Improved filtering methods to suppress cardiovascular contamination in electrical impedance tomography recordings
Physiological Measurement, vol. 45, pp. 055010
Abstract
Abstract Objective. Electrical impedance tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters. Approach. Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated (1) in the time domain (2) in the frequency domain (3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from 15 adult and neonatal intensive care unit patients. Main result. Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data. Significance. Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question.
Authors 6
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Erasmus MC · Erasmus MC - Sophia Children’s Hospital
Affiliation as printed
Department of Adult Intensive Care, Erasmus Medical Centre, Rotterdam, The Netherlands
Department of Neonatal and Pediatric Intensive Care, Erasmus Medical Centre-Sophia Children's Hospital, Rotterdam, The Netherlands
Department of Adult Intensive Care, Erasmus Medical Center, Dr. Molewaterplein 40, Rotterdam, Zuid-Holland, 3015 GD, NETHERLANDS
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Affiliation as printed
Department of Adult Intensive Care, Erasmus Medical Centre, Rotterdam, The Netherlands
Department of Adult Intensive Care, Erasmus Medical Center, Dr. Molewaterplein 40, Rotterdam, Zuid-Holland, 3015 GD, NETHERLANDS
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Erasmus MC · Delft University of Technology
Affiliation as printed
Department of Adult Intensive Care, Erasmus Medical Centre, Rotterdam, The Netherlands
Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands
Department of Adult Intensive Care, Erasmus Medical Center, Dr. Molewaterplein 40, Rotterdam, Zuid-Holland, 3015 GD, NETHERLANDS
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Leiden University Medical Center · Erasmus MC
Affiliation as printed
Department of Adult Intensive Care, Erasmus Medical Centre, Rotterdam, The Netherlands
Department of Neurology, LUMC, Leiden, The Netherlands
Department of Adult Intensive Care, Erasmus Medical Center, Dr. Molewaterplein 40, Rotterdam, Zuid-Holland, 3015 GD, NETHERLANDS
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Affiliation as printed
Department of Adult Intensive Care, Erasmus Medical Centre, Rotterdam, The Netherlands
Department of Adult Intensive Care, Erasmus Medical Center, Dr. Molewaterplein 40, Rotterdam, Zuid-Holland, 3015 GD, NETHERLANDS
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Affiliation as printed
Department of Adult Intensive Care, Erasmus Medical Centre, Rotterdam, The Netherlands
Erasmus MC, Dr. Molewaterplein 40, Rotterdam, 3015GD, NETHERLANDS
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