Method Inform Med
-
To investigate the effects of hypoxia during sleep on linear and self-similar components of heart rate variability (HRV) in eight healthy subjects at high altitude on Mount Everest. ⋯ While the biological interpretation of these results is still in progress, our data indicates that the cardiac response to high altitude hypoxia during sleep can hardly be fully explored by traditional HRV estimators only, and requires the additional support of more sophisticated indexes exploring also nonlinear and fractal features of cardiac variability.
-
Accurate and early diagnosis of various diseases and pathological conditions require analysis techniques that can capture time-varying (TV) dynamics. In the pursuit of promising TV signal processing methods applicable to real-time clinical monitoring applications, nonstationary spectral techniques are of great significance. ⋯ Integration of such robust algorithms into pulse oximeter device may have significant impact in real-time clinical monitoring and point-of-care healthcare settings.
-
1) To measure the incidence and impact of missed radiology and microbiology test results in an emergency department with an electronic test order and results viewing system, and 2) to assess the average times from test order to test availability. ⋯ Our rates of missed test results are lower than those reported from studies where paper ordering and reporting systems were used. This suggests that the availability of CPOE systems may reduce the risk of these events. Electronic result delivery, with electronic endorsement to allow documentation of follow-up of test results, may provide additional efficiency benefits and further reduce the risk of test results which are not followed up.
-
The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomnographic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. ⋯ This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.