Real-Time EEG Signal Classification for Monitoring and Predicting the Transition Between Different Anaesthetic States.

Quantitative identification of the transitions between anaesthetic states is very essential for optimizing patient safety and quality care during surgery but poses a very challenging task. The state-of-the-art monitors are still not capable of providing their manifest variables, so the practitioners must diagnose them based on their own experience. The present paper proposes a novel real-time method to identify these transitions.

Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals.

This study applies Bayesian techniques to analyze EEG signals for the assessment of the consciousness and depth of anesthesia (DoA). This method takes the limiting large-sample normal distribution as posterior inferences to implement the Bayesian paradigm. The maximum a posterior (MAP) is applied to denoise the wavelet coefficients based on a shrinkage function.

Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques.

This paper presents a new index to measure the hypnotic depth of anaesthesia (DoA) using EEG signals. This index is derived from applying combined Wavelet transform, eigenvector and normalisation techniques. The eigenvector method is first applied to build a feature function for six levels of coefficients in a discrete wavelet transform (DWT).

Measuring and reflecting depth of anesthesia using wavelet and power spectral density.

This paper evaluates depth of anesthesia (DoA) monitoring using a new index. The proposed method preconditions raw EEG data using an adaptive threshold technique to remove spikes and low-frequency noise. We also propose an adaptive window length technique to adjust the length of the sliding window.