The relationship between workload and exercise-induced cardiac troponin elevations is influenced by non-obstructive coronary atherosclerosis.

The relationship between exercise-induced troponin elevation and non-obstructive coronary artery disease (CAD) is unclear. This observational study assessed non-obstructive CAD's impact on exercise-induced cardiac Troponin I (cTnI) elevation in middle-aged recreational athletes. cTnI levels of 40 well-trained recreational athletes (73% males, 50 ± 9 years old) were assessed by a high-sensitive cTnI assay 24 h before, and at 3 and 24 h following two high-intensity exercises of different durations; a cardiopulmonary exercise test (CPET), and a 91-km mountain bike race. Workload was measured with power meters. Coronary computed tomography angiography was used to determine the presence or absence of non-obstructive (<50% obstruction) CAD. A total of 15 individuals had non-obstructive CAD (Atherosclerotic group), whereas 25 had no atherosclerosis (normal). There were higher post-exercise cTnI levels following the race compared with CPET, both at 3 h (77.0 (35.3-112.4) ng/L vs. 11.6 (6.4-22.5) ng/L, p < 0.001) and at 24 h (14.7 (6.7-16.3) vs. 5.0 (2.6-8.9) ng/L, p < 0.001). Absolute cTnI values did not differ among groups. Still, the association of cTnI response to power output was significantly stronger in the CAD versus Normal group both at 3 h post-exercise (Rho = 0.80, p < 0.001 vs. Rho = -0.20, p = 0.33) and 24-h post-exercise (Rho = 0.87, p < 0.001 vs. Rho = -0.13, p = 0.55). Exercise-induced cTnI elevation was strongly correlated with exercise workload in middle-aged athletes with non-obstructive CAD but not in individuals without CAD. This finding suggests that CAD influences the relationship between exercise workload and the cTnI response even without coronary artery obstruction.

Optimizing support vector machines and autoregressive integrated moving average methods for heart rate variability data correction.

Heart rate variability (HRV) is the variation in time between successive heartbeats and can be used as an indirect measure of autonomic nervous system (ANS) activity. During physical exercise, movement of the measuring device can cause artifacts in the HRV data, severely affecting the analysis of the HRV data. Current methods used for data artifact correction perform insufficiently when HRV is measured during exercise. In this paper we propose the use of autoregressive integrated moving average (ARIMA) and support vector regression (SVR) for HRV data artifact correction. Since both methods are only trained on previous data points, they can be applied not only for correction (i.e., gap filling), but also prediction (i.e., forecasting future values). Our paper describes:•why HRV is difficult to predict and why ARIMA and SVR might be valuable options.•finding the best hyperparameters for using ARIMA and SVR to correct HRV data, including which criterion to use for choosing the best model.•which correction method should be used given the data at hand.

Determinants of Interindividual Variation in Exercise-Induced Cardiac Troponin I Levels.

Background Postexercise cardiac troponin levels show considerable interindividual variations. This study aimed to identify the major determinants of this postexercise variation in cardiac troponin I (cTnI) following 3 episodes of prolonged high-intensity endurance exercise. Methods and Results Study subjects were recruited among prior participants in a study of recreational cyclists completing a 91-km mountain bike race in either 2013 or 2014 (first race). In 2018, study participants completed a cardiopulmonary exercise test 2 to 3 weeks before renewed participation in the same race (second race). Blood was sampled before and at 3 and 24 hours following all exercises. Blood samples were analyzed using the same Abbot high-sensitivity cTnI STAT assay. Fifty-nine individuals (aged 50±9 years, 13 women) without cardiovascular disease were included. Troponin values were lowest before, highest at 3 hours, and declining at 24 hours. The largest cTnI difference was at 3 hours following exercise between the most (first race) (cTnI: 200 [87-300] ng/L) and the least strenuous exercise (cardiopulmonary exercise test) (cTnI: 12 [7-23] ng/L; P<0.001). The strongest correlation between troponin values at corresponding times was before exercise (r=0.92, P<0.0001). The strongest correlations at 3 hours were between the 2 races (r=0.72, P<0.001) and at 24 hours between the cardiopulmonary exercise test and the second race (r=0.83, P<0.001). Participants with the highest or lowest cTnI levels showed no differences in race performance or baseline echocardiographic parameters. Conclusions The variation in exercise-induced cTnI elevation is largely determined by a unique individual cTnI response that is dependent on the duration of high-intensity exercise and the timing of cTnI sampling. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02166216.

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation.

Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field.

Artifact Correction in Short-Term HRV during Strenuous Physical Exercise.

Heart rate variability (HRV) analysis can be a useful tool to detect underlying heart or even general health problems. Currently, such analysis is usually performed in controlled or semi-controlled conditions. Since many of the typical HRV measures are sensitive to data quality, manual artifact correction is common in literature, both as an exclusive method or in addition to various filters. With proliferation of Personal Monitoring Devices with continuous HRV analysis an opportunity opens for HRV analysis in a new setting. However, current artifact correction approaches have several limitations that hamper the analysis of real-life HRV data. To address this issue we propose an algorithm for automated artifact correction that has a minimal impact on HRV measures, but can handle more artifacts than existing solutions. We verify this algorithm based on two datasets. One collected during a recreational bicycle race and another one in a laboratory, both using a PMD in form of a GPS watch. Data include direct measurement of electrical myocardial signals using chest straps and direct measurements of power using a crank sensor (in case of race dataset), both paired with the watch. Early results suggest that the algorithm can correct more artifacts than existing solutions without a need for manual support or parameter tuning. At the same time, the error introduced to HRV measures for peak correction and shorter gaps is similar to the best existing solution (Kubios-inspired threshold-based cubic interpolation) and better than commonly used median filter. For longer gaps, cubic interpolation can in some cases result in lower error in HRV measures, but the shape of the curve it generates matches ground truth worse than our algorithm. It might suggest that further development of the proposed algorithm may also improve these results.

Extended approach to sum of absolute differences method for improved identification of periods in biomedical time series.

Time series are a common data type in biomedical applications. Examples include heart rate, power output, and ECG. One of the typical analysis methods is to determine longest period a subject spent over a given heart rate threshold. While it might seem simple to find and measure such periods, biomedical data are often subject to significant noise and physiological artifacts. As a result, simple threshold calculations might not provide correct or expected results. A common way to improve such calculations is to use moving average filter. Length of the window is often determined using sum of absolute differences for various windows sizes. However, for real life biomedical data such approach might lead to extremely long windows that undesirably remove physiological information from the data. In this paper, we:•propose a new approach to finding windows length using zero-points of third gradient (jerk) of Sum of Absolute Differences method;•demonstrate how these points can be used to determine periods and area over a given threshold with and without uncertainty.We demonstrate validity of this approach on the PAMAP2 Physical Activity Monitoring Data Set, an open dataset from the UCI Machine Learning Repository, as well as on the PhysioNet Simultaneous Physiological Measurements dataset. It shows that first zero-point usually falls at around 8 and 5 second window length respectively, while second zero-point usually falls between 16 and 24 and 8-16 s respectively. The value for the first zero-point can remove simple measurement errors when data are recorded once every few seconds. The value for the second zero-point corresponds well with what is known about physiological response of heart to changing load.

Methods for preprocessing time and distance series data from personal monitoring devices.

There is a need to develop more advanced tools to improve guidance on physical exercise to reduce risk of adverse events and improve benefits of exercise. Vast amounts of data are generated continuously by Personal Monitoring Devices (PMDs) from sports events, biomedical experiments, and fitness self-monitoring that may be used to guide physical exercise. Most of these data are sampled as time- or distance-series. However, the inherent high-dimensionality of exercise data is a challenge during processing. As a result, current data analysis from PMDs seldomly extends beyond aggregates. Common challanges are:•alterations in data density comparing the time- and the distance domain;•large intra and interindividual variations in the relationship between numerical data and physiological properties;•alterations in temporal statistical properties of data derived from exercise of different exercise durations. These challenges are currently unresolved leading to suboptimal analytic models. In this paper, we present algorithms and approaches to address these problems, allowing the analysis of complete PMD datasets, rather than having to rely on cumulative statistics. Our suggested approaches permit effective application of established Symbolic Aggregate Approximation modeling and newer deep learning models, such as LSTM.