Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Krolak A

PROVIDER: S-EPMC7664660 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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

Królak Aleksandra A Wiktorski Tomasz T Bjørkavoll-Bergseth Magnus Friestad MF Ørn Stein S

Sensors (Basel, Switzerland) 20201108 21

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 H ...[more]

PMID: 33171676

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Project description:Importance:Persons with knee symptoms recognize the health benefits of engaging in physical activity, but uncertainty persists about whether regular strenuous physical activity or exercise can accelerate tissue damage. A sedentary lifestyle of inactivity or underloading may also be associated with deleterious joint health. Objective:To establish whether long-term strenuous physical activity participation and extensive sitting behavior are each associated with increased risk of developing radiographic knee osteoarthritis (KOA) in individuals at high risk for the disease. Design, Setting, and Participants:This cohort study analyzed data from the Osteoarthritis Initiative, a prospective longitudinal cohort study of men and women with or at an increased risk of developing symptomatic, radiographic KOA. Community-dwelling adults were recruited from 4 US sites (Baltimore, Maryland; Columbus, Ohio; Pittsburgh, Pennsylvania; and Pawtucket, Rhode Island) and were followed up for up to 10 years. Individuals were included if they had a baseline Kellgren and Lawrence grade of 0 in both knees and completed a PASE (Physical Activity Scale for the Elderly) questionnaire at baseline and at least 2 follow-up visits over an 8-year interval. Data analyses were conducted from May 2018 to November 2018. Exposures:Baseline to 8-year trajectories of strenuous physical activity participation and extensive sitting behavior were identified using group-based trajectory models. Main Outcomes and Measures:Incident radiographic KOA, defined as Kellgren and Lawrence grade 2 or higher in either knee by the 10-year follow-up visit. Results:A total of 1194 participants were included in the sample (697 women [58.4%]), with a baseline mean (SD) age of 58.4 (8.9) years and mean body mass index (BMI) of 26.8 (4.5). Four distinct trajectories of weekly hours spent in strenuous physical activities and 3 distinct trajectories of extensive sitting were identified. Long-term engagement in low-to-moderate physical activities (adjusted odds ratio [OR], 0.69; 95% CI, 0.48-1.01) or any strenuous physical activities (adjusted OR, 0.75; 95% CI, 0.53-1.07) was not associated with 10-year incident radiographic KOA. Persistent extensive sitting was not associated with incident KOA. Despite relatively mild symptoms and high function in this early-stage sample, 594 participants (49.7%) did not engage in any strenuous physical activities (ie, 0 h/wk) across 8 years, and 507 (42.5%) engaged in persistent moderate-to-high frequency of extensive sitting. Older age, higher BMI, more severe knee pain, non-college graduate educational level, weaker quadriceps, and depression were each associated with a persistent lack of engagement in strenuous physical activities. Conclusions and Relevance:Results from this study appeared to show no association between long-term strenuous physical activity participation and incident radiographic KOA. The findings raise the possibility of a protective association between incident KOA and a low-to-moderate level of strenuous physical activities.

Dataset Information

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

Publications

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

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