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EntropyVolume 24, Issue 1, January 2022, Article number 13

Reduction of artifacts in capacitive electrocardiogram signals of driving subjects(Article)(Open Access)

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  • Faculty of Technical Sciences, University of Novi Sad, Novi Sad, 21000, Serbia

Abstract

The development of smart cars with e-health services allows monitoring of the health condition of the driver. Driver comfort is preserved by the use of capacitive electrodes, but the recorded signal is characterized by large artifacts. This paper proposes a method for reducing artifacts from the ECG signal recorded by capacitive electrodes (cECG) in moving subjects. Two dominant artifact types are coarse and slow-changing artifacts. Slow-changing artifacts removal by classical filtering is not feasible as the spectral bands of artifacts and cECG overlap, mostly in the band from 0.5 to 15 Hz. We developed a method for artifact removal, based on estimating the fluctuation around linear trend, for both artifact types, including a condition for determining the presence of coarse artifacts. The method was validated on cECG recorded while driving, with the artifacts predominantly due to the movements, as well as on cECG recorded while lying, where the movements were performed according to a predefined protocol. The proposed method eliminates 96% to 100% of the coarse artifacts, while the slow-changing artifacts are completely reduced for the recorded cECG signals larger than 0.3 V. The obtained results are in accordance with the opinion of medical experts. The method is intended for reliable extraction of cardiovascular parameters to monitor driver fatigue status. © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Author keywords

Binarized approximate entropyCECG filterDDNNKNNMovement artefacts

Funding details

Funding sponsor Funding number Acronym
451-03-68/2021-14/200156,TR32040
  • 1

    Funding: This research was funded by the Serbian Ministry of Education, Science and Technology Development, under Grant 451-03-68/2021-14/200156 (TR32040).

  • ISSN: 10994300
  • Source Type: Journal
  • Original language: English
  • DOI: 10.3390/e24010013
  • Document Type: Article
  • Publisher: MDPI

  Škorić, T.; Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia;
© Copyright 2021 Elsevier B.V., All rights reserved.

Cited by 5 documents

Skoric, T. , Bajic, D.
Machine Learning-Based Detection of Driver Distraction by Capacitive Electrocardiogram Signals
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Škorić, T.
Stress Level Detection Based on the Capacitive Electrocardiogram Signals of Driving Subjects
(2023) Sensors
Klaic, L. , Stanesic, A. , Culjak, I.
Exploring the Wearable and Embeddable Solutions for Biopotential Signal Measurement: Dry and Non-Contact Technologies
(2023) BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings
View details of all 5 citations
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