Evaluation of T-wave alternans activity under stress conditions after 5 d and 21 d of sedentary head-down bed rest.

It is well known that prolonged microgravity leads to cardiovascular deconditioning, inducing significant changes in autonomic control of the cardiovascular system. This may adversely influence cardiac repolarization, and provoke cardiac rhythm disturbances. T-wave alternans (TWA), reflecting temporal and spatial repolarization heterogeneity, could be affected. The aim of this work was to test the hypothesis that 5 d and 21 d head-down (-6°) bed rest (HDBR) increases TWA, thus suggesting a higher underlying electrical instability and related arrhythmogenic risk. Forty-four healthy male volunteers were enrolled in the experiments as part of the European Space Agency's HDBR studies. High-fidelity ECG was recorded during orthostatic tolerance (OT) and aerobic power (AP) tests, before (PRE) and after HDBR (POST). A multilead scheme for TWA amplitude estimation was used, where non-normalized and T-wave amplitude normalized TWA indices were computed. In addition, spectral analysis of heart rate variability during OT was assessed. Both 5 d and 21 d HDBR induced a reduction in orthostatic tolerance time (OTT), as well as a decrease in maximal oxygen uptake and reserve capacity, thus suggesting cardiovascular deconditioning. However, TWA indices were found not to increase. Interestingly, subjects with lower OTT after 5 d HDBR also showed higher TWA during recovery after OT testing, associated with unbalanced sympathovagal response, even before the HDBR. In contrast with previous observations, augmented ventricular heterogeneity related to 5 d and 21 d HDBR was not sufficient to increase TWA under stress conditions.

A review of signals used in sleep analysis.

This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.