The Mozart effect has been the subject of lively but also controversial debate in literature during the last decades. Positive as well as negative results have been reported, using different musical stimuli as well as cognitive tasks. In addition, only a few studies included EEG recordings in the experimental design, and when they did, they focused exclusively on spectral power densities. A more complete experimental design is presented considering different stimuli as well as three different cognitive tasks including EEG measurements. Furthermore, we propose a novel strategy for the analysis of the functional network of brain dynamics, based on deviations from a universal stationary cross-correlation pattern. • An experimental design of the Mozart effect is presented, considering different cognitive tasks and two different musical stimuli, besides a control group • The presence of a stationary cross-correlation pattern that is nearly independent of the physilogical brain state is illustrated. • It is demostrasted that that deviations from the stationary pattern serve much better for the distinction of different task settings

Background/Objectives: Wearable devices offer a scalable approach to repeated cardiovascular assessment through ambulatory spot measurements and have demonstrated adequate performance in measuring heart rate (HR). However, their reliability for blood pressure (BP) assessment remains uncertain. This study aimed to evaluate BP and HR measurements obtained with the HUAWEI WATCH D2 smartwatch by comparison with standard reference devices under controlled postural conditions. Methods: A total of 30 participants (15 women and 15 men; age range, 21–88 years) were enrolled. One participant could not undergo smartwatch measurement because of wrist-size limitations, and measurement acquisition failed in another participant. Consequently, paired measurements obtained in the seated and upright positions were analyzed for 28 participants. Smartwatch-derived systolic BP (SBP), diastolic BP (DBP), and HR were compared with a digital sphygmomanometer, an analog sphygmomanometer, and a digital pulse oximeter. Agreement was assessed using percentage error and Bland–Altman analysis, complemented by linear regression to evaluate proportional bias. Results: In the seated position, the smartwatch showed agreement with percentage errors below 4% for SBP and DBP, with concordance coefficients indicating good agreement. In the upright position, accuracy decreased, with percentage errors up to 14%, increased variability, wider limits of agreement, and the presence of systematic and proportional discrepancies. Measurement performance was influenced by device placement, and isolated failures highlighted sensitivity to anatomical and technical factors. Conclusions: Smartwatch-based BP monitoring is feasible under controlled conditions but remains dependent on posture and measurement conditions. These findings support the use of wearable devices for scheduled spot-check cardiovascular assessment under controlled conditions and underscore the need for standardized measurement protocols before their implementation in ambulatory or long-term monitoring applications.

We perform a comprehensive statistical analysis of several features derived from the electrocardiogram (ECG) signal, based on the so-called fiducial points. These features, which we refer to collectively as fiducial measures (FMs), are grouped into two main categories: durations and amplitudes. From each FM, we obtain discrete-time series indexed by ECG cycles. The main goal of this study is to show that these FMs, when jointly analyzed through multivariate statistical techniques, allow to distinguish between two groups: healthy young and healthy elderly subjects. We begin by analyzing basic statistical properties of individual (univariate) fiducial time series. In particular, we show that these signals generally exhibit nonstationary behavior. To address this, we introduce two detrending techniques that render the signals stationary. Based on these detrended time series, we compute empirical distributions and estimate their corresponding Shannon entropy. Beyond the univariate level, we explore joint statistical relationships between FMs through multivariate analysis. Specifically, we calculate correlation matrices, mutual information, and Gaussian copulas for all pairs of FMs. Our results reveal a notable statistical independence between amplitude and duration FMs. Furthermore, we observe that the strength of pairwise dependence between durations, as well as between amplitudes, tends to decrease with age, suggesting a potential physiological decoupling associated with aging.