Toronto Metropolitan University
A_Singular_Spectrum_Analysis-Based_Data-Driven_Technique_for_the_Removal_of_Cardiogenic_Oscillations_in_Esophageal_Pressure_Signals.pdf (2.06 MB)

A Singular Spectrum Analysis-Based Data-Driven Technique for the Removal of Cardiogenic Oscillations in Esophageal Pressure Signals

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journal contribution
posted on 2022-10-29, 17:56 authored by Sourav Kumar Mukhopadhyay, Michael Zara, Irene Telias, Lu Chen, Rémi Coudroy, Takeshi YoshidaTakeshi Yoshida, Laurent Brochard, Sridhar KrishnanSridhar Krishnan

Objective: Assessing the respiratory and lung mechanics of the patients in intensive care units is of utmost need in order to guide the management of ventilation support. The esophageal pressure (Peso) signal is a minimally invasive measure, which portrays the mechanics of the lung and the pattern of breathing. Because of the close proximity of the lung to the beating heart inside the thoracic cavity, the Peso signals always get contaminated with that of the oscillatory-pressure-signal of the heart, which is known as the cardiogenic oscillation (CGO) signal. However, the area of research addressing the removal of CGO from Peso signal is still lagging behind. Methods and results: This paper presents a singular spectrum analysis-based high-efficient, adaptive and robust technique for the removal of CGO from Peso signal utilizing the inherent periodicity and morphological property of the Peso signal. The performance of the proposed technique is tested on Peso signals collected from the patients admitted to the intensive care unit, cadavers, and also on synthetic Peso signals. The efficiency of the proposed technique in removing CGO from the Peso signal is

quantified through both qualitative and quantitative measures, and the mean opinion scores of the denoised Peso signal fall under the categories ‘very good’ as per the subjective measure. Conclusion and clinical impact: The proposed technique: (1) does not follow any predefined mathematical model and hence, it is data-driven, (2) is adaptive to the sampling rate, and (3) can be adapted for denoising other biomedical signals which exhibit periodic or quasi-periodic nature.




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