Characterization of heart rate variability and oxygen saturation in sepsis patients

One of the most major and common health crises which occur across all the hospitals, worldwide, is seen to be sepsis that occurs in patients. However, despite its wide prevalence no novel tool has been devised for predicting its occurrence. An accurate and early prediction of sepsis in the patients could significantly help the physicians administer proper treatment and decrease the uncertain diagnosis. Some machine-learning-based models or schemes can help in identifying the potential clinical variables and display a better performance compared to the prevailing conventional low-performance models. In this study, a machine learning-based scheme for fast and accurate sepsis identification was proposed. This scheme employed the power spectrum and mean estimation for data record intervals, which were then classified for reaching the final decision. For a 72-h interval, the obtained detection accuracy was 94.2% that shows very good sign to use it as a fast and robust sepsis identification.