Journal of clinical monitoring and computing
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J Clin Monit Comput · Jul 2024
Virtual reality simulations to alleviate fear and anxiety in children awaiting MRI: a small-scale randomized controlled trial.
Up to 75% of paediatric patients experience anxiety and distress before undergoing new medical procedures. Virtual reality is an interesting avenue for alleviating the stress and fear of paediatric patients due to its ability to completely immerse the child in the virtual world and thus expose them to the sights and sounds of an MRI before undergoing the exam. We aimed to explore the impact of virtual reality exposure on reducing fear and anxiety in paediatric patients scheduled to undergo an MRI. ⋯ VR exposure effectively reduces pre-MRI anxiety in paediatric patients who are about to undergo the exam, this is important as it alleviates the psychological burden on the child. This research is in line with previous findings, showing the validity of VR as a method of reducing pre-procedural paediatric anxiety and suggesting that complex VR experiences may not be necessary to have a significant impact. There is, however, a need for further investigation in this field using larger and MRI-naïve groups of patients.
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J Clin Monit Comput · Jun 2024
A novel adaptive filter with a heart-rate-based reference signal for esophageal pressure signal denoising.
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. ⋯ 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.
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J Clin Monit Comput · Jun 2024
Comparative StudyCerebral oxygenation saturation in childhood: difference by age and comparison of two cerebral oximetry algorithms.
Few reports are available on the monitoring of regional cerebral oxygen saturation (rSO2) in pediatric patients undergoing non-cardiac surgical procedures. In addition, no study has examined the rSO2 levels in children of a broad age range. In this study, we aimed to assess and compare rSO2 levels in pediatric patients of different age groups undergoing non-cardiac surgery. ⋯ The values in INVOS 5100C and tNIRS-1 were affected by blood pressure and the minimum alveolar concentration of sevoflurane, respectively. In pediatric patients undergoing non-cardiac surgery, rSO2 values differed across the three age groups, and the pattern of these differences varied between the two oximeters employing different algorithms. Further research must be conducted to clarify cerebral oxygenation in children.
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J Clin Monit Comput · Jun 2024
Quantitative electroencephalogram in term neonates under different sleep states.
Electroencephalogram (EEG) can be used to assess depth of consciousness, but interpreting EEG can be challenging, especially in neonates whose EEG undergo rapid changes during the perinatal course. EEG can be processed into quantitative EEG (QEEG), but limited data exist on the range of QEEG for normal term neonates during wakefulness and sleep, baseline information that would be useful to determine changes during sedation or anesthesia. We aimed to determine the range of QEEG in neonates during awake, active sleep and quiet sleep states, and identified the ones best at discriminating between the three states. ⋯ Entropy beta and SEF50 were best at discriminating between awake and sleep states. QEEG were not as good at discriminating between quiet and active sleep. In the future, QEEG with high discriminatory power can be combined to further improve ability to differentiate between states of consciousness.