Biomedical sciences instrumentation
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Human modelling is an interdisciplinary research field. The topic, emotion-affected decision making, was originally a cognitive psychology issue, but is now recognized as an important research direction for both computer science and biomedical modelling. ⋯ The work is based on Ortony's theory of emotions and bounded rationality theory, and attempts to connect the emotion process with decision making. A computational emotion model is proposed, and the initial framework of this model in virtual human simulation within the platform of Virtools is presented.
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Previous research has developed a pneumatically driven device for delivering a controlled mechanical insult to cultured neurons. The neuronal cell culture was injured by applying a transient air pulse to a culture well fitted with a highly elastic Silastic culture well bottom. ⋯ The simulation results, using a finite element model of the culture well membrane, compared well with the results from the original experiments. When peak air pressure was varied from 69 kPa to 345 kPa (10 to 50 psig), numerical simulations showed that the corresponding membrane strains varied from 20 to 95% and the stress response varied from 0.5 to 1.2 MPa.
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Clinical Trial Controlled Clinical Trial
Assessment of heart rate variability during alterations in stress: complex demodulation vs. spectral analysis.
Complex demodulation (CDM) has been proposed as a method for the analysis of high- and low-frequency variabilities of heart rate and blood pressure under non-stationary conditions. In contrast to power spectral analysis, CDM provides time-dependent changes in signal amplitude and frequency on a continuous basis and may yield insights into short-term alterations in autonomic regulation. In particular, CDM may be uniquely suited for quantifying changes in respiratory sinus arrhythmia (RSA) at the onset of acute physical or mental stress conditions. ⋯ Compared to CDM, power spectral analysis results were less informative since they did not allow the disentangling of unique contributions of distinct amplitudes and frequencies at different time points. Our analyses indicate that CDM provides a powerful means of continuously assessing time-dependent changes in RSA during varying physical or mental stress. CDM may also hold promise for a range of physiological and environmental non-steady state conditions where rapid dynamic alterations in autonomic control are likely to occur.
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The use of sidestream capnometers, with a sampling rate of 150-250 cc/min, as a means of measuring a patient's expired CO2 (ETCO2) and respiratory rate, has been a common practice for many years. However, in recent years, there has been a focus on lower flow rate sampling sidestream systems due to the benefits of less loss of tidal volume for patients, such as infants or neonates. When developing a sidestream system, four principle issues must be considered; 1) The signal fidelity of the gas sample must be sufficiently maintained from the sampling site to the measurement site. 2) Condensate from a patient's breath, as well as blood, mucus, or other contaminates often pose problems for sidestream systems and requires mitigation. 3) The mechanics of transporting a gas sample at a constant flow rate through the sampling system, regardless of atmospheric or clinical conditions must be developed. 4) The physics of handling CO2 gas throughout the transport process must be understood in order to ensure accurate readings. These issues lead to a complex web of interrelations that are explored in the development of a low flow rate sidestream capnometer.
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Comparative Study Clinical Trial
Kullback-Leibler clustering of continuous wavelet transform measures of heart rate variability.
Power spectral analysis of beat-to-beat heart rate variability (HRV) has provided a useful means of understanding the interplay between autonomic and cardiovascular functionality. Despite their utility, commonly employed frequency-domain techniques are limited in their prerequisite for stationary signals and their inability to account for temporal changes in the power spectral and/or frequency properties of signals. The purpose of this study is to develop an algorithm that utilizes continuous wavelet transform (CWT) parameters as inputs to a Kohonen self-organizing map (SOM), providing a method of clustering subjects with similar wavelet transform signatures. ⋯ Differences in subject demographics between two final clusters were assessed via two-independent-groups t-tests or chi-square or Fisher's exact tests of contingency tables. Significant differences were found for age, initial systolic blood pressure, smoking status, and mean s.d. of coefficients in the high frequency band (0.15-0.4 Hz). These findings may have clinical significance and the developed algorithm provides an alternative means of analyzing HRV data originating from populations with complex covariates.