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ECG Feature-Based Classification of Induced Pain Levels

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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14470))

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Abstract

Appropriate pain treatment relies on an accurate assessment of pain. Limitations regarding subjective reporting of pain or observational bias, when pain is assessed by a healthcare professional, can lead to inadequate pain treatment. Therefore, pain assessment using physiological signals has been studied in past years due to the importance of objective measurement. The aim of this work is to use features extracted from Electrocardiogram (ECG) signals to classify pain induced by a Cold Pressor Task (CPT). Specifically, the goal is to determine the optimal hyperparameters of the classification algorithms and the optimal features for accurately distinguishing between higher and lower levels of pain. A model combining 15 ECG-features related to the P, R, S, and T waves and the Random Forest algorithm provided the best performance for predicting induced pain levels. This model achieved an accuracy of 95.3%, an F1-score of 94.0%, a precision of 97.9%, and a recall of 90.4%. These results show the feasibility of identifying pain through the physiological characteristics of the ECG.

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References

  1. Aggarwal, C.C.: Data Mining. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14142-8

    Book  MATH  Google Scholar 

  2. Al-Qerem, A.: An efficient machine-learning model based on data augmentation for pain intensity recognition. Egyptian Inform. J. 21, 241-257 (2020). (https://doi.org/10.1016/j.eij.2020.02.006), https://doi.org/10.3389/fnins.2017.00279

  3. Breiman, L.: Classification and Regression Trees (1984). https://doi.org/10.1201/9781315139470

  4. Breiman, L.: Random forests - random features, pp. 1–29 (1999)

    Google Scholar 

  5. Breivik, H., et al.: Assessment of pain. Br. J. Anaesth. 101(1), 17–24 (2008). https://doi.org/10.1093/bja/aen103

    Article  Google Scholar 

  6. Chu, Y., Zhao, X., Han, J., Su, Y.: Physiological signal-based method for measurement of pain intensity. Front. Neurosci. 11(279) (2017). https://doi.org/10.3389/fnins.2017.00279

  7. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28(2), 337–407 (2000). https://doi.org/10.1214/aos/1016120463

    Article  MathSciNet  MATH  Google Scholar 

  8. Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: Knn model-based approach in classification, vol. 2888, pp. 986–996 (2003). https://doi.org/10.1007/978-3-540-39964-3_62

  9. Hummel, P., van Dijk, M.: Pain assessment: current status and challenges. Semin. Fetal Neonatal. Med. 11(4), 237–245 (2006)

    Article  Google Scholar 

  10. Ledowski, T., Bromilow, J., Wu, J., Paech, M.J., Storm, H., Schug, S.A.: The assessment of postoperative pain by monitoring skin conductance: results of a prospective study. Anaesthesia 62(10), 989–993 (2007)

    Article  Google Scholar 

  11. Lim, H., Kim, B., Noh, G.J., Yoo, S.K.: A deep neural network-based pain classifier using a photoplethysmography signal. Sensors 19(2) (2019). https://doi.org/10.3390%2Fs19020384

  12. Maxwell, L.G., Fraga, M.V., Malavolta, C.P.: Assessment of pain in the newborn: an update. Clin. Perinatol. 46(4), 693–707 (2019). https://doi.org/10.1016/j.clp.2019.08.005

    Article  Google Scholar 

  13. Naeini, E.K., et al.: Pain recognition with electrocardiographic features in postoperative patients: method validation study. J. Med. Internet Res. 23(5), e25079 (2021). https://doi.org/10.2196/25079

    Article  Google Scholar 

  14. Natekin, A., Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorobot. 7 (2013). https://doi.org/10.3389/fnbot.2013.00021

  15. Saladin, S.K.: Human Anatomy, 5 edn. McGraw-Hill Education (2017)

    Google Scholar 

  16. Silva, P., Sebastião, R.: Using the electrocardiogram for pain classification under emotional contexts. Sensors 23(3), 1443 (2023). https://doi.org/10.3390/s2303144

    Article  Google Scholar 

  17. Thiam, P., Bellmann, P., Kestler, H.A., Schwenker, F.: Exploring deep physiological models for nociceptive pain recognition. Sensors 19(4503) (2019). https://doi.org/10.3390/s19204503

  18. Varma, S., Simon, R.: Bias in error estimation when using cross-validation for model selection. BMC Bioinform. 7(91), 3242–3249 (2006). https://doi.org/10.1186/1471-2105-7-91

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded by national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., under the PhD grant UI/BD/153374/2022 (D.P.), under the Scientific Employment Stimulus CEECIND/03986/2018 (R.S.) and CEECINST/00013/2021 (R.S.), within the R &D unit IEETA/UA (UIDB/00127/2020), and under the project EMPA (2022.05005.PTDC).

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Authors and Affiliations

  1. IEETA, DETI, LASI, University of Aveiro, 3810-193, Aveiro, Portugal

    Daniela Pais & Raquel Sebastião

  2. Polytechnic of Viseu, 3504-510, Viseu, Portugal

    Raquel Sebastião

Authors
  1. Daniela Pais
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  2. Raquel Sebastião
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Correspondence to Daniela Pais .

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Editors and Affiliations

  1. Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Coimbra, Portugal

    Verónica Vasconcelos

  2. Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Coimbra, Portugal

    Inês Domingues

  3. Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Coimbra, Portugal

    Simão Paredes

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Pais, D., Sebastião, R. (2024). ECG Feature-Based Classification of Induced Pain Levels. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14470. Springer, Cham. https://doi.org/10.1007/978-3-031-49249-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-49249-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49248-8

  • Online ISBN: 978-3-031-49249-5

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