Depth of anaesthesia assessment based on adult electroencephalograph beta frequency band

Abstract

This paper presents a new method to apply timing characteristics of electroencephalograph (EEG) beta frequency bands to assess the depth of anaesthesia (DoA). Firstly, the measured EEG signals are denoised and decomposed into 20 different frequency bands. The Mobility (M), permutation entropy (PE) and Lempel-Ziv complexity (LCZ) of each frequency band are calculated. The M, PE and LCZ values of beta frequency bands (21.5-30 Hz) are selected to derive a new index. The new index is evaluated and compared with measured bispectral (BIS). The results show that there is a very close correlation between the proposed index and the BIS during different anaesthetic states. The new index also shows a 25-264 s earlier time response than BIS during the transient period of anaesthetic states. In addition, the proposed index is able to continuously assess the DoA when the quality of signal is poor and the BIS does not have any valid outputs.

EEG sleep stages identification based on weighted undirected complex networks

“…As EEG signals vary over time, we processed each EEG segment into quasi stationary by dividing it into sub-blocks using a sliding window technique. A sliding window technique was applied by Li and Wen [38,39] In this paper, each segment was partitioned into predetermined overlapping intervals called strata or blocks. We kept the original segment length of 30 seconds based on Hypnograms associated with the datasets.…”

“…The least square support vector machine (LS-SVM) was first developed by Suyken and Vandewalle [61,62] as a modified version of the original support vector machine [5,6]. It was used by Siuly et al [38] for the motor image classification, also by Al Ghayab et al [4] for detecting the epileptic EEG signals.…”

A novel empirical wavelet SODP and spectral entropy based index for assessing the depth of anaesthesia

“…This index reflects the patient’s anaesthetic states during surgery and may assist the anaesthetist in providing appropriate medication for maintaining appropriate levels of unconsciousness. In recent studies, Li and Wen [ 11 ], Diykh et al [ 2 ], and Nguyen-Ky et al [ 12 , 13 ] addressed the limitations of its observed susceptibility to interference from noise, time delay, and inconsistency between patients in the BIS index. Continual research and development in refining EEG based DoA algorithms are necessary to assist medical professionals in the care of patients undergoing surgery.…”

“…Saadeh et al [ 8 ] proposed a method for DoA classification based on spectral estimation methods, including spectral edge frequency, beta ratio, and spectral energy; these selected features are known to be present in the BIS algorithm [ 8 , 11 , 16 , 17 ] and have been shown to develop indices with close representation to the BIS index. This work implemented a band-pass filter for signal denoising, and a fine decision tree classifier was used in the DoA index design.…”

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

“…Empirical mode decomposition (EMD) [11] and its variants have been employed to filter the samples of artefacts and obtain a clean EEG before attempting recognition. Some studies have investigated the value of different frequency bands in EEG [16]in the measurement of the DoA; for example, the alpha-frequency band showed a strong correlation to the level of consciousness during surgery under anaesthesia. Furthermore, some studies have utilised the chaotic feature as described by the chaos theory to follow the hypnotic levels [9].…”