Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method

Abstract

Distinguishing between awake and anesthetized states is one of the important problems in surgery. Vital signals contain valuable information that can be used in prediction of different levels of anesthesia. Some monitors based on electroencephalogram (EEG) such as the Bispectral (BIS) index have been proposed in recent years. This study proposes a new method for characterizing between awake and anesthetized states. We validated our method by obtaining data from 25 patients during the cardiac surgery that requires cardiopulmonary bypass. At first, some linear and non-linear features are extracted from EEG signals. Then a method called "LLE"(Locally Linear Embedding) is used to map high-dimensional features in a three-dimensional output space. Finally, low dimensional data are used as an input to a quadratic discriminant analyzer (QDA). The experimental results indicate that an overall accuracy of 88.4 % can be obtained using this method for classifying the EEG signal into conscious and unconscious states for all patients. Considering the reliability of this method, we can develop a new EEG monitoring system that could assist the anesthesiologists to estimate the depth of anesthesia accurately.

Mechanisms underlying brain monitoring during anesthesia: limitations, possible improvements, and perspectives

“…Others authors have focused on building new algorithms, starting from the analysis of EEG signals. For example, Mirsadeghi et al [ 58 ] proposed a new method for distinguishing between awake and anesthetised states. Their methodology is very interesting: through a specific analysis that uses a dimensionality reduction method (from high-dimensional to low-dimensional data), they processed some linear and non-linear features of raw EEG signals, citing an accuracy of 88.4% for classifying the EEG signal into conscious and unconscious states.…”

Monitoring the depth of anesthesia using Autoregressive model and Sample entropy

“…Although these methods can initially extract the characteristics of EEG signals, they have a common flaw: none of these parameters can independently monitor DOA. Therefore, many studies extracted the characteristics of EEG signals using multiple parameters [21,22].…”

Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs

“…For example, compressed spectral array (CSA) [11] focuses on the change in frequency characteristic of the electroencephalogram (EEG) during anesthesia; spectral edge frequency (SEF) [12], frequency band power ratio [13], or spectral entropy (SpE) [14] measure the change in the pattern of the power spectrum; mid-latency auditory evoked potential (MLAEP) [15] examined the response of the electroencephalogram (EEG) to an auditory stimulus or the bispectral index (BIS) using phase coupling between EEG frequency components [16], the latter of which is currently in clinical use for DOA monitoring [17–19]. However, these previous methods mainly use few EEG channels independently and most of multichannel EEG based studies are limited to specific regions of the brain.…”