Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2024
Observational StudyMeasurement accuracy of a microwave doppler sensor beneath the mattress as a continuous respiratory rate monitor: a method comparison study.
Non-contact continuous respiratory rate monitoring is preferred for early detection of patient deterioration. However, this technique is under development; a gold standard respiratory monitor has not been established. Therefore, this prospective observational method comparison study aimed to compare the measurement accuracy of a non-contact continuous respiratory rate monitor, a microwave Doppler sensor positioned beneath the mattress, with that of other monitors. ⋯ The microwave Doppler sensor had a small bias but relatively low precision, similar to other devices. In CEG analyses, the risk of each monitor leading to inadequate clinical decision-making was low.
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J Clin Monit Comput · Feb 2024
Observational StudyPostoperative circadian patterns in wearable sensor measured heart rate: a prospective observational study.
This study aimed to describe the 24-hour cycle of wearable sensor-obtained heart rate in patients with deterioration-free recovery and to compare it with patients experiencing postoperative deterioration. ⋯ The postoperative diurnal rhythm of heart rate is disturbed by different types of surgery. Both groups showed recovery of diurnal rhythm but in patients following cancer surgery, both peak and nadir heart rates were higher than in the bariatric surgery group. Especially nadir heart rate was identified as a potential prognostic marker for deterioration after cancer surgery.
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J Clin Monit Comput · Apr 2024
Autonomic dysfunction as a predictor of infection in neurocritical care unit: a prospective cohort study.
Infection in the neurocritical care unit ( NCCU) can cause significant mortality and morbidity. Autonomic nervous system plays an important role in defense against infection. Autonomic dysfunction causing inflammatory dysregulation can potentiate infection. We aimed to study the relationship between autonomic dysfunction and occurrence of infection in neurologically ill patients. ⋯ AD assessment can be used as a tool to predict development of infection in NCCU. This can help triage and institute early investigation and treatment.
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J Clin Monit Comput · Apr 2024
Support-vector classification of low-dose nitrous oxide administration with multi-channel EEG power spectra.
Support-vector machines (SVMs) can potentially improve patient monitoring during nitrous oxide anaesthesia. By elucidating the effects of low-dose nitrous oxide on the power spectra of multi-channel EEG recordings, we quantified the degree to which these effects generalise across participants. In this single-blind, cross-over study, 32-channel EEG was recorded from 12 healthy participants exposed to 0, 20, 30 and 40% end-tidal nitrous oxide. ⋯ This showed the relative importance of decreased delta power and the frontal region. SVM classification identified that the most important effects of nitrous oxide were found in the delta band in the frontal electrodes that was consistent between participants. Furthermore, support-vector classification of nitrous oxide dosage is a promising method that might be used to improve patient monitoring during nitrous oxide anaesthesia.
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J Clin Monit Comput · Jun 2022
ReviewPower spectrum and spectrogram of EEG analysis during general anesthesia: Python-based computer programming analysis.
The commonly used principle for measuring the depth of anesthesia involves changes in the frequency components of the electroencephalogram (EEG) under general anesthesia. Therefore, it is essential to construct an effective spectrum and spectrogram to analyze the relationship between the depth of anesthesia and the EEG frequency during general anesthesia. This paper reviews the computer programming techniques for analyzing the spectrum and spectrogram derived from a single-channel EEG recorded during general anesthesia. ⋯ Finally, the multitaper method, which can suppress artifacts caused by the edges of the analysis segments, suppress noise, and probabilistically infer values that are close to the real power spectral density, is explained using practical examples of the analysis. All analyses were performed and all graphs plotted using Python under Jupyter Notebook. The analyses demonstrated the effectiveness of Python-based programming under the integrated development environment Jupyter Notebook for constructing an effective spectrum and spectrogram for analyzing the relationship between the depth of anesthesia and EEG frequency analysis in general anesthesia.