Computing network-based features from physiological time series: application to sepsis detection
Autor: | Sabato Santaniello, Sridevi V. Sarma, Stephen J. Granite, Raimond L. Winslow |
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Rok vydání: | 2015 |
Předmět: |
Adult
medicine.medical_specialty Databases Factual Sepsis Correlation Electrocardiography Intensive care Medicine Humans Intensive care medicine Connectivity Monitoring Physiologic Aged 80 and over Series (stratigraphy) medicine.diagnostic_test business.industry Disease progression Signal Processing Computer-Assisted medicine.disease Intensive Care Units Disease Progression Graph (abstract data type) Female Neural Networks Computer business Algorithms |
Zdroj: | EMBC |
ISSN: | 2694-0604 |
Popis: | Sepsis is a systemic deleterious host response to infection. It is a major healthcare problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by using static scores derived from bed-side measurements individually, i.e., without systematically accounting for potential interactions between these signals and their dynamics. In this study, we apply network-based data analysis to take into account interactions between bed-side physiological time series (PTS) data collected in ICU patients, and we investigate features to distinguish between sepsis and non-sepsis conditions. We treated each PTS source as a node on a graph and we retrieved the graph connectivity matrix over time by tracking the correlation between each pair of sources' signals over consecutive time windows. Then, for each connectivity matrix, we computed the eigenvalue decomposition. We found that, even though raw PTS measurements may have indistinguishable distributions in non-sepsis and early sepsis states, the median /I of the eigenvalues computed from the same data is statistically different (p |
Databáze: | OpenAIRE |
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