Learning to estimate heart rate from accelerometer and user's demographics during physical exercises.

Getting prompt insights about health and well-being in a non-invasive way is one of the most popular features available on wearable devices. Among all vital signs available, heart rate (HR) monitoring is one of the most important since other measurements are based on it. Real-time HR estimation in wearables mostly relies on photoplethysmography (PPG), which is a fair technique to handle such a task. However, PPG is vulnerable to motion artifacts (MA). As a consequence, the HR estimated from PPG signals is strongly affected during physical exercises. Different approaches have been proposed to deal with this problem, however, they struggle to handle exercises with strong movements, such as a running session. In this paper, we present a new method for HR estimation in wearables that uses an accelerometer signal and user demographics to support the HR prediction when the PPG signal is affected by motion artifacts. This algorithm requires a tiny memory allocation and allows on-device personalization since the model parameters are finetuned in real time during workout executions. Also, the model may predict HR for a few minutes without using a PPG, which represents a useful contribution to an HR estimation pipeline. We evaluate our model on five different exercise datasets - performed on treadmills and in outdoor environments - and the results show that our method can improve the coverage of a PPG-based HR estimator while keeping a similar error performance, which is particularly useful to improve user experience.

Fatty acid profiles of the plasma and follicular fluid mares fed a combination of linseed and salmon oil.

This study evaluated the presence of polyunsaturated fatty acids in circulating blood and in the ovarian follicular fluid of mares, after supplementation of the diet with linseed oil. Six Mangalarga Marchador mares, weighing 397.00±31.89 kg, were kept on native pasture, and assigned to the current study. In a switch over design, mares were randomly allocated to receive 150 ml of vegetable oil daily, containing polyunsaturated fatty acids n3 (62.23 g ALA, 20.34 g LA, 2.27 g EPA, 2.32 g DHA), (n=3) or no supplementation (n=3) in two replicates. Blood and follicular fluid samples were taken on the first day (D0) and every 30 days until the end of the supplementation period (D60). After 60 days of supplementation, mares were switched across the treatments. Plasma concentrations of linolenic acid in total fatty acids were higher (P=0.006) in the supplemented compared to the control group (1.89±0.13 vs. 1.49±0.13%). There were positive correlations between plasma linoleic acid and follicular fluid arachidonic acid (P=0.0106; r2=0.13) and between plasma alpha linolenic acid and follicular fluid EPA (P=0.0004; r2=0.2544). Data indicated a low to moderate relationship between the dietary linseed-based oil supplementation studied and circulating and follicular fluid polyunsaturated fatty acids contents in mares.

EASY score (Eloquent, Age and baseline SYmptoms score) for outcome prediction in patients with acute ischemic stroke.

OBJECTIVE: A pragmatic tool for the early and reliable prediction of recovery in patients with acute ischemic stroke is needed. We aimed to test the addition of brain eloquent areas involvement in variables predicting poor outcome, using a simple scoring system. METHODS: Retrospective study of patients with anterior circulation acute ischemic stroke treated with best medical treatment and/or endovascular reperfusion. Primary outcome measure was 3-months poor outcome (mRs 3-6). We developed a prognostic model based on clinical data and a quantitative scoring system of the main eloquent brain areas involved on early follow-up CT, and analyzed its accuracy to predict poor outcome comparatively to three other prognostic models. The final model was used to develop a score for outcome prediction based on the multivariable analysis. RESULTS: A total of 197 patients were included (poor outcome = 62; mean age 67 ± 15.1 years; 44% females). Independent predictors of poor outcome were increasing age (p < 0.001), baseline NIHSS (p = 0.03), and the involvement of two brain areas: posterior limb of internal capsule (p < 0.001) and postero-superior corona radiata (p < 0.001). This model showed to be the most accurate to predict poor outcome (Balance Accuracy = 77.74%; C-Statistic = 0.891). The derived risk score attributing points for each of these variables (EASY score) showed similar performances (Balance Accuracy = 82.11%; C-Statistic = 0.90). CONCLUSION: The EASY score is an easy-to-apply and accurate tool to predict the 3-months functional outcome after ischemic stroke, relying on simple clinical features and the assessment of two key eloquent brain areas on early follow-up CT.

Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces.

This work presents a classification performance comparison between different frameworks for functional connectivity evaluation and complex network feature extraction aiming to distinguish motor imagery classes in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The analysis was performed in two online datasets: (1) a classical benchmark-the BCI competition IV dataset 2a-allowing a comparison with a representative set of strategies previously employed in this BCI paradigm and (2) a statistically representative dataset for signal processing technique comparisons over 52 subjects. Besides exploring three classical similarity measures-Pearson correlation, Spearman correlation, and mean phase coherence-this work also proposes a recurrence-based alternative for estimating EEG brain functional connectivity, which takes into account the recurrence density between pairwise electrodes over a time window. These strategies were followed by graph feature evaluation considering clustering coefficient, degree, betweenness centrality, and eigenvector centrality. The features were selected by Fisher's discriminating ratio and classification was performed by a least squares classifier in agreement with classical and online BCI processing strategies. The results revealed that the recurrence-based approach for functional connectivity evaluation was significantly better than the other frameworks, which is probably associated with the use of higher order statistics underlying the electrode joint probability estimation and a higher capability of capturing nonlinear inter-relations. There were no significant differences in performance among the evaluated graph features, but the eigenvector centrality was the best feature regarding processing time. Finally, the best ranked graph-based attributes were found in classical EEG motor cortex positions for the subjects with best performances, relating functional organization and motor activity. Graphical Abstract Evaluating functional connectivity based on Space-Time Recurrence Counting for motor imagery classification in brain-computer interfaces. Recurrences are evaluated between electrodes over a time window, and, after a density threshold, the electrodes adjacency matrix is stablish, leading to a graph. Graph-based topological measures are used for motor imagery classification.