![]() Three analyses tested the effects: post-fentanyl, preceding the first central depression, and preceding obstruction of the upper airway. A selection of HRV indices, able to work over sub-minute periods on non-stationary signals, were applied including a range of less common indices. This pilot observational study examined the dynamic sympathovagal changes during fentanyl-midazolam induced respiratory depression on 10 subjects scheduled for minor surgery. Sympathetic arousal generated by apneic events may separately be recognised with short-term HRV. While opioids tend to increase parasympathetic tone, a decrease in airway stability could be due to a decrease in respiratory parasympathetic activity. Regulation of the cardiovascular and respiratory centres may be coupled with a central mechanism that is indirectly measurable with heart rate variability (HRV). Airway patency during inspiration requires vagal modulation. Earlier detection would be beneficial in preventing increased morbidity and mortality of 0.01 % patients receiving analgesic opioids. The detection of critical respiratory depression usually occurs after the event. Opioids have an occasional but high-risk side effect of respiratory depression. We illustrate BioP圜 use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. Therefore, in this paper, we describe BioP圜, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. ![]() Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. Research on brain-computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. These indices require validation against physiological data before they can be applied to short-term HRV analysis of cardiac autonomic nervous system activity. Of these, 70 were unique and produced a finite number with 60 s data, so are included in the Toolbox. The survey identified a comprehensive list of 115 indices that were subsequently coded and screened. Indices were tested with short segments of archived data to remove those that produced invalid results, or were mathematically equivalent to, but less well known than other indices. The survey included measures of time domain, frequency domain, respiratory sinus arrhythmia, Poincaré plot, and heart rate characteristics. This study surveyed published methods of HRV analysis searching for indices that could be applied to very short time HRV analysis. Many of the indices have been sparingly used and have not been investigated for application to short-term use. ![]() From the 1980s there has been a wealth of HRV indices produced in the quest for better measures of the change in parasympathetic and sympathetic activity. during drug administration, or the start or end of exercise). Heart rate variability (HRV) analysis over very short (<60 s) periods may be useful for monitoring dynamic changes in autonomic nervous system activity where steady-state conditions are not maintained (e.g. ![]()
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