An intelligent model to predict ANI in patients undergoing general anesthesia

  • Jove E
  • Gonzalez-Cava J
  • Casteleiro-Roca J
 et al. 
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© 2018, Springer International Publishing AG. One of the main challenges in anesthesia is the proposal of safe and efficient methods to administer drugs to regulate the pain that the patient is sufffering during the surgical process. First steps towards this objective is the proposal of adequate indexes that correlate well with analgesia. One of the most promising index is ANI (Antinociception Index). This research focuses on the modelling of the ANI response in patients undergoing general anesthesia with intravenous drug infusion. The aim is to predict the ANI response in terms of the analgesic infusion rate. For this a model based on intelligent regression techniques is proposed. To create the model, it has been checked Artificial Neural Networks (ANN) and Support Vector Regression (SVR). Results were validated using data from patients in the operating room. The measured performance attest for the potential of the proposed technique.

Author-supplied keywords

  • ANI (Analgesia Nociception Index)
  • EMG (ElectroMyoGram signal)
  • MLP (Multi-Layer Perceptron)
  • SVR (Support Vector Regression)

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