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Exploring Alterations in Electrocardiogram During the Postoperative Pain

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Pattern Recognition and Image Analysis (IbPRIA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13256))

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Abstract

The assessment of postoperative pain after a surgical procedure is a critical step to guarantee a suitable analgesic control of pain, and currently, it is based on the self-reports of the patients. However, these assessment methods are subjective, discontinuous, and inadequate for evaluating the pain of patients unable or with limited ability to communicate verbally. Developing an objective and continuous tool for assessing and monitoring postoperative pain, which does not require patient reports could assist pain management during the patient stay in the post-surgery care unit and, ultimately, promote better recovery. In the last years, the evaluation of pain through physiological indicators has been investigated. In the present work, electrocardiogram (ECG) signals collected from 19 patients during the postoperative period were studied in order to find relationships between physiological alterations and pain and identify which ECG-feature or combination of ECG-features better describe postoperative pain.

Considering a multivariate approach, analysing the performance of sets of two or more ECG-features using clustering algorithms proved to be promising, allowing the identification of different pain characteristics based on the extracted features from the ECG signals.

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Notes

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    https://neurokit2.readthedocs.io/en/latest/.

  2. 2.

    https://scipy.org/.

  3. 3.

    https://scikit-learn.org/stable/.

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Acknowledgments

This work was funded by national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., under the Scientific Employment Stimulus - Individual Call - CEECIND/03986/2018, and is also supported by the FCT through national funds, within IEETA/UA R&D unit (UIDB/00127/2020). This work is also funded by national funds, European Regional Development Fund, FSE through COMPETE2020, through FCT, in the scope of the framework contract foreseen in the numbers 4, 5, and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19.

Particular thanks are due to the clinical team for allowing and supporting the researchers of this work during the procedure of data collection. The authors also acknowledge all volunteers that participated in this study.

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Authors and Affiliations

  1. Institute of Electronics and Informatics Engineering of Aveiro (IEETA) Department of Electronics, Telecommunications and Informatics (DETI), University of Aveiro, 3810-193, Aveiro, Portugal

    Daniela Pais, Susana Brás & Raquel Sebastião

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  1. Daniela Pais
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  2. Susana Brás
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  3. Raquel Sebastião
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Corresponding author

Correspondence to Daniela Pais .

Editor information

Editors and Affiliations

  1. University of Aveiro, Aveiro, Portugal

    Armando J. Pinho

  2. University of Aveiro, Aveiro, Portugal

    Petia Georgieva

  3. University of Porto, Porto, Portugal

    Luís F. Teixeira

  4. Universitat Politècnica de València, Valencia, Spain

    Joan Andreu Sánchez

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Pais, D., Brás, S., Sebastião, R. (2022). Exploring Alterations in Electrocardiogram During the Postoperative Pain. In: Pinho, A.J., Georgieva, P., Teixeira, L.F., Sánchez, J.A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2022. Lecture Notes in Computer Science, vol 13256. Springer, Cham. https://doi.org/10.1007/978-3-031-04881-4_14

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  • DOI: https://doi.org/10.1007/978-3-031-04881-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04880-7

  • Online ISBN: 978-3-031-04881-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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