Multi-Scale Entropy and Neural Networks for Detection of Depth of Anaesthesia within EEG Signals

In recent years, multi-scale sample entropy (MSSE) is rapidly gaining popularity as an interesting tool for exploring neurophysiological mechanisms. In this paper, we propose a new method based on MSSE for on-line monitoring of the depth of anaesthesia (DoA) to quantify the anaesthetic effect with real-time electroencephalography by using MSSE. Empirical mode decomposition (EMD) was used successfully to filter EEG recordings of artefacts before the imitation of the recognition phases. Artificial neural networks were used to classify three scales of the MSSE into three stages of hypnosis. Results showed 95% accuracy of the tested clinical anaesthesia EEG recordings obtained for 20 patients.

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