A template subtraction method for the removal of cardiogenic oscillations on esophageal pressure signals

Graßhoff, Jan; Petersen, Eike; Eger, Marcus; Bellani, Giacomo; Rostalski, Philipp

doi:10.1109/embc.2017.8037299

Cited by 14 publications

(22 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All four algorithmic families -TS, model-based, waveletbased, and EMD-based algorithms -have been shown to perform well in comparison to other approaches for the removal of cardiac artifacts [21], [25], [28], [29]. In particular, note that single-channel adaptations of the popular adaptive filtering approach [14], on the one hand, have been clearly shown to perform inferior compared to the modelbased approaches [21], and on the other hand represent close but less adaptive relatives of the TS algorithm [30].…”

Section: Introductionmentioning

confidence: 99%

Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms

et al. 2020

Self Cite

View full text Add to dashboard Cite

Electromyographic (EMG) measurements of the respiratory muscles provide a convenient and noninvasive way to assess respiratory muscle function and detect patient activity during assisted mechanical ventilation. However, surface EMG measurements of the diaphragm and intercostal muscles are substantially contaminated by cardiac activity due to the vicinity of the cardiac muscles. Many algorithmic solutions to this problem have been proposed, yet a conclusive performance comparison of the most promising candidates currently is missing. The objective of this work is to provide a quantitative performance comparison of six previously proposed cardiac artifact removal algorithms operating on single-channel EMG measurements, and two newly proposed, improved versions of these algorithms. Algorithmic performance is evaluated quantitatively based on four different measures of separation success, using both synthetic validation signals and electromyographic measurements of the respiratory muscles in eight subjects. The compared algorithms are two versions of the empirical template subtraction algorithm, two model-based Bayesian filtering procedures, a wavelet denoising approach, an empirical mode decomposition (EMD) based approach, and classical high-pass filtering. Different algorithms perform well with respect to different performance measures. Template subtraction algorithms yield the best results if the characteristics of the raw signal are of interest, while filtering algorithms like simple high-pass filtering, wavelet denoising, and EMD-based denoising show superior performance for calculating a cleaned envelope signal. No algorithm completely removes the cardiac interference, but the characteristic errors introduced by the considered algorithms differ. Hence, the choice of the algorithm to use should be made depending on the target application. Finally, we also demonstrate that our empirical SNR measure, which can be calculated without knowledge of the true, undisturbed signals, correlates strongly with the exact reconstruction error. Thus, it represents a reliable indicator for algorithm performance on real measurement data.

show abstract

Section: Introductionmentioning

confidence: 99%

Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…A direct comparison of performance of the proposed signal denoising technique with that of other techniques which are reported in [4], [6], [7], [8] and [9] is not possible as the performance these techniques are evaluated with different settings and databases. However, in [6], the simulated signals were added with artificial and white Gaussian noise (WGN) to have a signal-to-noise ratio of 10 dB, and then the noisy signals are denoised.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…A template subtraction-based technique is proposed by Grabhoff et alin [9] for the removal of from signal. Here also, in [9], a reference-signal, namely electromyogram (EMG) is required to generate a template of the signal. The techniqueis designed based on three main steps: detection of the R-peak-indices from EMG signal, template generation and template subtraction.…”

Section: Introductionmentioning

confidence: 99%

“…In [10], the contaminated signal is decomposed into a number of intrinsic mode functions (IMFs), and the IMFs which are associated with the signal only, are summed up to obtain a -free signal. However, All the above discussed techniques [4], [6]- [9] (except [10]), require an additional signal, be it an ECG or EMG, along with . Acquisition of an extra signal undoubtedly increases the complexity of the system and hinders patients' comfort.…”

Section: Introductionmentioning

confidence: 99%

“…A template subtraction-based technique is proposed by Grabhoff et al in [9] for the removal of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {CGO}$ \end{document} from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${ \boldsymbol {P}}_{ \boldsymbol {eso}}$ \end{document} signal. Here also, in [9] , a reference-signal, namely electromyogram (EMG) is required to generate a template of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {CGO}$ \end{document} signal. The techniqueis designed based on three main steps: detection of the R-peak-indices from EMG signal, template generation and template subtraction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Mukhopadhyay

Zara

Telias

et al. 2020

IEEE J. Transl. Eng. Health Med.

View full text Add to dashboard Cite

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.

show abstract

A novel adaptive filter with a heart-rate-based reference signal for esophageal pressure signal denoising

Qin,

Huang,

Zhou

et al. 2024

J Clin Monit Comput

View full text Add to dashboard Cite

Esophageal pressure (Peso) is one of the most common and minimally invasive methods used to assess the respiratory and lung mechanics in patients receiving mechanical ventilation. However, the Peso measurement is contaminated by cardiogenic oscillations (CGOs), which cannot be easily eliminated in real-time. The field of study dealing with the elimination of CGO from Peso signals is still in the early stages of its development. In this study, we present an adaptive filtering-based method by constructing a reference signal based on the heart rate and sine function to remove CGOs in real-time. The proposed technique is tested using clinical data acquired from 20 patients admitted to the intensive care unit. Lung compliance ( QUOTE ) and esophageal pressure swings (△Pes) are used to evaluate the performance and efficiency of the proposed technique. The CGO can be efficiently suppressed when the constructional reference signal contains the fundamental, and second and third harmonic frequencies of the heart rate signal. The analysis of the data of 8 patients with controlled mechanical ventilation reveals that the standard deviation/mean of the QUOTE is reduced by 28.4–79.2% without changing the QUOTE and the △Pes measurement is more accurate, with the use of our proposed technique. The proposed technique can effectively eliminate the CGOs from the measured Peso signals in real-time without requiring additional equipment to collect the reference signal.

show abstract

A template subtraction method for the removal of cardiogenic oscillations on esophageal pressure signals

Cited by 14 publications

References 11 publications

Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms

Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms

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

A novel adaptive filter with a heart-rate-based reference signal for esophageal pressure signal denoising

Contact Info

Product

Resources

About