Heart Rate Vital Signs Research Articles

Evaluation of the Impact of Data Uncertainty on the Prediction of Physiological Patient Deterioration

In this paper, we investigate the impact of data uncertainty on the prediction of a vital sign of abnormalities. We analyze the evolution of mean arterial blood pressure (MAP) and heart rate (HR) vital signs time series over a given forecasting window, whose values are classified according to a model of normality that models patient’s physiological deterioration. To this end, we developed a generic prediction framework that enables the deployment of different methods for data preparation, training, prediction, and classification so as to perform a set of experiments. To address our purpose, we selected a general regression neural network model (to predict vital sign trends) and a support vector machine (to recognize abnormalities) as the appropriate methods for a baseline analysis. However, other algorithm choices could be tailored in the defined prediction framework. We extract from the multiparameter intelligent monitoring in intensive care II waveform database a set of 302, 940 vital sign samples (organized as 459 records) to define training and testing data sets. From our experiments, we obtained evidence demonstrating the incidence of null values in the MAP/HR input time series on forecasting tasks. This was performed under different conditions in terms of null values in the observation window, and varying other factors like labeling methods and forecast window sizes. In our experiments, we concentrate on analyzing the extent of prediction performance under such conditions and identify that a tolerable level of null values is around 25% where it is possible to still derive forecast windows with an accuracy target $\geq 0.9$ for both MAP and HR vital signs. The presented evaluation contributes to acknowledge the importance of uncertainty conditions on the prediction performance of vital signs and paves the way for future extensions involving other prediction approaches.

Measurement of cardiovascular and pulmonary function endpoints and other physiological effects following partial or complete substitution of cigarettes with electronic cigarettes in adult smokers

Acute changes in select physiological parameters associated with cardiovascular physiology (systolic and diastolic blood pressure (BP) and heart rate (HR)), pulmonary function (FVC, FEV1, and exhaled CO and NO) and adverse events were measured in 105 clinically confined subjects who were randomized into groups that either completely or partially switched from conventional cigarettes to e-cigarettes or completely discontinued using tobacco and nicotine products altogether. Use of the e-cigarettes for five days under the various study conditions did not lead to higher BP or HR values, negative respiratory health outcomes or serious adverse health events. Reductions in BP and HR vital signs were observed in most of the participants that either ceased tobacco and nicotine products use altogether or switched completely to using e-cigarettes. Pulmonary function tests showed small but non-statistically significant improvements in FVC and FEV1 measurements in most use groups. Statistically significant (p < 0.05) benefits associated with smoking reduction were also noted in exhaled CO and NO levels. All study products were well tolerated. The study findings suggest that there are potential cardiovascular and pulmonary function benefits when smokers switch to using e-cigarette products. This further reinforces the potential that e-cigarettes offer smokers seeking an alternative to conventional tobacco products.