A Transformer-based Prediction Method for Depth of Anesthesia During Target-controlled Infusion of Propofol and Remifentanil

TL;DR

This paper introduces a transformer-based deep learning approach combining LSTM, GRN, and attention mechanisms to accurately predict anesthesia depth during drug infusion, outperforming traditional models especially during abrupt changes.

Contribution

It presents a novel transformer-based prediction method integrating attention and advanced neural networks for improved anesthesia depth forecasting.

Findings

Outperforms traditional PK-PD models in accuracy.

Effectively predicts abrupt changes in anesthesia depth.

Addresses data imbalance with label smoothing and reweighting.

Abstract

Accurately predicting anesthetic effects is essential for target-controlled infusion systems. The traditional (PK-PD) models for Bispectral index (BIS) prediction require manual selection of model parameters, which can be challenging in clinical settings. Recently proposed deep learning methods can only capture general trends and may not predict abrupt changes in BIS. To address these issues, we propose a transformer-based method for predicting the depth of anesthesia (DOA) using drug infusions of propofol and remifentanil. Our method employs long short-term memory (LSTM) and gate residual network (GRN) networks to improve the efficiency of feature fusion and applies an attention mechanism to discover the interactions between the drugs. We also use label distribution smoothing and reweighting losses to address data imbalance. Experimental results show that our proposed method outperforms traditional PK-PD models and previous deep learning methods, effectively predicting anesthetic depth under sudden and deep anesthesia conditions.