Loss of Consciousness Early Warning Modelling with EEG Signals

An accurate Depth of Anaesthesia (DoA) assessment is crucial for enhancing a patient's surgical experience, as it helps avoid intraoperative (anaesthesia) awareness and reduces postoperative recovery time and cognitive dysfunction. This paper presents two novel research outcomes. The first is the development of a new DoA index using processed electroencephalogram (EEG) signals and machine learning techniques. This research uniquely combines powerful features extracted by two distinct time series decomposition methods: the Fast Fourier Transform (FFT) and Variation Mode Decomposition (VMD). Permutation Entropy, Multiscale (Permutation) Lempel-Ziv Complexity, Hjorth's mobility, and Petrosian Fractal Dimension features trained a Support Vector Regressor producing a highly responsive DoA index of 85.8% accuracy. Secondly, a novel fixed-period Loss of Consciousness (LoC) early warning system is developed. Using the same feature set, a KNeighbors (KNN) classifier achieves an accuracy and Area Under the Receiver Operating Characteristic Curve of 82% after Synthetic Minority Oversampling Technique (SMOTE) dataset imbalance correction. The KNN model outperforms Decision Trees, Random Forests and Support Vector classification. Reliably predicting the LoC within a fixed period would greatly assist medical practitioners by ensuring an appropriate level of anaesthetic is administered to achieve the LoC, thus reducing the risk of anaesthesia awareness and preventing anaesthetic overdose.