Early hospital mortality prediction using vital signals

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE 2018 and published in Smart Health journa

Nonlinear heart rate variability features for real-life stress detection. Case study : students under stress due to university examination

Background: This study investigates the variations of Heart Rate Variability (HRV) due to a real-life stressor and proposes a classifier based on nonlinear features of HRV for automatic stress detection. Methods: 42 students volunteered to participate to the study about HRV and stress. For each student, two recordings were performed: one during an on-going university examination, assumed as a real-life stressor, and one after holidays. Nonlinear analysis of HRV was performed by using Poincaré Plot, Approximate Entropy, Correlation dimension, Detrended Fluctuation Analysis, Recurrence Plot. For statistical comparison, we adopted the Wilcoxon Signed Rank test and for development of a classifier we adopted the Linear Discriminant Analysis (LDA). Results: Almost all HRV features measuring heart rate complexity were significantly decreased in the stress session. LDA generated a simple classifier based on the two Poincaré Plot parameters and Approximate Entropy, which enables stress detection with a total classification accuracy, a sensitivity and a specificity rate of 90%, 86%, and 95% respectively. Conclusions: The results of the current study suggest that nonlinear HRV analysis using short term ECG recording could be effective in automatically detecting real-life stress condition, such as a university examination

Contextual Motifs: Increasing the Utility of Motifs using Contextual Data

Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. Thus, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, oftentimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to better predictions of hypo- and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long- and short-term prediction horizon when processing and interpreting physiological waveform data.Comment: 10 pages, 7 figures, accepted for oral presentation at KDD '1

Cardiovascular Consequences of Unfair Pay

This paper investigates physiological responses to perceptions of unfair pay. In a simple principal agent experiment agents produce revenue by working on a tedious task. Principals decide how this revenue is allocated between themselves and their agents. In this environment unfairness can arise if an agent's reward expectation is not met. Throughout the experiment we record agents' heart rate variability. Our findings provide evidence of a link between perceived unfairness and heart rate variability.The latter is an indicator of stressrelated impaired cardiac autonomic control, which has been shown to predict coronary heart diseases in the long run. Establishing a causal link between unfair pay and heart rate variability therefore uncovers a mechanism of how perceptions of unfairness can adversely affect cardiovascular health. Wefurther test potential adverse health effects of unfair pay using data from a large representative data set. Complementary to our experimental findings we find a strong and highly significant association between health outcomes, in particular cardiovascular health, and fairness of pay.Fairness, social preferences, inequality, heart rate variability, health, experiments, SOEP

Cardiovascular Consequences of Unfair Pay

This paper investigates physiological responses to perceptions of unfair pay. In a simple principal agent experiment agents produce revenue by working on a tedious task. Principals decide how this revenue is allocated between themselves and their agents. In this environment unfairness can arise if an agent's reward expectation is not met. Throughout the experiment we record agents' heart rate variability. Our findings provide evidence of a link between perceived unfairness and heart rate variability. The latter is an indicator of stress-related impaired cardiac autonomic control, which has been shown to predict coronary heart diseases in the long run. Establishing a causal link between unfair pay and heart rate variability therefore uncovers a mechanism of how perceptions of unfairness can adversely affect cardiovascular health. We further test potential adverse health effects of unfair pay using data from a large representative data set. Complementary to our experimental findings we find a strong and highly significant association between health outcomes, in particular cardiovascular health, and fairness of pay.fairness, social preferences, inequality, heart rate variability, health, experiments, SOEP

Respiratory parameters at varied altitudes in intermittent mining work

Objectives: Workers in the mining industry in altitude are subjected to several risk factors, e.g., airborne silica and low barometric pressure. The aim of this study has been to assess the risks for this work category, evaluating single risk factors as airborne silica, altitude and work shift, and relating them with cardiovascular and ventilatory parameters. Material and Methods: Healthy miners employed in a mining company, Chile, working at varied altitudes, and subjected to unusual work shifts, were evaluated. Cardiovascular and respiratory parameters were investigated. Exposure to airborne silica was evaluated and compared to currently binding exposure limits. Results: At varied altitudes and work shifts, alterations emerged in haemoglobin, ventilation and respiratory parameters, related to employment duration, due to compensatory mechanisms for hypoxia. Haemoglobin increased with altitude, saturation fell down under 90% in the highest mines. The multiple linear regression analysis showed a direct relationship, in the higher mine, between years of exposure to altitude and increased forced vital capacity percent (FVC%), and forced expiratory volume in 1 s (FEV1). An inverse relationship emerged between forced vital capacity (FVC) and years of exposure to airborne silica. In the workplace Mina Subterrànea (MT-3600), statistically significant inverse relationship emerged between the Tiffeneau index and body weight. Conclusions: The working conditions in the mining industry in altitude appeared to be potentially pathogenic; further investigations should be realized integrating risk assessment protocols even in consideration of their undeniable unconventionality

Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis

Recently, different algorithms have been suggested to improve Sample Entropy (SE) performance. Although new methods for calculating SE have been proposed, so far improving the efficiency (computational time) of SE calculation methods has not been considered. This research shows such an analysis of calculating a correlation between Electroencephalogram(EEG) and Heart Rate Variability(HRV) based on their SE values. Our results indicate that the parsimonious outcome of SE calculation can be achieved by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain.Peer reviewe

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions