Real-time approach to hrv analysis

Guilherme Ramos, Miquel Alfaras, Hugo Gamboa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present the assessment of heart rate variability (HRV) applied to real-time processing of electrocardiographic (ECG) signals. A general approach for R-peak detection is described based on the computational implementation of Pan and Tompkins algorithm, used in the offline version. Besides feature extraction (from temporal and frequency domain), the paper presents the development steps taken towards online real-time biosignal processing. The functional basis of the online approach consists in the implementation of a simple adaptive double-threshold algorithm for peak detection and a sliding window mechanism along acquisition that provides a dynamically generated tachogram for the features to be successively extracted, highlighting the new application opportunities for continuous observation of HRV parameters.

Original languageEnglish
Title of host publicationBIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018
EditorsHugo Gamboa, Sergi Bermudez i Badia, Giovanni Saggio, Ana Fred
PublisherSciTePress
Pages208-215
Number of pages8
Volume4
ISBN (Electronic)9789897582790
Publication statusPublished - 1 Jan 2018
Event11th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2018 - Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018 - Funchal, Madeira, Portugal
Duration: 19 Jan 201821 Jan 2018

Conference

Conference11th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2018 - Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018
CountryPortugal
CityFunchal, Madeira
Period19/01/1821/01/18

Fingerprint

Processing
Feature extraction

Keywords

  • Biomedical Signal Processing
  • HRV Analysis
  • Real-Time Processing
  • Sliding Window

Cite this

Ramos, G., Alfaras, M., & Gamboa, H. (2018). Real-time approach to hrv analysis. In H. Gamboa, S. Bermudez i Badia, G. Saggio, & A. Fred (Eds.), BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018 (Vol. 4, pp. 208-215). SciTePress.
Ramos, Guilherme ; Alfaras, Miquel ; Gamboa, Hugo. / Real-time approach to hrv analysis. BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. editor / Hugo Gamboa ; Sergi Bermudez i Badia ; Giovanni Saggio ; Ana Fred. Vol. 4 SciTePress, 2018. pp. 208-215
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title = "Real-time approach to hrv analysis",
abstract = "In this paper, we present the assessment of heart rate variability (HRV) applied to real-time processing of electrocardiographic (ECG) signals. A general approach for R-peak detection is described based on the computational implementation of Pan and Tompkins algorithm, used in the offline version. Besides feature extraction (from temporal and frequency domain), the paper presents the development steps taken towards online real-time biosignal processing. The functional basis of the online approach consists in the implementation of a simple adaptive double-threshold algorithm for peak detection and a sliding window mechanism along acquisition that provides a dynamically generated tachogram for the features to be successively extracted, highlighting the new application opportunities for continuous observation of HRV parameters.",
keywords = "Biomedical Signal Processing, HRV Analysis, Real-Time Processing, Sliding Window",
author = "Guilherme Ramos and Miquel Alfaras and Hugo Gamboa",
note = "info:eu-repo/grantAgreement/EC/H2020/722022/EU# The authors acknowledge the support received from ITN AffecTech under the Marie Skłodowska Curie Actions (ERC H2020 Project ID: 722022).",
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Ramos, G, Alfaras, M & Gamboa, H 2018, Real-time approach to hrv analysis. in H Gamboa, S Bermudez i Badia, G Saggio & A Fred (eds), BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. vol. 4, SciTePress, pp. 208-215, 11th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2018 - Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018, Funchal, Madeira, Portugal, 19/01/18.

Real-time approach to hrv analysis. / Ramos, Guilherme; Alfaras, Miquel; Gamboa, Hugo.

BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. ed. / Hugo Gamboa; Sergi Bermudez i Badia; Giovanni Saggio; Ana Fred. Vol. 4 SciTePress, 2018. p. 208-215.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Real-time approach to hrv analysis

AU - Ramos, Guilherme

AU - Alfaras, Miquel

AU - Gamboa, Hugo

N1 - info:eu-repo/grantAgreement/EC/H2020/722022/EU# The authors acknowledge the support received from ITN AffecTech under the Marie Skłodowska Curie Actions (ERC H2020 Project ID: 722022).

PY - 2018/1/1

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N2 - In this paper, we present the assessment of heart rate variability (HRV) applied to real-time processing of electrocardiographic (ECG) signals. A general approach for R-peak detection is described based on the computational implementation of Pan and Tompkins algorithm, used in the offline version. Besides feature extraction (from temporal and frequency domain), the paper presents the development steps taken towards online real-time biosignal processing. The functional basis of the online approach consists in the implementation of a simple adaptive double-threshold algorithm for peak detection and a sliding window mechanism along acquisition that provides a dynamically generated tachogram for the features to be successively extracted, highlighting the new application opportunities for continuous observation of HRV parameters.

AB - In this paper, we present the assessment of heart rate variability (HRV) applied to real-time processing of electrocardiographic (ECG) signals. A general approach for R-peak detection is described based on the computational implementation of Pan and Tompkins algorithm, used in the offline version. Besides feature extraction (from temporal and frequency domain), the paper presents the development steps taken towards online real-time biosignal processing. The functional basis of the online approach consists in the implementation of a simple adaptive double-threshold algorithm for peak detection and a sliding window mechanism along acquisition that provides a dynamically generated tachogram for the features to be successively extracted, highlighting the new application opportunities for continuous observation of HRV parameters.

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M3 - Conference contribution

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BT - BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018

A2 - Gamboa, Hugo

A2 - Bermudez i Badia, Sergi

A2 - Saggio, Giovanni

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Ramos G, Alfaras M, Gamboa H. Real-time approach to hrv analysis. In Gamboa H, Bermudez i Badia S, Saggio G, Fred A, editors, BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. Vol. 4. SciTePress. 2018. p. 208-215