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Pain scores estimation using surgical pleth index and long short-term memory neural networks

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Abstract

Pain monitoring is crucial to provide proper healthcare for patients during general anesthesia (GA). In this study, photoplethysmographic waveform amplitude (PPGA), heartbeat interval (HBI), and surgical pleth index (SPI) are utilized for predicting pain scores during GA based on expert medical doctors’ assessments (EMDAs). Time series features are fed into different long short-term memory (LSTM) models, with different hyperparameters. The models’ performance is evaluated using mean absolute error (MAE), standard deviation (SD), and correlation (Corr). Three different models are used, the first model resulted in 6.9271 ± 1.913, 9.4635 ± 2.456, and 0.5955 0.069 for an overall MAE, SD, and Corr, respectively. The second model resulted in 3.418 ± 0.715, 3.847 ± 0.557, and 0.634 ± 0.068 for an overall MAE, SD, and Corr, respectively. In contrast, the third model resulted in 3.4009 ± 0.648, 3.909 ± 0.548, and 0.6197 ± 0.0625 for an overall MAE, SD, and Corr, respectively. The second model is selected as the best model based on its performance and applied 5-fold cross-validation for verification. Statistical results are quite similar: 4.722 ± 0.742, 3.922 ± 0.672, and 0.597 ± 0.053 for MAE, SD, and Corr, respectively. In conclusion, the SPI effectively predicted pain score based on EMDA, not only on good evaluation performance, but the trend of EMDA is replicated, which can be interpreted as a relation between SPI and EMDA; however, further improvements on data consistency are also needed to validate the results and obtain better performance. Furthermore, the usage of further signal features could be considered along with SPI.

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Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due state restrictions such as privacy and ethical restrictions.

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Acknowledgements

This research is supported by Ministry of Science and Technology (Grant number: MOST 110-2221-E-155 -004—MY2).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Yuan Ze University, Taoyuan, 320, Taiwan

    Omar M. T. Abdel Deen, Wei-Horng Jean & Jiann-Shing Shieh

  2. Department of Anesthesiology, Far Eastern Memorial Hospital, Banqiao District, New Taipei City, 220, Taiwan

    Wei-Horng Jean

  3. Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei, 100, Taiwan

    Shou-Zen Fan

  4. Department of Anesthesiology, En Chu Kong Hospital, New Taipei City, 237, Taiwan

    Shou-Zen Fan

  5. Department of Electronics and Electrical Engineering, Brunel University London, London, UB8 3PH, UK

    Maysam F. Abbod

Authors
  1. Omar M. T. Abdel Deen
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  2. Wei-Horng Jean
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  3. Shou-Zen Fan
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  4. Maysam F. Abbod
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  5. Jiann-Shing Shieh
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Correspondence to Jiann-Shing Shieh.

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This work was presented in part at the joint symposium of the 28th International Symposium on Artificial Life and Robotics, the 8th International Symposium on BioComplexity, and the 6th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Beppu, Oita, and Online, January 25–27, 2023).

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Abdel Deen, O.M.T., Jean, WH., Fan, SZ. et al. Pain scores estimation using surgical pleth index and long short-term memory neural networks. Artif Life Robotics 28, 600–608 (2023). https://doi.org/10.1007/s10015-023-00880-0

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  • DOI: https://doi.org/10.1007/s10015-023-00880-0

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