energy consumption ratio (ECR) and the energy consumption gain (ECG) of a cellular RAN when the cell size is reduced for a given user density and service area. The paper affirms that reducing the cell size reduces the cell ECR as desired while increasing the capacity density but the overall RAN energy consumption remains unchanged. In order to trade the increase in capacity density with RAN energy consumption, without degrading the cell capacity provision, a sleep mode is introduced. In sleep mode, cells without active users are powered-off, thereby saving energy. By combining a sleep mode with a small-cell deployment architecture, the paper shows that the ECG can be increased by the factor n = (R/R) while the cell ECR continues to decrease with decreasing cell size.
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Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing.
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We observed an anomaly in the human electroencephalogram (EEG) associated with exposure to terrestrial trunked radio (TETRA) Radiofrequency Fields (RF). Here, we characterize the time and frequency components of the anomaly and demonstrate that it is an artefact caused by TETRA RF interfering with the EEG recording equipment and not by any direct or indirect effect on the brain.
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Background - Several antipsychotic agents are known to prolong the QT interval in a dose dependent manner. Corrected QT interval (QTc) exceeding a threshold value of 450 ms may be associated with an increased risk of life threatening arrhythmias. Antipsychotic agents are often given in combination with other psychotropic drugs, such as antidepressants, that may also contribute to QT prolongation. This observational study compares the effects observed on QT interval between antipsychotic monotherapy and psychoactive polytherapy, which included an additional antidepressant or lithium treatment. Method - We examined two groups of hospitalized women with Schizophrenia, Bipolar Disorder and Schizoaffective Disorder in a naturalistic setting. Group 1 was composed of nineteen hospitalized women treated with antipsychotic monotherapy (either haloperidol, olanzapine, risperidone or clozapine) and Group 2 was composed of nineteen hospitalized women treated with an antipsychotic (either haloperidol, olanzapine, risperidone or quetiapine) with an additional antidepressant (citalopram, escitalopram, sertraline, paroxetine, fluvoxamine, mirtazapine, venlafaxine or clomipramine) or lithium. An Electrocardiogram (ECG) was carried out before the beginning of the treatment for both groups and at a second time after four days of therapy at full dosage, when blood was also drawn for determination of serum levels of the antipsychotic. Statistical analysis included repeated measures ANOVA, Fisher Exact Test and Indipendent T Test. Results - Mean QTc intervals significantly increased in Group 2 (24 ± 21 ms) however this was not the case in Group 1 (-1 ± 30 ms) (Repeated measures ANOVA p < 0,01). Furthermore we found a significant difference in the number of patients who exceeded the threshold of borderline QTc interval value (450 ms) between the two groups, with seven patients in Group 2 (38%) compared to one patient in Group 1 (7%) (Fisher Exact Text, p < 0,05). Conclusions - No significant prolongation of the QT interval was found following monotherapy with an antipsychotic agent, while combination of these drugs with antidepressants caused a significant QT prolongation. Careful monitoring of the QT interval is suggested in patients taking a combined treatment of antipsychotic and antidepressant agents.
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Although slow waves of the electroencephalogram (EEG) have been associated with attentional processes, the functional significance of the alpha component in the EEG (8.1–12 Hz) remains uncertain. Conventionally, synchronisation in the alpha frequency range is taken to be a marker of cognitive inactivity, i.e. ‘cortical idling’. However, it has been suggested that alpha may index the active inhibition of sensory information during internally directed attentional tasks such as mental imagery. More recently, this idea has been amended to encompass the notion of alpha synchronisation as a means of inhibition of non-task relevant cortical areas irrespective of the direction of attention. Here we test the adequacy of the one idling and two inhibition hypotheses about alpha. In two experiments we investigated the relation between alpha and internally vs. externally directed attention using mental imagery vs. sensory-intake paradigms. Results from both experiments showed a clear relationship between alpha and both attentional factors and increased task demands. At various scalp sites alpha amplitudes were greater during internally directed attention and during increased load, results incompatible with alpha reflecting cortical idling and more in keeping with suggestions of active inhibition necessary for internally driven mental operations.
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OBJECTIVES: Mobile phones (MP) are used extensively and yet little is known about the effects they may have on human physiology. There have been conflicting reports regarding the relation between MP use and the electroencephalogram (EEG). The present study suggests that this conflict may be due to methodological differences such as exposure durations, and tests whether exposure to an active MP affects EEG as a function of time. METHODS: Twenty-four subjects participated in a single-blind fully counterbalanced cross-over design, where both resting EEG and phase-locked neural responses to auditory stimuli were measured while a MP was either operating or turned off. RESULTS: MP exposure altered resting EEG, decreasing 1-4 Hz activity (right hemisphere sites), and increasing 8-12 Hz activity as a function of exposure duration (midline posterior sites). MP exposure also altered early phase-locked neural responses, attenuating the normal response decrement over time in the 4-8 Hz band, decreasing the response in the 1230 Hz band globally and as a function of time, and increasing midline frontal and lateral posterior responses in the 30-45 Hz band. CONCLUSIONS: Active MPs affect neural function in humans and do so as a function of exposure duration. The temporal nature of this effect may contribute to the lack of consistent results reported in the literature.
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OBJECTIVES: Exposure to active mobile phones (MP) has been shown to affect human neural function as shown by the electroencephalogram (EEG). Although it has not been determined whether such effects are harmful, a number of devices have been developed that attempt to minimize these MP-related effects. One such device, the Q Link Ally® (QL; Clarus Products, International, L.L.C., San Rafael, CA), is argued to affect the human organism in such a way as to attenuate the effect of MPs. The present pilot study was designed to determine whether there is any indication that QL does alter MP-related effects on the human EEG. DESIGN: Twenty-four (24) subjects participated in a single-blind, fully counterbalanced crossover design in which subjects' resting EEG and phase-locked neural responses to auditory stimuli were assessed under conditions of either active MP or active MP plus QL. RESULTS: The addition of QL to the MP condition increased resting EEG in the gamma range and did so as a function of exposure duration, and it attenuated MP-related effects in the delta and alpha range (at trend-level). The addition of the QL also affected phase-locked neural responses, with a laterality reversal in the alpha range and an alteration to changes over time in the delta range, a reduction of the MP-related beta decrease over time at fronto-posterior sites, and a global reduction in the gamma range that increased as a function of exposure duration. No unambiguous relations were found between these changes and either performance or psychologic state. CONCLUSIONS: This pilot study suggests that the addition of the QL to active MP-exposure does affect neural function in humans, altering both resting EEG patterns and the evoked neural response to auditory stimuli, and that there is a tendency for some MP-related changes to the EEG to be attenuated by the QL.
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Summarizing the accumulated experience for a long time in the polyparametric cognitive modeling of different physiological processes (electrocardiogram, electroencephalogram, electroreovasogram and others) and the development on this basis some diagnostics methods give ground for formulating a new methodology of the system analysis in biology. The gist of the methodology consists of parametrization of fractals of electrophysiological processes, matrix description of functional state of an object with a unified set of parameters, construction of the polyparametric cognitive geometric model with artificial intelligence algorithms. The geometry model enables to display the parameter relationships are adequate to requirements of the system approach. The objective character of the elements of the models and high degree of formalization which facilitate the use of the mathematical methods are advantages of these models. At the same time the geometric images are easily interpreted in physiological and clinical terms. The polyparametric modeling is an object oriented tool possessed advances functional facilities and some principal features.
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Technological advances have driven some attempt of vital parameters monitoring in adverse environments; these improvements will make possible to monitor cardiac activity also in automotive environments. In this scenario, heart rate changes associated with alcohol consumption, become of great importance to assess the drivers state during time. This paper presents the results of a first set of experiments aimed to discover heart rate variability modification induced by moderate assumption of alcoholic drink (i.e. single draft beer) as that typically occurs in weekend among some people. In the study, twenty subjects were enrolled and for each of them two electrocardiographic recordings were carried out: the first before alcohol ingestion and the second after 25-30 minutes. Each participant remained fasting until the second ECG acquisition was completed. ECG signal were analyzed by typical timedomain, frequency and non linear analysis. Results showed a small increase in LF/HF ratio which reflects a dominance of the sympathetic system over the parasympathetic system, and an increase in signal complexity as proven by non linear analysis. However, the study highlighted the need to monitor HRV starting from alcohol ingestion until its complete metabolization to allow a more precise description of its variation. © Springer International Publishing Switzerland 2014.
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The use of electrocardiogram as biometric has raised attention in the last decade and a wide variety of ECG features were explored to verify the feasibility of such a signal. In this work the authors aim to describe a simple template based approach to the electrocardiographic biometric identification using the morphology of individual's heartbeat. The developed algorithm was tested on different recordings made available in the Physionet public database Fantasia: two different sets of heartbeats were extracted from individual recordings one was used for the template building while the second for the tests. The performances of the algorithm are encouraging with a true acceptance rate of 99.4%, however, the procedure needs to be tested on different recordings of the same individual, or during the course of a whole day or physical activity. © 2013 IEEE.
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Long term recording of biomedical signals such as ECG, EMG, respiration and other information (e.g. body motion) can improve diagnosis and potentially monitor the evolution of many widespread diseases. However, long term monitoring requires specific solutions, portable and wearable equipment that should be particularly comfortable for patients. The key-issues of portable biomedical instrumentation are: power consumption, long-term sensor stability, comfortable wearing and wireless connectivity. In this scenario, it would be valuable to realize prototypes using available technologies to assess long-term personal monitoring and foster new ways to provide healthcare services. The aim of this work is to discuss the advantages and the drawbacks in long term monitoring of biopotentials and body movements using textile electrodes embedded in clothes. The textile electrodes were embedded into garments; tiny shirt and short were used to acquire electrocardiographic and electromyographic signals. The garment was equipped with low power electronics for signal acquisition and data wireless transmission via Bluetooth. A small, battery powered, biopotential amplifier and three-axes acceleration body monitor was realized. Patient monitor incorporates a microcontroller, analog-to-digital signal conversion at programmable sampling frequencies. The system was able to acquire and to transmit real-time signals, within 10 m range, to any Bluetooth device (including PDA or cellular phone). The electronics were embedded in the shirt resulting comfortable to wear for patients. Small size MEMS 3-axes accelerometers were also integrated. © 2011 IEEE.
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Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.