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

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.