University of Oulu

Iman Alikhani et al 2018 Physiol. Meas. 39 115002,

Characterization and reduction of exercise-based motion influence on heart rate variability using accelerator signals and channel decoding in the time–frequency domain

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Author: Alikhani, Iman1; Noponen, Kai1; Hautala, Arto1;
Organizations: 1Physiological Signal Analysis Team, Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
Persistent link:
Language: English
Published: IOP Publishing, 2018
Publish Date: 2019-10-25


Objective: Heart rate variability (HRV) is defined as the variation of the heart’s beat to beat time intervals. Although HRV has been studied for decades, its response to stress tests and off-rest measurements is still under investigation. In this paper, we studied the influence of motion on HRV throughout different exercise tests, including a maximal running of healthy recreational runners, cycling, and walking tests of healthy subjects.

Approach: In our proposed method, we utilized the motion trajectory (which is known to exist partially in HRV) measured by a three-channel accelerator (ACC). We then estimated their shares in HRV using a wearable electrocardiogram (ECG) and an error-correcting problem formulation. In this method, we characterized the motion components of three orthogonal directions induced into the HRV signal, and then we suppressed the estimated motion artefact to construct a motion-attenuated spectrogram.

Main results and Significance: Our analysis showed that HRV in the exercise context is susceptible to motion artefacts. Furthermore, the interpretation of autonomic nervous system (ANS) activity and HRV indices throughout exercise has a high margin of error depending on the intensity level, type of exercise, and motion trajectory. Our experiment on 84 healthy subjects throughout mid-intensity cycling and walking tests showed 39% and 32% influence on average, respectively. In addition, our proposed method revealed through a maximal running test with 11 runners that motion can describe on average 20%–40% of the HRV high-frequency (HF) energy at different workloads of running.

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Series: Physiological measurement
ISSN: 0967-3334
ISSN-E: 1361-6579
ISSN-L: 0967-3334
Volume: 39
Issue: 11
Article number: 115002
DOI: 10.1088/1361-6579/aadeff
Type of Publication: A1 Journal article – refereed
Field of Science: 3111 Biomedicine
315 Sport and fitness sciences
3121 General medicine, internal medicine and other clinical medicine
Copyright information: © 2018 Institute of Physics and Engineering in Medicine. This is an author-created, un-copyedited version of an article published in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at