Journal of clinical monitoring and computing
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J Clin Monit Comput · Jun 2022
ReviewDescription of the validity of the Analgesia Nociception Index (ANI) and Nociception Level Index (NOL) for nociception assessment in anesthetized patients undergoing surgery: a systematized review.
Maintaining optimum analgesia in anesthetized patients is challenging due to the inability to self-report pain or exhibit pain-related behaviours. The Analgesia Nociception Index (ANI) (based on heart rate variability [HRV]) and the Nociception Level Index (NOL) (based on HRV, photoplethysmography, skin conductance, and temperature) both include HRV and provide continuous index monitoring for nociception assessment. The research question was: "What are the validation strategies of the NOL and ANI for nociception assessment in anesthetized patients?". ⋯ Both technologies performed superiorly in detecting nociceptive stimuli compared to individual monitoring of HR and blood pressure. Although the aforementioned validation strategies are deemed appropriate, in the absence of a gold standard, criterion validation findings should be interpreted with caution. Moreover, reliability could be examined using test-retest with consistent ANI/NOL values during a stable time-interval.
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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.
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This paper provides a review of a selection of papers published in the Journal of Clinical Monitoring and Computing in 2020 and 2021 highlighting what is new within the field of respiratory monitoring. Selected papers cover work in pulse oximetry monitoring, acoustic monitoring, respiratory system mechanics, monitoring during surgery, electrical impedance tomography, respiratory rate monitoring, lung ultrasound and detection of patient-ventilator asynchrony.