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Background <p>Cardiogenic oscillations in airflow can cause ventilator autotriggering during pressure support ventilation, potentially leading to inappropriate hyperventilation. A method to attenuate these oscillations in real time may help reduce autotriggering.</p> Materials and methods <p>High-resolution airflow and ECG signals were collected from intubated surgical patients receiving pressure support ventilation. Singular spectrum analysis (SSA) was applied in a sliding-window format to generate a smoothed respiratory waveform. We quantified attenuation of cardiogenic oscillations using ECG-aligned timing, frequency-domain analysis, and reduction in cardiac-frequency spectral power. Waveform fidelity was assessed using respiratory-envelope correlation and root-mean-square error (RMSE). Computational feasibility was evaluated by measuring processing time per window.</p> Results <p>SSA substantially reduced cardiac-frequency spectral power (82–87% reduction) while preserving respiratory structure (correlation with respiratory envelope 0.92–0.94). Reconstruction error was modest (RMSE 0.08–0.11 normalized units). Computation time per 6-s window was 14–22&#xa0;ms, supporting potential real-time use. Attenuation performance remained stable during changes in respiratory rate.</p> Conclusions <p>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.</p>

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Removal of cardiogenic oscillations during pressure support ventilation using sliding window singular spectrum analysis: proof-of-concept

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月5日

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  • Parwane P. Pagano,
  • Edward J. Ciaccio

摘要

Background

Cardiogenic oscillations in airflow can cause ventilator autotriggering during pressure support ventilation, potentially leading to inappropriate hyperventilation. A method to attenuate these oscillations in real time may help reduce autotriggering.

Materials and methods

High-resolution airflow and ECG signals were collected from intubated surgical patients receiving pressure support ventilation. Singular spectrum analysis (SSA) was applied in a sliding-window format to generate a smoothed respiratory waveform. We quantified attenuation of cardiogenic oscillations using ECG-aligned timing, frequency-domain analysis, and reduction in cardiac-frequency spectral power. Waveform fidelity was assessed using respiratory-envelope correlation and root-mean-square error (RMSE). Computational feasibility was evaluated by measuring processing time per window.

Results

SSA substantially reduced cardiac-frequency spectral power (82–87% reduction) while preserving respiratory structure (correlation with respiratory envelope 0.92–0.94). Reconstruction error was modest (RMSE 0.08–0.11 normalized units). Computation time per 6-s window was 14–22 ms, supporting potential real-time use. Attenuation performance remained stable during changes in respiratory rate.

Conclusions

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.