Student project

Forecasting respiratory rate in sedated patients with partial least squares regression

Remifentanil, like the other sedative drugs used to achieve conscious sedation, has an impact on the efficiency of patient ventilation. Since the quality of breathing has an indirect effect on blood O2 and CO2 concentrations, keeping patients' blood homeostasis relies on anesthetists' and nurses' vigilence. The current state of patient monitoring during conscious sedation can detect unsuccessful respirations only after they have occurred. Hence, there is a clinical need for a signal classifier to predict potential respiration failure in sedated patients. Given that the physiological changes involved in this context are transitory, and that opioids affect the Central Nervous System, the hypothesis is that opioids' effect on a patient drive to breath will result in detectable changes in electrocardiogram and capnogram signals. The purpose of this project is to design a signal classiffier to predict respiratory rate (RR) based on the analysis of the ECG spectrum and capnogram shape. Using patient audit data, a modeling set was created. Power Spectral Density calculation on a sliding Hamming window was the feature extraction used on ECG. A custom filter was designed to extract values describing the shape of each breaths in terms of slope, curvature and integral of the capnographic trace. Both patient specific (single user) and robust (mulit-user) solution were investigated. PLS regression resulted in parsimonious patient specific models capable of predicting the respiratory rate with an average accuracy of 90%. The performance, however, dropped for low RR. The analysis of the weights showed that the frequencies under 7.5Hz contain the majority of the information. Several limitations are addressed in the building of a robust multi-user model. The results obtained suggest an existing patient-specific correlation between the frequency content of ECG and the upcoming RR. Further investigation is required to achieve clinically useful prediction


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