Monitoring Level of Hypnosis Using Stationary Wavelet Transform and Singular Value Decomposition Entropy With Feedforward Neural Network

Abstract

Classifying the patient’s depth of anesthesia (LoH) level into a few distinct states may lead to inappropriate drug administration. To tackle the problem, this paper presents a robust and computationally efficient framework that predicts a continuous LoH index scale from 0–100 in addition to the LoH state. This paper proposes a novel approach for accurate LoH estimation based on Stationary Wavelet Transform (SWT) and fractal features. The deep learning model adopts an optimized temporal, fractal, and spectral feature set to identify the patient sedation level irrespective of age and the type of anesthetic agent. This feature set is then fed into a multilayer perceptron network (MLP), a class of feed-forward neural networks. A comparative analysis of regression and classification is made to measure the performance of the chosen features on the neural network architecture. The proposed LoH classifier outperforms the state-of-the-art LoH prediction algorithms with the highest accuracy of 97.1% while utilizing minimized feature set and MLP classifier. Moreover, for the first time, the LoH regressor achieves the highest performance metrics ( $\text{R}^{{{2}}}=0.9$ , MAE = 1.5) as compared to previous work. This study is very helpful for developing highly accurate monitoring for LoH which is important for intraoperative and postoperative patients’ health.

Keywords

• Level of hypnosis (LoH)
• multilayer perceptron (MLP)
• electroencephalogram (EEG)
• stationary wavelet transform (SWT)