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

Svane, Jakob; Wiktorski, Tomasz; Ørn, Stein; Eftestøl, Trygve Christian

doi:10.1016/j.mex.2023.102381

Cited by 4 publications

(2 citation statements)

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“…The accuracy of predictions relies on well-structured data and becomes crucial when dealing with large and complex time series data, encompassing multiple variables, such as weather temperature, seasonal variations influenced by weekends and public holidays, and web search queries related to the destination [21] . To analyze these relationships effectively, the Vector Autoregression (VAR) method may be used, which includes tests like the Granger causality test [22] , [23] .…”

Section: Methods Detailsmentioning

confidence: 99%

Forecasting of Indian tourism industry using modeling approach

Devi,

Agrawal,

Dhar

et al. 2024

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Section: Methods Detailsmentioning

confidence: 99%

Forecasting of Indian tourism industry using modeling approach

Devi,

Agrawal,

Dhar

et al. 2024

MethodsX

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“…For our data, we found that a median filter with a 5% threshold seemed to yield the best results. For artifact correction, we applied deletion, linear interpolation and autoregressive integrated moving average (ARIMA), based on results from [13]. Cubic interpolation was also applied, since it is the most commonly used method [14].…”

Section: Preprocessingmentioning

confidence: 99%

Coronary artery calcification is associated with adverse autonomic and hemodynamic responses to prolonged high-intensity endurance exercise

Svane,

Wiktorski,

Eftestøl

et al. 2024

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Background Physical exercise is associated with increased life duration and improved life quality. Paradoxically, high exercise intensity is also associated with increased coronary artery calcification (CAC) and a small but significant increased risk of myocardial infarction and cardiac death during high-intensity exercise. The mechanisms and the clinical implications of the association between prolonged high exercise intensity and increased CAC are unknown. Aim This study aims to determine if there are differences in cardiovascular hemodynamic measures and heart rate variability (HRV) in individuals with (CAC+) and without CAC (CAC-). Methods Hemodynamic measures from 56 healthy, middle-aged (51.0 [43.0-58.0] years [median, (Q1-Q3)]) individuals (41 men/15 women) participating in a 91-km (251.2 [217.2-271.6] minutes) leisure sport mountain bike race were included in this study. 25 participants (20 men/5 women), were classified as CAC+ based on coronary computer tomographic assessment. Hemodynamic measures and HRV were used to assess the study subjects at the top of the hardest hill (THH) during the last quarter of the race. Results At the top of THH, CAC+ individuals had significantly higher systolic blood pressure (235.0 [225.0-245.0] mmHg vs 220.0 [192.5-237.5] mmHg, p=0.008), higher diastolic blood pressure (DBP) (105.0 [95.0-110.0] mmHg vs 95.0 [85.0-110.0] mmHg, p=0.006), higher pulse pressure (130.0 [125.0-140.0] mmHg vs 123.0 [110.0-130.0] mmHg, p=0.039), higher mean rate pressure product (33882 [30872-35053] bpm x mmHg vs 31028 [27392-33047] bpm x mmHg, p=0.028), and larger increase in DBP from baseline (20.0 [20.0-30.0] mmHg vs 10.0 [0.0-20.0] mmHg, p=0.001), compared with CAC- individuals. Further, CAC+ showed a significant reduction in the low-frequency component of HRV (HRVLF) (6.3 [2.4-11.5] ms2 vs 12.4 [6.8-20.2] ms2, p=0.044). In multivariate analysis, HRVLF was an independent predictor of presence of CAC even after adjusting for established risk factors of atherosclerosis. Conclusion CAC+ individuals had significant alterations in hemodynamic measures and HRVLF following prolonged high-intensity exercise compared to individuals without CAC. HRVLF was an independent predictor of CAC, suggesting an adverse autonomic response to high-intensity exercise in individuals with CAC.

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