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. (2026) conducted an 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.