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

Abstract

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 () 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 signals always get contaminated with that of the oscillatory-pressure-signal of the heart, which is known as the cardiogenic oscillation () signal. However, the area of research addressing the removal of from 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 from signal utilizing the inherent periodicity and morphological property of the signal. The performance of the proposed technique is tested on signals collected from the patients admitted to the intensive care unit, cadavers, and also on synthetic signals. The efficiency of the proposed technique in removing from the signal is quantified through both qualitative and quantitative measures, and the mean opinion scores of the denoised 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.

Quantitative validation of the suprasternal pressure signal to assess respiratory effort during sleep

“…If an ECG signal is not present, the heart rate can be estimated from other cardiac signals, such as photoplethysmography (PPG), or from the SSP signal itself (Cerina et al 2023). Other works in Pes literature employed adaptive filters (Schuessler et al 1998), singular spectrum analysis (Mukhopadhyay et al 2020) or template subtraction (Graß hoff et al 2017). In all cases, the filtering cannot prevent small shifts in the fiducial points of the Pes and SSP signal caused by cardiogenic oscillations (as visible in figure 2), with consequences on all the features of breaths' morphology that we analyzed.…”

Analytical Validation of a Webcam-Based Assessment of Speech Kinematics: Digital Biomarker Evaluation following the V3 Framework

“…All data were filtered using singular spectrum analysis [43], in which the first component of the analysis was retained and used subsequently. Singular spectrum analysis acts as a data-driven adaptive filter with zero-phase, finite-impulse response properties [44]. Window length was chosen as at least 2 [43] and proportional to the sampling rate of the signal [44]; it corresponded to approximately 60 ms of data which, given the differences in sampling rate between the recording methods, represented 2 (webcam), 4 (RealSense), or 6 samples (EMA).…”

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“…Cardiac contractions transmitted to the balloon can slightly distort the P oes signal that nevertheless usually can still be read. Optimal removal of cardiac artefacts is a topic of research [ 41 , 42 ]; practically, it is recommended to take end-inspiratory and end-expiratory P oes values at the same time-point within the artefact.…”