Wearable pulmonary monitoring system with integrated functional lung imaging and chest sound recording: a clinical investigation in healthy subjects.

Objective.Current wearable respiratory monitoring devices provide a basic assessment of the breathing pattern of the examined subjects. More complex monitoring is needed for healthcare applications in patients with lung diseases. A multi-sensor vest allowing continuous lung imaging by electrical impedance tomography (EIT) and auscultation at six chest locations was developed for such advanced application. The aims of our study were to determine the vest's capacity to record the intended bio-signals, its safety and the comfort of wearing in a first clinical investigation in healthy adult subjects.Approach.Twenty subjects (age range: 23-65 years) were studied while wearing the vests during a 14-step study protocol comprising phases of quiet and deep breathing, slow and forced full expiration manoeuvres, coughing, breath-holding in seated and three horizontal postures. EIT, chest sound and accelerometer signals were streamed to a tablet using a dedicated application and uploaded to a back-end server. The subjects filled in a questionnaire on the vest properties using a Likert scale.Main results.All subjects completed the full protocol. Good to excellent EIT waveforms and functional EIT images were obtained in 89% of the subjects. Breathing pattern and posture dependent changes in ventilation distribution were properly detected by EIT. Chest sounds were recorded in all subjects. Detection of audible heart sounds was feasible in 44%-67% of the subjects, depending on the sensor location. Accelerometry correctly identified the posture in all subjects. The vests were safe and their properties positively rated, thermal and tactile properties achieved the highest scores.Significance.The functionality and safety of the studied wearable multi-sensor vest and the high level of its acceptance by the study participants were confirmed. Availability of personalized vests might further advance its performance by improving the sensor-skin contact.

Identification and analysis of stable breathing periods in electrical impedance tomography recordings.

Objective. In this paper, an automated stable tidal breathing period (STBP) identification method based on processing electrical impedance tomography (EIT) waveforms is proposed and the possibility of detecting and identifying such periods using EIT waveforms is analyzed. In wearable chest EIT, patients breathe spontaneously, and therefore, their breathing pattern might not be stable. Since most of the EIT feature extraction methods are applied to STBPs, this renders their automatic identification of central importance.Approach. The EIT frame sequence is reconstructed from the raw EIT recordings and the raw global impedance waveform (GIW) is computed. Next, the respiratory component of the raw GIW is extracted and processed for the automatic respiratory cycle (breath) extraction and their subsequent grouping into STBPs.Main results. We suggest three criteria for the identification of STBPs, namely, the coefficient of variation of (i) breath tidal volume, (ii) breath duration and (iii) end-expiratory impedance. The total number of true STBPs identified by the proposed method was 294 out of 318 identified by the expert corresponding to accuracy over 90%. Specific activities such as speaking, eating and arm elevation are identified as sources of false positives and their discrimination is discussed.Significance. Simple and computationally efficient STBP detection and identification is a highly desirable component in the EIT processing pipeline. Our study implies that it is feasible, however, the determination of its limits is necessary in order to consider the implementation of more advanced and computationally demanding approaches such as deep learning and fusion with data from other wearable sensors such as accelerometers and microphones.

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 1st 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.

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.

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.

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.

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.

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.

Multiparametric modeling of the ineffective efforts in assisted ventilation within an ICU.

In the context of assisted ventilation in ICU, it is of vital importance to keep a high synchronization between the patient's attempt to breath and the assisted ventilation event, so that the patient receives the ventilation support requested. In this work, experimental equipment is employed, which allows for unobtrusive and continuous monitoring of a multiple relevant bioparameters. These are meant to guide the medical professionals in appropriately adapting the treatment and fine-tune the ventilation. However, synchronization phenomena of different origin (neurological, mechanical, ventilation parameters) may occur, which vary among patients, and during the course of monitoring of a single patient, the timely recognition of which is challenging even for experts. The dynamics and complex causal relations among bioparameters and the ventilation synchronization are not well studied. The purpose of this work is to elaborate on a methodology toward modeling the ventilation synchronization failures based on the evolution of monitored bioparameters. Principal component analysis is employed for the transformation into a small number of features and the investigation of repeating patterns and clusters within measurements. Using these features, nonlinear prediction models based on support vector machines regression are explored, in terms of what past knowledge is required and what is the future horizon that can be predicted. The proposed model shows good correlation (over 0.74) with the actual outputs, constituting an encouraging step toward understanding of ICU ventilation dynamic phenomena.

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.

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.

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.

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.

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.

From adverse drug event detection to prevention. A novel clinical decision support framework for medication safety.

BACKGROUND: Errors related to medication seriously affect patient safety and the quality of healthcare. It has been widely argued that various types of such errors may be prevented by introducing Clinical Decision Support Systems (CDSSs) at the point of care. OBJECTIVES: Although significant research has been conducted in the field, still medication safety is a crucial issue, while few research outcomes are mature enough to be considered for use in actual clinical settings. In this paper, we present a clinical decision support framework targeting medication safety with major focus on adverse drug event (ADE) prevention. METHODS: The novelty of the framework lies in its design that approaches the problem holistically, i.e., starting from knowledge discovery to provide reliable numbers about ADEs per hospital or medical unit to describe their consequences and probable causes, and next employing the acquired knowledge for decision support services development and deployment. Major design features of the framework's services are: a) their adaptation to the context of care (i.e. patient characteristics, place of care, and significance of ADEs), and b) their straightforward integration in the healthcare information technologies (IT) infrastructure thanks to the adoption of a service-oriented architecture (SOA) and relevant standards. RESULTS: Our results illustrate the successful interoperability of the framework with two commercially available IT products, i.e., a Computerized Physician Order Entry (CPOE) and an Electronic Health Record (EHR) system, respectively, along with a Web prototype that is independent of existing healthcare IT products. The conducted clinical validation with domain experts and test cases illustrates that the impact of the framework is expected to be major, with respect to patient safety, and towards introducing the CDSS functionality in practical use. CONCLUSIONS: This study illustrates an important potential for the applicability of the presented framework in delivering contextualized decision support services at the point of care and for making a substantial contribution towards ADE prevention. Nonetheless, further research is required in order to quantitatively and thoroughly assess its impact in medication safety.

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.

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.

Temperature multiscale entropy analysis: a promising marker for early prediction of mortality in septic patients.

A few studies estimating temperature complexity have found decreased Shannon entropy, during severe stress. In this study, we measured both Shannon and Tsallis entropy of temperature signals in a cohort of critically ill patients and compared these measures with the sequential organ failure assessment (SOFA) score, in terms of intensive care unit (ICU) mortality. Skin temperature was recorded in 21 mechanically ventilated patients, who developed sepsis and septic shock during the first 24 h of an ICU-acquired infection. Shannon and Tsallis entropies were calculated in wavelet-based decompositions of the temperature signal. Statistically significant differences of entropy features were tested between survivors and non-survivors and classification models were built, for predicting final outcome. Significantly reduced Tsallis and Shannon entropies were found in non-survivors (seven patients, 33%) as compared to survivors. Wavelet measurements of both entropy metrics were found to predict ICU mortality better than SOFA, according to a combination of area under the curve, sensitivity and specificity values. Both entropies exhibited similar prognostic accuracy. Combination of SOFA and entropy presented improved the outcome of univariate models. We suggest that reduced wavelet Shannon and Tsallis entropies of temperature signals may complement SOFA in mortality prediction, during the first 24 h of an ICU-acquired infection.

A pervasive health system integrating patient monitoring, status logging and social sharing.

In this paper, we present the design and development of a pervasive health system enabling self-management of chronic patients during their everyday activities. The proposed system integrates patient health monitoring, status logging for capturing various problems or symptoms met, and social sharing of the recorded information within the patients community, aiming to facilitate disease management. A prototype is implemented on a mobile device illustrating the feasibility and applicability of the presented work by adopting unobtrusive vital signs monitoring through a wearable multi-sensing device, a service oriented architecture for handling communication issues, and popular micro-blogging services. Furthermore, a study has been conducted with 16 hypertensive patients, in order to investigate the user acceptance, the usefulness, and the virtue of the proposed system. The results show that the system is welcome by the chronic patients who are especially willing to share healthcare information, and easy to learn and use, while its features have been overall regarded by the patients as helpful for their disease management and treatment.