Clinical deterioration detection for continuous vital signs monitoring using wearable sensors

Surgical patients are at risk of experiencing clinical deterioration events, especially when transferred to general wards during the postoperative period of their hospital stay. Cur rently, such events are detected by combining Early Warning Scores (EWS) with manual and periodical vital signs measurements, performed by nurses every 4 to 6 hours. Hence, deterioration may remain unnoticed for hours, delaying patient treatment, which might lead to increased morbidity and mortality. Also, EWS are inadequate to predict events so physiologically complex. So that early warning of deterioration could be provided, it was investigated the potential of warning systems that combine machine learning-based prediction models with continuous vital signs monitoring, provided by wearable sensors. This dissertation presents the development of such a warning system, fully indepen dent of manual measurements and based on a logistic regression prediction model with 85% sensitivity, 79% precision and 98% specificity. Additionally, a new personalized ap proach to handle missing data periods in vital signs and a novel variation of a RR-interval preprocessing technique were developed. The results obtained revealed a relevant im provement in the detection of deterioration events and a significant reduction in false alarms, when comparing the warning system with a commonly employed EWS (42% sensitivity, 14% precision and 90% specificity). It was also found that the developed sys tem can assess patient’s condition much more frequently and with timely deterioration detection, without even requiring nurses to interrupt their workflow. These findings sup port the idea that these warning systems are reliable, more practical, more appropriate and produce smarter alarms than current methods, making early deterioration detection possible, thus contributing for better patients outcomes. Nonetheless, the performance achieved may yet reveal insufficient for application in real clinical contexts. Therefore, further work is necessary to improve prediction performance to a greater extent and to confirm these systems reliability

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation

Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm’s performance was compared versus the gold standard (the ventilator’s waveform recordings for CP-VI were scored visually by three experts; Fleiss’ kappa = 0.90 (0.87–0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient’s own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78–0.86) and 0.78 (0.78–0.85), and accuracies of 0.93 (0.89–0.93) and 0.89 (0.89–0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.This work was funded by projects PI16/01606, integrated in the Plan Nacional de R+D+I and co-funded by the ISCIII- Subdirección General de Evaluación y el Fondo Europeo de Desarrollo Regional (FEDER). RTC-2017-6193-1 (AEI/FEDER UE). CIBER Enfermedades Respiratorias, and Fundació Parc Taulí

Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis

Multivariate Entropy quantification algorithms are becoming a prominent tool for the extraction of information from multi-channel physiological time-series. However, in the analysis of physiological signals from heterogeneous organ systems, certain channels may overshadow the patterns of others, resulting in information loss. Here, we introduce the framework of Stratified Entropy to prioritize each channels' dynamics based on their allocation to respective strata, leading to a richer description of the multi-channel time-series. As an implementation of the framework, three algorithmic variations of the Stratified Multivariate Multiscale Dispersion Entropy are introduced. These variations and the original algorithm are applied to synthetic time-series, waveform physiological time-series, and derivative physiological data . Based on the synthetic time-series experiments, the variations successfully prioritize channels following their strata allocation while maintaining the low computation time of the original algorithm. In experiments on waveform physiological time-series and derivative physiological data, increased discrimination capacity was noted for multiple strata allocations in the variations when benchmarked to the original algorithm. This suggests improved physiological state monitoring by the variations. Furthermore, our variations can be modified to utilize a priori knowledge for the stratification of channels. Thus, our research provides a novel approach for the extraction of previously inaccessible information from multi-channel time series acquired from heterogeneous systems

The fluctuation behavior of heart and respiratory system signals as a quantitative tool for studying long-term environmental exposures and chronic diseases

Background: Several studies over the last decades have suggested that a wide range of disease states, as well as the aging process itself, are marked by progressive impairment of the involved physiological processes to adapt, resulting in a loss of complexity in the dynamics of physiological functions. Therefore, measuring complexity from physiological system signals holds enormous promise for providing a new understanding of the mechanisms underlying physiological systems and how they change with diseases and aging. Furthermore, since physiological systems are continuously exposed to environmental factors, measuring how physiological complexity changes during exposure to environmental elements might also provide new insights into their effects. Indeed, this approach may be able to unveil subtle but important changes in the regulatory mechanisms of physiological systems not detectable by traditional analysis methods. Objectives: The overall objective of this PhD thesis was to quantify the complexity of the dynamics of heart and respiratory system signals, in order to investigate how this complexity changes with long-term environmental exposures and chronic diseases, using data from large epidemiological and clinical studies, in order to control for most potential confounders of the fluctuation behavior of systems signals (e.g., demographic, environmental, clinical, and lifestyle factors). We specifically aimed (1) at assessing the influence, first, of long-term smoking cessation, and second, of long-term exposure to traffic-related particulate matter of less than 10 micrometers in diameter (TPM10), on the regulation of the autonomic cardiovascular system and heart rate dynamics in an aging general population, using data from the SAPALDIA cohort study; (2) to assess whether the subgrouping of patients with recurrent obstructive airway diseases, including mild-to-moderate asthma, severe asthma, and COPD, according to their pattern of lung function fluctuation, allows for the identification of phenotypes with specific treatable traits, using data from the BIOAIR study. Methods: In the SAPALDIA cohort, a population-based Swiss cohort, 1608 participants ≥ 50 years of age underwent ambulatory 24-hr electrocardiogram monitoring and reported on lifestyle and medical history. In each participant, heart rate variability and heart rate dynamics were characterized by means of various quantitative analyses of the inter-beat interval time series generated from 24-hour electrocardiogram recordings. Each parameter obtained was then used as the outcome variable in multivariable linear regression models in order to evaluate the association with (1) smoking status and time elapsed since smoking cessation; (2) long-term exposure to TPM10. The models were adjusted for known confounding factors. In the BIOAIR study, we conducted a time series clustering analysis based on the fluctuation of twice-daily FEV1 measurements recorded over a one year period in a mixed group of 134 adults with mild-to-moderate asthma, severe asthma, or COPD from the longitudinal Pan-European BIOAIR study. Results: In the SAPALDIA cohort, our findings indicate that smoking triggers adverse changes in the regulation of the cardiovascular system, even at low levels of exposure since current light smokers exhibited significant changes as compared to lifelong non-smokers. Moreover, there was evidence for a dose-response effect. Furthermore, full recovery was achieved in former smokers (i.e., normalization to the level of lifelong non-smokers). However, while light smokers fully recovered within the 15 first years of cessation, heavy former smokers might need up to 15-25 years to fully recover. Regarding long-term exposure to TPM10, we did not observe an overall association with heart rate variability/heart rate dynamics in the entire study population. However, significant changes in the heart rate dynamics were found in subjects without cardiovascular morbidity and significant changes, both in the heart rate dynamics and in the heart rate variability, were found in non-obese subjects without cardiovascular morbidity. Furthermore, subjects with homozygous GSTM1 gene deletion appeared to be more susceptible to the effects of TPM10. In the BIOAIR study, we identified five phenotypes, of those three distinct phenotypes of severe asthma, in which the progressive functional alteration of the lung corresponded to a gradually increasing clinical severity and translated into specific risks of exacerbation and treatment response features. Conclusions: This thesis hopes to demonstrate the importance of multidimensional approaches to gain understanding in the complex functioning of the human physiological system and of disease processes. Characterization of the complexity in the fluctuation behavior of system signals holds enormous promise for providing new understandings of the regulatory mechanisms of physiological systems and how they change with diseases. However, it is important to combine this kind of approach with classical epidemiological approaches in order to disentangle the various contributions of the intrinsic physiological dynamics, aging, diseases and comorbidities, lifestyle, and environment. In the SAPALDIA cohort study, we were able to disentangle the influence of specific environmental exposures, such as particulate matter air pollution and smoking exposure, on the heart rate variability and heart rate dynamics, and thus to unveil long-term alterations in former heavy smokers, as well as adverse effects of low level, but long-term, exposure to TPM10 in healthy subjects and in subjects with homozygous GSTM1 gene deletion. In the BIOAIR study, we provide evidence that airway dynamics contain substantial information, which enables the identification of clinically meaningful phenotypes, in which the functional alteration of the lung translates into specific treatable traits

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks