P-wave beat-to-beat analysis to predict atrial fibrillation recurrence after catheter ablation

Introduction

Identification of patients prone to atrial fibrillation (AF) relapse after catheter ablation is essential for better patient selection and risk stratification.

Purpose

The current prospective cohort study aims to validate a novel P-wave index based on beat-to-beat (B2B) P-wave morphological and wavelet analysis designed to detect patients with low burden AF, as a predictor of AF recurrence within a year after successful catheter ablation.

Methods

12-lead ECG and 10-minute vectorcardiogram (VCG) recordings were obtained from 138 consecutive patients scheduled for AF ablation. Pre-ablation B2B P-wave index, along with standard P-wave indices, clinical scores and patients history and physical examination parameters were evaluated as AF recurrence predictors.

Results

Univariate analysis revealed that patients with higher B2B P-wave index had a two-fold risk for AF recurrence (HR: 2.35, 95% CI: 1.24–4.44, p: 0.010). Prolonged P-wave, interatrial block, early AF recurrence, female gender, heart failure history, previous stroke, and CHA2DS2-VASc score ≥2 were also found to be related to higher recurrence rate. Multivariate analysis of predictors that can be assessed before ablation revealed that B2B P-wave index, along with heart failure history and history of previous stroke or transient ischemic attack are independent predicting factors of AF relapse.

Conclusion

B2B P-wave morphology and wavelet analysis, is a promising, non-invasive technique, able to identify patients prone to AF recurrence after pulmonary veins ablation. Further studies are needed to assess the predictive value of B2B index with greater accuracy and evaluate a possible relationship with atrial substrate analysis.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): Hellenic Society of Cardiology

Assessment of Singularities in the EEG During A-Phases of Sleep Based on Wavelet Decomposition

Electroencephalography (EEG) signals convey information related to different processes that take place in the brain. From the EEG fluctuations during sleep, it is possible to establish the sleep stages and identify short events, commonly related to a specific physiological process or pathology. Some of these short events (called A-phases) present an organization and build up the concept of the Cyclic Alternating Pattern (CAP) phenomenon. In general, the A-phases abruptly modify the EEG fluctuations, and a singular behavior could occur. With the aim to quantify the abrupt changes during A-phases, in this work the wavelet analysis is considered to compute Hölder exponents, which measure the singularity strength. We considered time windows of 2s outside and 5s inside A-phases onset (or offset). A total number of 5121 A-phases from 9 healthy participants and 10 patients with periodic leg movements were analyzed. Within an A-phase the Hölder numerical value tends to be 0.6, which implies a less abrupt singularity. Whereas outside of A-phases, it is observed that the Hölder value is approximately equal to 0.3, which implies stronger singularities, i.e., a more evident discontinuity in the signal behavior. In addition, it seems that the number of singularities increases inside of A-phases. The numerical results suggest that the EEG naturally conveys singularities modified by the A-phase occurrence, and this information could help to conceptualize the CAP phenomenon from a new perspective based on the sharpness of the EEG instead of the oscillatory way.

Beat-to-beat P-wave analysis outperforms conventional P-wave indices in identifying patients with a history of paroxysmal atrial fibrillation, during sinus rhythm

Introduction

Atrial fibrillation (AF) is the most common arrhythmia and is associated with high risk of morbidity and mortality. In many patients, AF is of episodic character (paroxysmal AF – PAF), which makes the identification of these patients during sinus rhythm (SR) challenging.

Purpose

The aim of the present study is to compare the performance of beat-to-beat P-wave analysis with P-wave indices used as predictors of PAF, such as P-wave duration, area, voltage, axis, terminal force in V1, inter-atrial block or orthogonal type, in identifying patients with history of PAF during sinus rhythm.

Methods

Standard 12-lead ECG and 10-minute orthogonal ECG recordings were obtained from 40 consecutive patients with short history of PAF under no antiarrhythmic medication and 60 age- and sex- matched healthy controls. The P-waves on the 10-minute recordings were analyzed on a beat-to-beat basis and classified as belonging to a primary or secondary morphology according to previous study. Wavelet transform used to further analyze P-wave orthogonal signals of main morphology on a beat-to-beat basis.

Results

38 out of 327 studied features were found to differ significantly among the two groups. These features were tested for their diagnostic ability and receiver operating characteristic curves were ploted. Only 3 of them performed adequetly, with an area under curve (AUC) above 0.65; Two of them came from morphology analysis (percentage of beats following main morphology in axis X and Y) and one from wavelet analysis (max energy in high frequency zone -Y axis). Among standard P-wave indices, P-wave area in lead II was the one with the highest AUC (0.64).

Conclusion

Novel indices derived from beat-to-beat analysis outperform stadard P-wave markers in identifying patients with PAF history during sinus rhythm.

Funding Acknowledgement

Type of funding sources: None. ROC curves of most significant featuresAUC characteristics of P-wave indices

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

P-wave beat-to-beat morphology analysis outperforms conventional P-wave indices in detecting patients with paroxysmal atrial fibrillation

Funding Acknowledgements

Type of funding sources: None.

Background

Atrial fibrillation (AF) - the most common sustained cardiac arrhythmia - while not a life-threatening condition itself, leads to an increased risk of stroke and high rates of mortality. Early detection and diagnosis of AF is a critical issue for all health stakeholders.

Purpose

The aim of this study is to compare the performance of standard P-wave indices with beat-to-beat P-wave morphological variability parameters in identifying patients with history of Paroxysmal Atrial Fibrillation (PAF).

Methods

Three-dimensional 1000Hz ECG digital recordings of 10 minutes duration were obtained from a total of 39 PAF patients and 60 healthy individuals. Following artifacts and ectopic beats removal, P‑wave morphology analysis was performed based on the dynamic application of the k‑means clustering process and main and secondary P-wave morphologies were identified. The percentage of P-waves following the main or the secondary morphology in each lead was calculated, as well as established indices such as Advanced Interatrial Block, P-wave duration, axis and area, P-wave Terminal Force in lead V1 and Orthogonal Leads Type 1, 2 or 3.

Results

9 out of 24 parameters studied, were found to be significantly different between the two groups. 7 of these indices were derived from morphology analysis and 2 from P-wave area. Logistic regression revealed that the percentage of P-waves allocated to main morphology in X axis performed better than all other indices in identifying patients with PAF history from healthy volunteers in terms of total accuracy and F1 measure.

Conclusion

P-wave beat-to-beat morphology analysis can identify PAF patients during normal sinus rhythm more efficiently than standard P-wave indices. Abstract Figure.

219A machine learning classification algorithm to detect patients with paroxysmal atrial fibrillation during sinus rhythm

Background

Atrial fibrillation (AF) - the most common sustained cardiac arrhythmia - while not a life-threatening condition itself, leads to an increased risk of stroke and high rates of mortality. Early detection and diagnosis of AF is a critical issue for all health stakeholders.

Purpose

The aim of this study is to identify P-wave morphology patterns encountered in patients with Paroxysmal AF (PAF) and to develop a classifier discriminating PAF patients from healthy volunteers.

Methods

Three-dimensional 1000Hz ECG signals of 5 minutes duration were obtained through the use of a Galix GBI-3S Holter monitor from a total of 68 PAF patients and 52 healthy individuals. Signal pre-processing, consisting of denoising, QRS auto-detection, and ectopic beats removal was performed and a signal window of 250ms prior to the Q-wave (Pseg) was considered for every single beat. P‑wave morphology analysis based on the dynamic application of the k‑means clustering process was performed. For those Pseg that were assigned in the largest cluster, the mean P-wave was computed. The correlation of every P-wave with the mean P-wave of the main cluster was calculated. In case that it exceeded a prespecified threshold, the P-wave was allocated to the main morphology. For the remaining P‑waves, the same approach was followed once again, and the secondary morphology was extracted (picture). The P-waves of the dominant morphology were further analyzed using wavelet transform, whereas time-domain characteristics were also extracted.

A Support Vector Machine (SVM) model was created using the Gaussian Radial Basis Function kernel and the forward feature selection wrapper approach was followed. ECGs were allocated to the training, internal validation, and testing datasets in a 3:1:1 ratio.

Results

The percentage of P-waves following the main morphology in all three leads was lower in PAF patients (91.2 ±7.3%) than in healthy subjects (96.1 ±3.5%, p = 0.02). Classification between the two groups highlighted 7 features, while the SVM classifier resulted in a balanced accuracy of 91.4 ± 0.2% (sensitivity 94.2 ± 0.3%, specificity 88.6 ± 0.1%)

Conclusion

An Artificial Intelligence based ECG Classifier can efficiently identify PAF patients during normal sinus rhythm.

Abstract Figure.

244An automated beat exclusion algorithm to improve beat-to-beat P-wave morphology analysis

Background

A manually beat-to-beat P-wave analysis has previously revealed the existence of multiple P-wave morphologies in patients with paroxysmal Atrial Fibrillation (AF) while on sinus rhythm, distinguishing them from healthy, AF free patients.

Purpose

The aim of this study was to investigate the effectiveness of an Automated Beat Exclusion algorithm (ABE) that excludes noisy or ectopic beats, replacing manual beat evaluation during beat-to-beat P-wave analysis, by assessing its effect on inter-rater variability and reproducibility.

Methods

Beat-to-beat P-wave morphology analysis was performed on 34 ten-minute ECG recordings of patients with a history of AF. Each recording was analyzed independently by two clinical experts for a total of four analysis runs; once with ABE and once again with the manual exclusion of ineligible beats. The inter-rater variability and reproducibility of the analysis with and without ABE were assessed by comparing the agreement of analysis runs with respect to secondary morphology detection, primary morphology ECG template and the percentage of both, as these aspects have been previously used to discriminate PAF patients from controls.

Results

Comparing ABE to manual exclusion in detecting secondary P-wave morphologies displayed substantial (Cohen"s k = 0.69) to almost perfect (k = 0.82) agreement. Area difference among auto and manually calculated main morphology templates was in every case <5% (p < 0.01) and the correlation coefficient was >0.99 (p < 0.01). Finally, the percentages of beats classified to the primary or secondary morphology per recording by each analysis were strongly correlated, for both main and secondary P-wave morphologies, ranging from ρ=0.756 to ρ=0.940 (picture)

Conclusion

The use of the ABE algorithm does not diminish inter-rater variability and reproducibility of the analysis. The primary and secondary P-wave morphologies produced by all analyses were similar, both in terms of their template and their frequency. Based on the results of this study, the ABE algorithm incorporated in the beat-to-beat P-wave morphology analysis drastically reduces operator workload without influencing the quality of the analysis.

Abstract Figure.

P322Understanding the multiple P-wave morphologies in paroxysmal atrial fibrillation, during sinus rhythm, using computer simulation

Background

Atrial Fibrillation (AF) is the most common atrial arrhythmia. The initiation and perpetuation of AF are related to atrial remodeling affecting the electrical and structural atrial characteristics. The beat-to-beat analysis of the P-wave morphology (PWM), during sinus rhythm (SR), revealed the existence of a secondary PWM, while the proportion of the P-waves which follow the secondary morphology is higher in patients with a history of paroxysmal AF (pAF). This observation has led to the hypothesis that the multiple PWM may be the result of a transient shift in the stimulus origin, possibly within the broader anatomical region of the sinoatrial (SA) node, and it is the atrial electrical remodeling that contributes to more frequent P-waves following a secondary morphology in patients with pAF.

Purpose

To better understand the pathophysiology of AF there is a need to link different levels of analysis, in order to interpret macroscopic observations, through a surface electrocardiogram, with changes occurring at cell and tissue level. Towards this direction, computational modeling can be used as it is a non-invasive and reproducible method of analyzing the electrical activity of the heart.

Methods

The CRN atrial model was used, and a two-dimensional geometry of the atrial architecture was considered, including the major anatomical structures, like Crista Terminalis, Pectinate Muscles and Pulmonary Veins. Using existing knowledge, the CRN model was adapted to describe the ionic properties of the atrial structures as well as the electrical remodeling occurring under pAF conditions. Several scenarios were considered related to the extent of the electrical remodeled tissue and Heart Rate (HR) values. The stimulation protocol was designed as 5 stimuli originated at a specific point within the SA node area whereas the sixth stimulus originated either at the same location or 1 mm far from the previous one. The temporal variations of the atrial activation as a result of the transient shift of the sixth stimulus origin were computed.

Results

In electrically remodeled tissue, the displacement of the excitation site within the SA node resulted in a significant increase of the differences in atrial activation compared to healthy tissue, and the greater the spatial extent of the remodeling the greater the differences in the completion of the electrophysiological processes. In addition, increased HR or HR variability led to the increase of the differences especially when electrical remodeling coexists.

Conclusions

The observed differences in atrial substrate activation can explain the increased number of P-waves that match a secondary PWM in pAF patients during SR, while a future perspective is to use PWM as a marker to estimate the electrical remodeling extent in the atrial tissue. These results underline the need to link the macroscopic findings to the suspected microscopic electrical activity in order to better understand the pathophysiology of AF.

Prediction of the Adherence to a Home-Based Cardiac Rehabilitation Program

The incidence and prevalence of cardiovascular diseases (CVD) is increasing which is partly due to an increase in unhealthy lifestyles, including lack of physical activity. Therefore, following a cardiovascular event, patients are encouraged to participate in a supervised exercise-based cardiac rehabilitation (CR) program. However, uptake rates of these programs are low and compliance to adequate volumes of physical activity after the completion of such programs are even lower. An approach that has been proposed towards the increase of patient adherence to exercise, is the incorporation of technology-enabled solutions which are applied at patient's homes. However, different factors may affect patient engagement with such alternative solutions. In this work, we use diverse types of data, including baseline characteristics of the patient (i.e. physiological, behavioral, demographical data) as well as usage data of a tele-rehabilitation solution during a 4-week familiarization period, in order to predict the compliance of patients with CVD to a technology-supported physical activity intervention after completion of a supervised exercise program. Patients were clustered based on their use of a technology intervention during a previously conducted study. Following a feature selection approach, a support vector machine was trained to classify patients as adherent or non-adherent to the intervention. The performance of the classifier was assessed by means of the receiving operator curve (ROC). Bio-psycho-social baseline variables predicted adherence with a ROC of 0.86, but adding usage data of the platform during a 4-week familiarization period increased the ROC up to 0.94. Furthermore, the high sensitivity values (83.8% and 95.5% respectively) support the strength of the models to identify those patients with CVD that will be adherent to a technology-enabled, home-based CR program.

Nutrition Adherence in Critically Ill Patients; How is nutritional intake within the 1st week of hospitalization affecting the patient's Outcome?

Nutritional requirements vary during a patient's stay in the Intensive Care Unit (ICU) and their calculation can be relatively complex. During ICU stay nutrition requirements are rarely met, especially during the initial days of the hospitalization. Studies have shown that poor nutrition is associated with adverse patient outcome. This study examines for correlation between poor nutrition (calories, proteins, lipids and micronutrients) during the 1^st week of ICU stay and adverse patient outcome. Nutritional adherence effect is examined on groups of patients, such as patients with high BMI that receive low nutrition and critically ill males. Regarding the latter analysis, an accuracy rate of 76.4% was achieved when classifying the critically ill males towards their outcome. The results of this work could contribute to the development of smart alarms in the ICU.

Exploring the impact of sleep and stress on daily physical activity of cardiac patients: a preliminary study

BACKGROUND

Current approaches to cardiac rehabilitation services tailoring are often based on patient demographics or readiness for behavior change. However, the success of interventions acceptance and improved adherence to recommendations could be much higher when considering and adapting to a patient's lifestyle, such as sleep and stress.

AIMS

We aimed to analyze the potential associations between patient sleep and stress and daily moderate-intensity activity in patients with cardiovascular disease and to gain experience on the methods to collect and analyze a combination of qualitative and quantitative data.

METHODS

Patients with cardiovascular disease enrolled for an outpatient cardiac rehabilitation program were assessed at the study baseline regarding sociodemographic, clinical profile, and perceived level of stress. To collect daily physical activity and sleep data, all participants had two-week long diaries. Collected data was analyzed through correlation analysis, linear regression, and one-way ANOVA analysis.

RESULTS

The mean age of the participants (n =11) was 67.3 ± 9.6 years old. The patients were mainly male (82 %), married (91 %), and having at least one comorbid disease (64 %). The results of the analysis revealed that the night sleep duration is associated with moderate-intensity physical activity [F(1,6) =7.417, p =0.034]. Stress was not associated with patients' moderate-intensity daily physical activity.

CONCLUSION

The outcomes of the study can support the development of e-health and home-based interventions design and strategies to promote adherence to physical activity. Tailoring an intervention to a daily behavioral pattern of a patient, such as sleep, can support the planning of the physical activity in a form to be easier accepted by the patient. This finding emphasizes the need for further investigation of the association with a larger population sample and the use of objective physical activity and sleep-related measure instruments. HIPPOKRATIA 2019, 23(1): 15-20.

Detection of wheezes using their signature in the spectrogram space and musical features

In this work thirty features were tested in order to identify the best feature set for the robust detection of wheezes. The features include the detection of the wheezes signature in the spectrogram space (WS-SS) and twenty-nine musical features usually used in the context of Music Information Retrieval. The method proposed to detect the signature of wheezes imposes a temporal Gaussian regularization and a reduction of the false positives based on the (geodesic) morphological opening by reconstruction operator. Our dataset contains wheezes, crackles and normal breath sounds. Four selection algorithms were used to rank the features. The performance of the features was asserted having into account the Matthews correlation coefficient (MCC). All the selection algorithms ranked the WS-SS feature as the most important. A significant boost in performance was obtained by using around ten features. This improvement was independent of the selection algorithm. The use of more than ten features only allows for a small increase of the MCC value.

Information Technology Can Enhance Quality in Regional Health Delivery

Objectives: a) The use of information technology (IT) based solutions for quality health delivery in regional health information networks and the study of the enabling factors for their use in a regional health care network from key classes of users such as the medical personnel and the citizens. b) Identification of potential technologies for usage from all citizens and health providers in a regional environment, in all aspects of everyday life. c) Presentation of a generic user model for reference when developing and assessing IT based health delivery solutions.

Methods: After defining the major questions to be addressed, an overview of tele-health and tele-medicine technologies and solutions currently available shall be presented. Further, a generic user model applied to the use of IT based regional health delivery solutions both for the daily life and home care, and for research and clinical routine purposes are presented. Enabling technologies for integration of different IT modules, medical data processing and management procedures and the wireless application protocol (WAP) technology is discussed.

Results: Different levels of user applications are presented such as mobile telephony driven health information monitoring and systems integrating electronic health care records with multimedia medical information management and processing modules.

Conclusions: Although IT solutions are advanced and continue to evolve, still the user acceptance and user friendliness issues are unresolved. Mobile telecommunication solutions however may hold the key for wide scale implementation of IT solutions in regional health information networks and increased quality of health services.

Linear and nonlinear features of fetal heart rate on the assessment of fetal development in the course of pregnancy and the impact of fetal gender

OBJECTIVE

This work aims to investigate the impact of gestational age and fetal gender on fetal heart rate (FHR) tracings.

APPROACH

Different linear and nonlinear parameters indicating correlation or complexity were used to study the influence of fetal age and gender on FHR tracings. The signals were recorded from 99 normal pregnant women in a singleton pregnancy at gestational ages from 28 to 40 weeks, before the onset of labor. There were 56 female fetuses and 43 male.

MAIN RESULTS

Analysis of FHR shows that the means as well as measures of irregularity of FHR, such as approximate entropy and algorithmic complexity, decrease as gestation progresses. There were also indications that mutual information and multiscale entropy were lower in male fetuses in early pregnancy.

SIGNIFICANCE

Fetal age and gender seem to influence FHR tracings. Taking this into consideration would improve the interpretation of FHR monitoring.

An Open and Reconfigurable Wireless Sensor Network for Pervasive Health Monitoring

Objectives: Sensor networks constitute the backbone for the construction of personalized monitoring systems. Up to now, several sensor networks have been proposed for diverse pervasive healthcare applications, which are however characterized by a significant lack of open architectures, resulting in closed, non-interoperable and difficult to extend solutions. In this context, we propose an open and reconfigurable wireless sensor network (WSN) for pervasive health monitoring, with particular emphasis in its easy extension with additional sensors and functionality by incorporating embedded intelligence mechanisms.

Methods: We consider a generic WSN architecture comprised of diverse sensor nodes (with communication and processing capabilities) and a mobile base unit (MBU) operating as the gateway between the sensors and the medical personnel, formulating this way a body area network (BAN). The primary focus of this work is on the intra-BAN data communication issues, adopting SensorML as the data representation mean, including the encoding of the monitoring patterns and the functionality of the sensor network.

Results: In our prototype implementation two sensor nodes are emulated; one for heart rate monitoring and the other for blood glucose observations, while the MBU corresponds to a personal digital assistant (PDA) device. Java 2 Micro Edition (J2ME) is used to implement both the sensor nodes and the MBU components. Intra-BAN wireless communication relies on the Bluetooth protocol. Via an adaptive user interface in the MBU, health professionals may specify the monitoring parameters of the WSN and define the monitoring patterns of interest in terms of rules.

Conclusions: This work constitutes an essential step towards the construction of open, extensible, inter - operable and intelligent WSNs for pervasive health monitoring.

Detecting fetal heart sounds by means of Fractal Dimension analysis in the Wavelet domain

Phonocardiography is a low-cost technique for the detection of fetal heart sounds (FHS) that can extend clinical auscultation in mobile and home care setups. The work presented here examines the transferability of a Wavelet Transform (WT)-based method that combines also Fractal Dimension (FD) analysis, previously proposed as WT-FD for the cases of lung and bowel sound analysis [4], to the extraction of FHSs. The WT-FD method has been evaluated with 12 simulated FHS signals and has shown promising results in terms of accuracy and performance (89%) in identifying the location of heartbeat, even in cases of signals with additive noise up to (6dB). This robustness paves the way for WT-FD testing in real FHSs, recorded under clinical setting, clearly contributing to better evaluation of the fetal heart functionality.

Detection of crackle events using a multi-feature approach

The automatic detection of adventitious lung sounds is a valuable tool to monitor respiratory diseases like chronic obstructive pulmonary disease. Crackles are adventitious and explosive respiratory sounds that are usually associated with the inflammation or infection of the small bronchi, bronchioles and alveoli. In this study a multi-feature approach is proposed for the detection of events, in the frame space, that contain one or more crackles. The performance of thirty-five features was tested. These features include thirty-one features usually used in the context of Music Information Retrieval, a wavelet based feature as well as the Teager energy and the entropy. The classification was done using a logistic regression classifier. Data from seventeen patients with manifestations of adventitious sounds and three healthy volunteers were used to evaluate the performance of the proposed method. The dataset includes crackles, wheezes and normal lung sounds. The optimal detection parameters, such as the number of features, were chosen based on a grid search. The performance of the detection was studied taking into account the sensitivity and the positive predictive value. For the conditions tested, the best results were obtained for the frame size equal to 128 ms and twenty-seven features.

Evaluation of lung ventilation distribution in chronic obstructive pulmonary disease patients using the global inhomogeneity index

The global inhomogeneity (GI) index is a electrical impedance tomography (EIT) parameter that quantifies the tidal volume distribution within the lung. In this work the global inhomogeneity index was computed for twenty subjects in order to evaluate his potential use in the detection and follow up of chronic obstructive pulmonary disease (COPD) patients. EIT data of 17 subjects were acquired: 14 patients with the main diagnoses of COPD and 3 healthy subjects which served as a control group. Two or three datasets of around 30 seconds were acquired at 33 scans/s and analysed for each subject. After reconstruction, a tidal EIT image was computed for each breathing cycle and a GI index calculated from it. Results have shown significant differences in GI values between the two groups (0.745 ± 0.007 for COPD and 0.668 ± 0.006 for lung-healthy subject, p <; 0.005). The GI values obtained for each subject have shown small variance between them, which is a good indication of stability. The results suggested that the GI may be useful for the identification and follow up of ventilation problems in patients with COPD.

Restrictions in the T-cell repertoire of chronic lymphocytic leukemia: high-throughput immunoprofiling supports selection by shared antigenic elements

Immunoglobulin (IG) gene repertoire restrictions strongly support antigen selection in the pathogenesis of chronic lymphocytic leukemia (CLL). Given the emerging multifarious interactions between CLL and bystander T cells, we sought to determine whether antigen(s) are also selecting T cells in CLL. We performed a large-scale, next-generation sequencing (NGS) study of the T-cell repertoire, focusing on major stereotyped subsets representing CLL subgroups with undisputed antigenic drive, but also included patients carrying non-subset IG rearrangements to seek for T-cell immunogenetic signatures ubiquitous in CLL. Considering the inherent limitations of NGS, we deployed bioinformatics algorithms for qualitative curation of T-cell receptor rearrangements, and included multiple types of controls. Overall, we document the clonal architecture of the T-cell repertoire in CLL. These T-cell clones persist and further expand overtime, and can be shared by different patients, most especially patients belonging to the same stereotyped subset. Notably, these shared clonotypes appear to be disease-specific, as they are found in neither public databases nor healthy controls. Altogether, these findings indicate that antigen drive likely underlies T-cell expansions in CLL and may be acting in a CLL subset-specific context. Whether these are the same antigens interacting with the malignant clone or tumor-derived antigens remains to be elucidated.

Detection of different types of noise in lung sounds

Lung sound signal processing has proven to be a great improvement to the traditional acoustic interpretation of lung sounds. However, that analysis can be seriously hindered by the presence of different types of noise originated in the acquisition environment or caused by physiological processes. Consequently, the diagnostic accuracy of pulmonary diseases can be severely affected, especially if the implementation of telemonitoring systems is considered. The present study is focused on the implementation of an algorithm able to identify noisy periods, either voluntarily (vocalizations, chest movement and background voices) or involuntarily produced during acquisitions of lung sounds. The developed approach also had to deal with the presence of simulated cough events, that carry important diagnostic information regarding several pulmonary diseases. Features such as Katz fractal dimension, Teager-Kaiser energy operator and normalized mutual information, were extracted from the time domain of healthy and a pathological lung signals. Noise detection was the result of a good discrimination between uncontaminated lung sounds and both cough and noise episodes and a slightly worse classification of cough events. In fact, detection of cough periods carrying diagnostic information was influenced by the presence of two other types of noise having similar signal characteristics.

Healthcare information standards: comparison of the approaches

Electronic transfer of healthcare record information between heterogeneous systems raises the need for healthcare information standards. Such standards improve the level of quality that healthcare organizations offer and also control the expenditure on healthcare services provided by these organizations. There are a large number of healthcare related applications that effectively support specific needs but are isolated or incompatible. There is an urgent need to integrate those applications or to interoperate between them, ensuring security concepts. The cost is another crucial aspect that should be considered in the integration of already implemented systems and the design of new systems. This paper aims to give a description of the current developed standards that can be used to provide interoperability in healthcare information systems and to make a short comparison between these standards, including the following: HL7, HL7’s CCOW, CEN/TC251 Healthcare Information System Architecture Standard. This paper also discusses a solution that is being developed in the Citizen Health System (CHS) project to achieve integration of data using a healthcare informatics standard. A test scenario to make some of the project’s modules visually interoperate is also implemented.

Influence of torso and arm positions on chest examinations by electrical impedance tomography

Electrical impedance tomography (EIT) is increasingly used in patients suffering from respiratory disorders during pulmonary function testing (PFT). The EIT chest examinations often take place simultaneously to conventional PFT during which the patients involuntarily move in order to facilitate their breathing. Since the influence of torso and arm movements on EIT chest examinations is unknown, we studied this effect in 13 healthy subjects (37  ±  4 years, mean age  ±  SD) and 15 patients with obstructive lung diseases (72  ±  8 years) during stable tidal breathing. We carried out the examinations in an upright sitting position with both arms adducted, in a leaning forward position and in an upright sitting position with consecutive right and left arm elevations. We analysed the differences in EIT-derived regional end-expiratory impedance values, tidal impedance variations and their spatial distributions during all successive study phases. Both the torso and the arm movements had a highly significant influence on the end-expiratory impedance values in the healthy subjects (p  =  0.0054 and p  <  0.0001, respectively) and the patients (p  <  0.0001 in both cases). The global tidal impedance variation was affected by the torso, but not the arm movements in both study groups (p  =  0.0447 and p  =  0.0418, respectively). The spatial heterogeneity of the tidal ventilation distribution was slightly influenced by the alteration of the torso position only in the patients (p  =  0.0391). The arm movements did not impact the ventilation distribution in either study group. In summary, the forward torso movement and the arms' abduction exert significant effects on the EIT waveforms during tidal breathing. We recommend strict adherence to the upright sitting position during PFT when EIT is used.

Ineffective efforts in ICU assisted ventilation: Exploring causalities via multiscale analysis

Intensive Care Unit (ICU) is a data intensive environment, requiring continuous monitoring of patient's physiology and response to treatment. In assisted ventilation, where patient effort that triggers the ventilator and there is need for patient-ventilator coupling, attention is required in cases where patient's effort that doesn't trigger the ventilator at all. When synchronization between the patient's attempt to breath and the assisted ventilation event is lost, an ineffective effort (IE) event takes place. A series of relevant bioparameters continuously monitored, are meant to guide the medical professionals in appropriately adapting the operation and treatment, in order to minimize IEs. The purpose of this work is to investigate the causal relations between physiological or ventilation parameters and IE events. A multiscale approach is proposed, based on wavelet similarity and localized phase relationship. The proposed method indicates the existence of distinct frequency zones correlated with the IE experienced by the patient.

A data model to support the evaluation of coordinating care EU programmes in the context of the ACT programme

Advancing Care Coordination and Telehealth Deployment (ACT) is a European Union (EU) project, completed last October, which has developed a framework for evaluating and improving pioneering health care programs regarding coordinating care and telehealth (CC & TH) across specific EU regions. In this paper we present the key design decisions of the project's data model and the challenges faced. We focus on the definition of the multi-dimensional indicators in order to overcome data incompleteness and heterogeneity issues. Finally, we also suggest a graph based approach that could facilitate development of such data models in similar projects.

Medical Informatics Education & Research in Greece

OBJECTIVES

This paper aims to present an overview of the medical informatics landscape in Greece, to describe the Greek ehealth background and to highlight the main education and research axes in medical informatics, along with activities, achievements and pitfalls.

METHODS

With respect to research and education, formal and informal sources were investigated and information was collected and presented in a qualitative manner, including also quantitative indicators when possible.

RESULTS

Greece has adopted and applied medical informatics education in various ways, including undergraduate courses in health sciences schools as well as multidisciplinary postgraduate courses. There is a continuous research effort, and large participation in EU-wide initiatives, in all the spectrum of medical informatics research, with notable scientific contributions, although technology maturation is not without barriers. Wide-scale deployment of eHealth is anticipated in the healthcare system in the near future. While ePrescription deployment has been an important step, ICT for integrated care and telehealth have a lot of room for further deployment.

CONCLUSIONS

Greece is a valuable contributor in the European medical informatics arena, and has the potential to offer more as long as the barriers of research and innovation fragmentation are addressed and alleviated.

Relation between heart beat fluctuations and cyclic alternating pattern during sleep in insomnia patients

Insomnia is a condition that affects the nervous and muscular system. Thirty percent of the population between 18 and 60 years suffers from insomnia. The effects of this disorder involve problems such as poor school or job performance and traffic accidents. In addition, patients with insomnia present changes in the cardiac function during sleep. Furthermore, the structure of electroencephalographic A-phases, which builds up the Cyclic Alternating Pattern during sleep, is related to the insomnia events. Therefore, the relationship between these brain activations (A-phases) and the autonomic nervous system would be of interest, revealing the interplay of central and autonomic activity during insomnia. With this goal, a study of the relationship between A-phases and heart rate fluctuations is presented. Polysomnography recording of five healthy subjects, five sleep misperception patients and five patients with psychophysiological insomnia were used in the study. Detrended Fluctuation Analysis (DFA) was used in order to evaluate the heart rate dynamics and this was correlated with the number of A-phases. The results suggest that pathological patients present changes in the dynamics of the heart rate. This is reflected in the modification of A-phases dynamics, which seems to modify of heart rate dynamics.

On separability of A-phases during the cyclic alternating pattern

A statistical analysis of the separability of EEG A-phases, with respect to basal activity, is presented in this study. A-phases are short central events that build up the Cyclic Alternating Pattern (CAP) during sleep. The CAP is a brain phenomenon which is thought to be related to the construction, destruction and instability of sleep stages dynamics. From the EEG signals, segments obtained around the onset and offset of the A-phases were used to evaluate the separability between A-phases and basal sleep stage oscillations. In addition, a classifier was trained to separate the different A-phase types (A1, A2 and A3). Temporal, energy and complexity measures were used as descriptors for the classifier. The results show a percentage of separation between onset and preceding basal oscillations higher than 85 % for all A-phases types. For Offset separation from following baseline, the accuracy is higher than 80 % but specificity is around 75%. Concerning to A-phase type separation, A1-phase and A3-phase are well separated with accuracy higher than 80, while A1 and A2-phases show a separation lower than 50%. These results encourage the design of automatic classifiers for Onset detection and for separating among A-phases type A1 and A3. On the other hand, the A-phase Offsets present a smooth transition towards the basal sleep stage oscillations, and A2-phases are very similar to A1-phases, suggesting that a high uncertainty may exist during CAP annotation.

Insomnia types and sleep microstructure dynamics

This work aims to investigate sleep microstructure as expressed by Cyclic Alternating Pattern (CAP), and its possible alterations in pathological sleep. Three groups, of 10 subjects each, are considered: a) normal sleep, b) psychophysiological insomnia, and c) sleep misperception. One night sleep PSG and sleep macro- micro structure annotations were available per subject. The statistical properties and the dynamics of CAP events are in focus. Multiscale and non-linear methods are presented for the analysis of the microstructure event time series, applied for each type of CAP events, and their combination. The results suggest that a) both types of insomnia present CAP differences from normal sleep related to hyperarousal, b) sleep misperception presents more extensive differences from normal, potentially reflecting multiple sleep mechanisms, c) there are differences between the two types of insomnia as regard to the intertwining of events of different subtypes. The analysis constitutes a contribution towards new markers for the quantitative characterization of insomnia, and its subtypes.

Wavelet variability of SA node originated P waves in atrial fibrillation and in signals with ectopic beats

Atrial fibrillation (AF) is one of the most common cardiac arrhythmia. Electrical properties of the atrial myocardium may be related to the appearance of this type of arrhythmia. However ectopic beats, occurring normally in healthy people, disturb cardiac rhythm. Those beats arise from fiber outside the region of SA node. With this work we aim at highlighting differences in the atrial activity between healthy subjects, healthy subjects presenting many ectopic events and patients with AF. The X-Y-Z leads of vectorcardiography recordings are considered. Wavelet-based parameters are extracted from a window which represents atrial activity originated from SA node and compared between signals of the three groups. Results show differences potentially related to the conduction system of the atrium between healthy people and people with AF, as well as between healthy people and people with ectopic events. No difference was found from the analysis of SA node beats between people with AF and healthy with ectopic events.

Nonlinear analysis of the change points between A and B phases during the Cyclic Alternating Pattern under normal sleep

This study analyzes the nonlinear properties of the EEG at transition points of the sequences that build the Cyclic Alternating Pattern (CAP). CAP is a sleep phenomenon built up by consecutive sequences of activations and non-activations observed during the sleep time. The sleep condition can be evaluated from the patterns formed by these sequences. Eleven recordings from healthy and good sleepers were included in this study. We investigated the complexity properties of the signal at the onset and offset of the activations. The results show that EEG signals present significant differences (p&#60;0.05) between activations and non-activations in the Sample Entropy and Tsallis Entropy indices. These indices could be useful in the development of automatic methods for detecting the onset and offset of the activations, leading to significant savings of the physician's time by simplifying the manual inspection task.

Two dimensional wavelet energy analysis on a beat to beat basis: application to Atrial Fibrillation

Atrial Fibrillation (AF) is a condition in which heart rhythm is not associated with normal sinoatrial (SA) node pacemaker but it derives from different areas on the atrium, often from the area of Pulmonary veins (PVs) A way to eliminate the influence of PVs in the inducement of AF is the PVs isolation surgery. In this study, an effort is made towards investigating the morphology and dynamics of P-waves, when the potentially arrhythmogenic tissue in PVs is involved or isolated via ablation. For this reason, 20 patients who were subjected to PVs isolation were studied, via vectrorcardiography recordings obtained before and after the ablation. Wavelet energies for five frequency bands were analyzed, using a two dimensional representation. The proposed technique was applied for the analysis of wavelet energies in consecutive beats, and their correlation with the RR interval. Features for the evaluation of those plots were extracted, such as the axes of a fitted to the plot ellipse and the center of the mass. The statistical analysis demonstrated significant differences between the groups, which imply the modification of the atrial substrate concerning electrical conduction toward to a more stable condition.

CAP sleep in insomnia: new methodological aspects for sleep microstructure analysis

This work aims to propose new methodologies for the quantitative characterization of insomnia. Sleep microstructure, as expressed by Cyclic Alternatic pattern (CAP) sleep, is studied and differences between normal sleepers and insomniacs are investigated. The dynamic in the structure of CAP activation events is studied by use of wavelet analysis and the content of events, i.e. EEG dynamics, is studied in terms of complexity analysis. Both in structure and content, features exhibiting statistically significant differences are proposed, opening new perspectives for the understanding and the quantitative characterization of sleep and its disorders.

A novel method to assist the detection of the Cyclic Alternating Pattern (CAP)

This study proposes a novel method to assist the detection of the components that build up the Cyclic Alternating Pattern (CAP). CAP is a sleep phenomenon formed by consecutive sequences of activations (A1, A2, A3) and non-activations during nonREM sleep. The main importance of CAP evaluation is the possibility of defining the sleep process more accurately. Ten recordings from healthy and good sleepers were included in this study. The method is based on inferential statistics to define the initial and ending points of the CAP components based only on an initialization point given by the expert. The results show concordance up to 95% for A1, 85% for A2 and 60% for A3, together with an overestimation of 1.5 s in A1, 1.3 s in A2 and 0 s in A3. The total CAP rate presents a total underestimation of 7 min. Those results suggest that the method is able to accurately detect the initial and ending points of the activations, and may be helpful for the physicians by reducing the time dedicated to the manual inspection task.

Assessment of the EEG complexity during activations from sleep

The present study quantitatively analyzes the EEG characteristics during activations (Act) that occur during NREM sleep, and constitute elements of sleep microstructure (i.e. the Cyclic Alternating Pattern). The fractal dimension (FD) and the sample entropy (SampEn) measures were used to study the different sleep stages and the Act that build up the sleep structure. Polysomnographic recordings from 10 good sleepers were analyzed. The complexity indexes of the Act were compared with the non-activation (NAct) periods during non-REM sleep. In addition, complexity measures among the different Act subtypes (A1, A2 and A3) were analyzed. A3 presented a quite similar complexity independently of the sleep stage, while A1 and A2 showed higher complexity in light sleep than during deep sleep. The current results suggest that Act present a hierarchic complexity between subtypes A3 (higher), A2 (intermediate) and A1 (lower) in all sleep stages.

Predicting EEG complexity from sleep macro and microstructure

This work investigates the relation between the complexity of electroencephalography (EEG) signal, as measured by fractal dimension (FD), and normal sleep structure in terms of its macrostructure and microstructure. Sleep features are defined, encoding sleep stage and cyclic alternating pattern (CAP) related information, both in short and long term. The relevance of each sleep feature to the EEG FD is investigated, and the most informative ones are depicted. In order to quantitatively assess the relation between sleep characteristics and EEG dynamics, a modeling approach is proposed which employs subsets of the sleep macrostructure and microstructure features as input variables and predicts EEG FD based on these features of sleep micro/macrostructure. Different sleep feature sets are investigated along with linear and nonlinear models. Findings suggest that the EEG FD time series is best predicted by a nonlinear support vector machine (SVM) model, employing both sleep stage/transitions and CAP features at different time scales depending on the EEG activation subtype. This combination of features suggests that short-term and long-term history of macro and micro sleep events interact in a complex manner toward generating the dynamics of sleep.

EEG complexity during sleep: on the effect of micro and macro sleep structure

This work investigates the relation between EEG complexity measures, in particular Fractal Dimension and Sample Entropy, and sleep structure, in terms of both macrostructure, i.e. sleep stages, and microstructure, i.e. phase A activation of CAP sleep. Activation phases are compared with the non-activation periods of non-REM sleep. The study suggests that complexity features can serve as consistent descriptors of sleep dynamics and can potentially assist in the classification of sleep stages.

Beat to beat wavelet variability in atrial fibrillation

Atrial fibrillation (AF) is a complex phenomenon, related with a multitude of factors, including the electrical properties of the atrial substrate. The purpose of this work is to present a method that highlights electrocardiographic differences between normal subjects and patients with paroxysmal AF episodes (PAF), potentially related with substrate differences. Vectorcardiography recordings are considered and, for each lead (X-Y-Z), on a beat by beat basis, a steady window before QRS, corresponding to the atrial activity, is analysed via continuous wavelet transform. Wavelet-based parameters are calculated and compared between the normal and AF group, with the beat to beat variation of wavelet energy as the most important feature showing a significantly higher variability in the AF group.

The Healthgrid White Paper

Over the last four years, a community of researchers working on Grid and High Performance Computing technologies started discussing the barriers and opportunities that grid technologies must face and exploit for the development of health-related applications. This interest lead to the first Healthgrid conference, held in Lyon, France, on January 16th-17th, 2003, with the focus of creating increased awareness about the possibilities and advantages linked to the deployment of grid technologies in health, ultimately targeting the creation of a European/international grid infrastructure for health. The topics of this conference converged with the position of the eHealth division of the European Commission, whose mandate from the Lisbon Meeting was "To develop an intelligent environment that enables ubiquitous management of citizens' health status, and to assist health professionals in coping with some major challenges, risk management and the integration into clinical practice of advances in health knowledge." In this context "Health" involves not only clinical procedures but covers the whole range of information from molecular level (genetic and proteomic information) over cells and tissues, to the individual and finally the population level (social healthcare). Grid technology offers the opportunity to create a common working backbone for all different members of this large "health family" and will hopefully lead to an increased awareness and interoperability among disciplines. The first HealthGrid conference led to the creation of the Healthgrid association, a non-profit research association legally incorporated in France but formed from the broad community of European researchers and institutions sharing expertise in health grids. After the second Healthgrid conference, held in Clermont-Ferrand on January 29th-30th, 2004, the need for a "white paper" on the current status and prospective of health grids was raised. Over fifty experts from different areas of grid technologies, eHealth applications and the medical world were invited to contribute to the preparation of this document.

Grid-enabled biosensor networks for pervasive healthcare

Current advances in biosensor technology allow multiple miniaturized or textile sensors to record continuously biosignals, such as blood pressure or heart rate, and transmit the information of interest to clinical sites. New applications are emerging, based on such systems, towards pervasive healthcare. This paper describes an architecture enabling biosensors, forming a Body Area Network (BAN), to be integrated in a Grid infrastructure. The Grid services proposed, such as access to recorded data, are offered via the BAN console, an enhanced wearable computer, where the recordings of multiple biosensors are integrated. Medical Grid-enabled Nodes can have access to biosensor measurements upon demand, or can agree to get notifications and alerts. Thus, in such a distributed environment, data and computational resources are independent, yet cooperating unobtrusively, contributing to the notion of pervasive healthcare.