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.2022 Aug 15;22(16):6099.
doi: 10.3390/s22166099.

Real-Time Depth of Anaesthesia Assessment Based on Hybrid Statistical Features of EEG

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Real-Time Depth of Anaesthesia Assessment Based on Hybrid Statistical Features of EEG

Yi Huang et al. Sensors (Basel)..

Abstract

This paper proposed a new depth of anaesthesia (DoA) index for the real-time assessment of DoA using electroencephalography (EEG). In the proposed new DoA index, a wavelet transform threshold was applied to denoise raw EEG signals, and five features were extracted to construct classification models. Then, the Gaussian process regression model was employed for real-time assessment of anaesthesia states. The proposed real-time DoA index was implemented using a sliding window technique and validated using clinical EEG data recorded with the most popular commercial DoA product Bispectral Index monitor (BIS). The results are evaluated using the correlation coefficients and Bland-Altman methods. The outcomes show that the highest and the average correlation coefficients are 0.840 and 0.814, respectively, in the testing dataset. Meanwhile, the scatter plot of Bland-Altman shows that the agreement between BIS and the proposed index is 94.91%. In contrast, the proposed index is free from the electromyography (EMG) effect and surpasses the BIS performance when the signal quality indicator (SQI) is lower than 15, as the proposed index can display high correlation and reliable assessment results compared with clinic observations.

Keywords: EEG; depth of anaesthesia; machine learning; real-time.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The diagram of the new DoA index development.
Figure 2
Figure 2
Results of denoising raw EEG data, including the low amplitude (time is between the 2000 s and 2010 s) and spike noise (time is between 2980 s and 2990 s). (a) Raw EEG data, patient ID: L12161431. (b) Raw EEG data with low amplitude noise. (c) Raw EEG data having spike noise. (d) EEG signal after denoising low amplitude with thresholdTh. (e) EEG signal after denoising spike with thresholdTh. (f) EEG signal after denoising low amplitude with proposed thresholdThnew. (g) EEG signal after denoising spike with proposed thresholdThnew.
Figure 3
Figure 3
Relationship between the Hurst range response value and the BIS value.
Figure 4
Figure 4
Predicted response from the SEGP-RM vs. actual values (BIS).
Figure 5
Figure 5
Response plot of the SEGR-RM for the 60 training subjects.
Figure 6
Figure 6
Pearson correlation coefficients of the testing results and BIS values for 13 subjects (patient 14 represents the average results of the 13 testing subjects).
Figure 7
Figure 7
(a) Bland-Altman plot betweenNDoA index and BIS. (b) Distribution plot between new DoA index and BIS.
Figure 8
Figure 8
The proposed index,NDoA, can monitor the anaesthetic states change, but BIS cannot when SQI is lower than 15 (subject ID: L01121339).
Figure 9
Figure 9
(a) Comparison betweenNDoA and BIS in the case of poor signal quality (subject ID: L01210841). (b) Zoom out during the period between 0 and 650 s. (c) Zoom out during the period between 16,940 and 17,748 s.
Figure 10
Figure 10
Comparison betweenNDoA and BIS in the case of poor signal quality (subject ID: L01131131).
Figure 11
Figure 11
Comparison betweenNDoA and BIS in the case of poor signal quality (subject ID: L01131400).
Figure 12
Figure 12
(a) EMG causes the BIS index to increase at two spikes, and the trend ofNDoA does not change during these periods. Subject ID: L01041002. (b) Zoom out during the period between 2500 and 3000 s.
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