Capnography: A Feasible Tool in Clinical and Experimental Settings

Oliveira, Pricila Mara Novais de; Moreira, Marcos Mello

doi:10.4187/respcare.04504

Cited by 4 publications

(3 citation statements)

References 24 publications

(22 reference statements)

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“…To account for respiration, oro-nasal airflow signals from SIESTA and the capnography signals from VitalDB were included. The latter is a non-invasive technique used to monitor the level of carbon dioxide in exhaled breath and to measure the patient's respiratory status [33] [34]. The technique involves the use of sensors detecting concentration of CO 2 in the airway [33] [34].…”

Section: A Collected Datasetsmentioning

confidence: 99%

“…The latter is a non-invasive technique used to monitor the level of carbon dioxide in exhaled breath and to measure the patient's respiratory status [33] [34]. The technique involves the use of sensors detecting concentration of CO 2 in the airway [33] [34]. In contrast, airflow sensing is another technique used to monitor respiratory status which involves the use of sensors that detect the flow of air in and out of the lungs.…”

Section: A Collected Datasetsmentioning

confidence: 99%

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Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis

Idrobo-Ávila,

Bognár,

Krefting

et al. 2024

IEEE Open J. Eng. Med. Biol.

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Goal: Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysiswhich involves their joint analysis -can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. Methods: We applied widely known algorithms entitled "signal quality indicators" to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. Results: 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. A correlation was found between entropy outliers and non-suitable electrocardiographic signals. Conclusions: The majority of data within the surgery dataset VitalDB demonstrated suitability for multimodal analysis.

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Section: A Collected Datasetsmentioning

confidence: 99%

Section: A Collected Datasetsmentioning

confidence: 99%

Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis

Idrobo-Ávila,

Bognár,

Krefting

et al. 2024

IEEE Open J. Eng. Med. Biol.

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“…

The graphical representation of the partial pressure of carbon dioxide (CO 2 ) expired during one tidal breath is called volumetric capnography (VCap). 1,2 It is formed by phase I, or the initial period of expiration when no CO 2 comes from the conducting airways; the steep phase II, which is formed by the CO 2 coming from lung units emptying into the main airways at different rates; and phase III representing pure alveolar gas (Figure 1). 3 VCap is an expression of the body's CO 2 kinetics and enables the calculation

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mentioning

confidence: 99%

Effects of apparatus dead space on volumetric capnograms in neonates with healthy lungs: a simulation study

Campos¹,

Palazzi²,

Böhm

et al. 2023

Pediatric Anesthesia

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BackgroundVolumetric capnography in healthy ventilated neonates showed deformed waveforms, which are supposedly due to technological limitations of flow and carbon dioxide sensors.AimsThis bench study analyzed the role of apparatus dead space on the shape of capnograms in simulated neonates with healthy lungs.MethodsWe simulated mechanical breaths in neonates of 2, 2.5, and 3 kg of body weight using a neonatal volumetric capnography simulator. The simulator was fed by a fixed amount of carbon dioxide of 6 mL/kg/min. Such simulator was ventilated in a volume control mode using fixed ventilatory settings with a tidal volume of 8 mL/kg and respiratory rates of 40, 35, and 30 breaths per minute for the 2, 2.5 and 3 kg neonates, respectively. We tested the above baseline ventilation with and without an additional apparatus dead space of 4 mL.ResultsSimulations showed that adding the apparatus dead space to baseline ventilation increased the amount of re‐inhaled carbon dioxide in all neonates: 0.16 ± 0.01 to 0.32 ± 0.03 mL (2 kg), 0.14 ± 0.02 to 0.39 ± 0.05 mL (2.5 kg), and 0.13 ± 0.01 to 0.36 ± 0.05 mL (3 kg); (p < .001). Apparatus dead space was computed as part of the airway dead space, and therefore, the ratio of airway dead space to tidal volume increased from 0.51 ± 0.04 to 0.68 ± 0.06, from 0.43 ± 0.04 to 0.62 ± 0.01 and from 0.38 ± 0.01 to 0.60 ± 0.02 in the 2, 2.5 and 3 kg simulated neonates, respectively (p < .001). Compared to baseline ventilation, adding apparatus dead space decreased the ratio of the volume of phase III to VT size from 31% to 11% (2 kg), from 40% to 16% (2.5 kg) and from 50% to 18% (3 kg); (p < .001).ConclusionsThe addition of a small apparatus dead space artificially deformed the volumetric capnograms in simulated neonates with healthy lungs.

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