Sliding-window SSA reduced cardiogenic oscillations in airflow signals by 82-87% while preserving respiratory waveform with correlation of 0.92-0.94 in adult intubated surgical patients during pressure support ventilation.
Observational (n=2)
No
Does sliding-window singular spectrum analysis attenuate cardiogenic oscillations in airflow signals of intubated patients on pressure support ventilation?
Sliding-window singular spectrum analysis effectively filters cardiogenic oscillations from airflow signals in real-time, offering a potential software solution to prevent ventilator autotriggering.
Effect estimate: 82-87% reduction in cardiac-frequency spectral power
Sliding-window SSA attenuated cardiogenic oscillations in patient airflow signals and preserved the dominant respiratory pattern. As a proof-of-concept, this approach shows potential for integration into autotrigger-suppression logic, though further validation in larger and more diverse populations is required.
Pagano et al. (Thu,) conducted a observational in Adult intubated patients under general anesthesia receiving pressure support ventilation during non-cardiac surgery (n=2). Sliding-window singular spectrum analysis (SSA) applied to airflow signals vs. Original raw airflow signals without SSA filtering was evaluated on Attenuation of cardiogenic oscillations assessed by reduction in cardiac-frequency spectral power and preservation of respiratory waveform morphology (82-87% reduction in cardiac-frequency spectral power). Sliding-window SSA reduced cardiogenic oscillations in airflow signals by 82-87% while preserving respiratory waveform with correlation of 0.92-0.94 in adult intubated surgical patients during pressure support ventilation.