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Comparison of outlier heartbeat identification and spectral transformation strategies for deriving heart rate variability indices for drivers at different stages of sleepiness
Chalmers Tekniska Högskola.
Chalmers Tekniska Högskola.
Chalmers Tekniska Högskola.
Autoliv Development AB.
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2018 (English)In: Traffic Injury Prevention, ISSN 1538-9588, E-ISSN 1538-957X, Vol. 19, p. S112-S119Article in journal (Refereed) Published
Abstract [en]

Objective: Appropriate preprocessing for detecting and removing outlier heartbeats and spectral transformation is essential for deriving heart rate variability (HRV) indices from cardiac monitoring data with high accuracy. The objective of this study is to evaluate agreement between standard preprocessing methods for cardiac monitoring data used to detect outlier heartbeats and perform spectral transformation, in relation to estimating HRV indices for drivers at different stages of sleepiness.

Methods: The study analyzed more than 3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. Electrocardiography (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The Pan-Tompkins algorithm was used for peak detection of heartbeats from ECG data. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat interval deviating >30% from the mean of the 4 previous intervals, and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain; these methods were (1) the Fourier transform; (2) an autoregressive model; and (3) the Lomb-Scargle periodogram. The preprocessing methods were compared quantitatively and by assessing agreement between estimations of 13 common HRV indices using Bland-Altman plots and paired Student's t-tests.

Results: The PC method detected more than 4times as many outliers (0.28%) than SD (0.065%). Most HRV indices derived using different preprocessing methods exhibited significant systematic (P <.05) and substantial random variations.

Conclusions: The standard preprocessing methods for HRV data for outlier heartbeat detection and spectral transformation show low levels of agreement. This finding implies that, prior to designing algorithms for detection of sleepy drivers based on HRV analysis, the impact of different preprocessing methods and combinations thereof on driver sleepiness assessment needs to be studied.

Place, publisher, year, edition, pages
TAYLOR & FRANCIS INC , 2018. Vol. 19, p. S112-S119
Keywords [en]
Driver sleepiness, heart rate variability, outlier heartbeat detection, spectral transformation
National Category
Medical Laboratory and Measurements Technologies
Identifiers
URN: urn:nbn:se:vti:diva-13055DOI: 10.1080/15389588.2017.1393073ISI: 000428682800017PubMedID: 29584487Scopus ID: 2-s2.0-85044627374OAI: oai:DiVA.org:vti-13055DiVA, id: diva2:1221704
Available from: 2018-06-20 Created: 2018-06-20 Last updated: 2018-06-20Bibliographically approved

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Anund, Anna

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