A novel spectral entropy-based index for assessing the depth of anaesthesia

Sook, Jee; Li, Tianning; Li, Yan

doi:10.1186/s40708-021-00130-8

Cited by 15 publications

(8 citation statements)

References 36 publications

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“…The performance accuracy of the binary (92%) and multiclass (52%) SVMs is slightly lower than the accuracies of SVM models in other anaesthetics [24][25][26][27][28][29][30]. This could be because nitrous oxide produces less pronounced EEG effects.…”

Section: Discussionmentioning

confidence: 87%

“…These features are commonly used in EEG analysis for level of anaesthesia classification as they are easy to calculate, and have a physiological meaning as they are known to be related to certain brain states and to be driven by specific brain regions [23][24][25][26][27], making them biologically explainable. Besides frequency, entropy is also commonly used as a feature input for SVM modelling [24,26,[28][29][30]. SVM classification of the depth of anaesthesia for all kinds of anaesthetics (excluding nitrous oxide) has been compared with other machine-learning algorithms, like random forest classification, regression, and artificial neural networks, with mixed results [26,27,29].…”

Section: Introductionmentioning

confidence: 99%

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Support-vector classification of low-dose nitrous oxide administration with multi-channel EEG power spectra

Vrijdag

Hallum

Tonks

et al. 2023

J Clin Monit Comput

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Support-vector machines (SVMs) can potentially improve patient monitoring during nitrous oxide anaesthesia. By elucidating the effects of low-dose nitrous oxide on the power spectra of multi-channel EEG recordings, we quantified the degree to which these effects generalise across participants. In this single-blind, cross-over study, 32-channel EEG was recorded from 12 healthy participants exposed to 0, 20, 30 and 40% end-tidal nitrous oxide. Features of the delta-, theta-, alpha- and beta-band power were used within a 12-fold, participant-wise cross-validation framework to train and test two SVMs: (1) binary SVM classifying EEG during 0 or 40% exposure (chance = 50%); (2) multi-class SVM classifying EEG during 0, 20, 30 or 40% exposure (chance = 25%). Both the binary (accuracy 92%) and the multi-class (accuracy 52%) SVMs classified EEG recordings at rates significantly better than chance (p < 0.001 and p = 0.01, respectively). To determine the relative importance of frequency band features for classification accuracy, we systematically removed features before re-training and re-testing the SVMs. This showed the relative importance of decreased delta power and the frontal region. SVM classification identified that the most important effects of nitrous oxide were found in the delta band in the frontal electrodes that was consistent between participants. Furthermore, support-vector classification of nitrous oxide dosage is a promising method that might be used to improve patient monitoring during nitrous oxide anaesthesia.

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Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Support-vector classification of low-dose nitrous oxide administration with multi-channel EEG power spectra

Vrijdag

Hallum

Tonks

et al. 2023

J Clin Monit Comput

View full text Add to dashboard Cite

show abstract

“…Judging the significance according to the corrected P value, P < 0.05 represented a significant difference, and 95% confidence intervals (CIs) were calculated. (3) For the PE, we also used the coefficient of determination (R 2 ) to quantify the degree of similarity between two PE curves that derived from rEEG and sEEG [52]. A high R 2 value meant high similarity between the raw and simulated PE curves.…”

Section: Discussionmentioning

confidence: 99%

Tracking the effects of propofol, sevoflurane and (S)-ketamine anesthesia using an unscented Kalman filter-based neural mass model

et al. 2023

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Objective: Neural mass model (NMM) has been widely used to investigate the neurophysiological mechanisms of anesthestic drugs induced general anesthesia (GA). However, whether the parameters of NMM could track the effects of anesthesia still unknown.Approach: We proposed using the cortical NMM (CNMM) to infer the potential neurophysiological mechanism of three different anesthetic drugs (i.e., propofol, sevoflurane, and (S)-ketamine) induced GA, and we employed unscented Kalman filter (UKF) to track any change in raw electroencephalography (rEEG) in frontal area during GA. We did this by estimating the parameters of population gain [i.e., excitatory/inhibitory postsynaptic potential (EPSP/IPSP, i.e., parameter A/B in CNMM) and the time constant rate of EPSP/IPSP (i.e., parameter a/b in CNMM). We compared the rEEG and simulated EEG (sEEG) from the perspective of spectrum, phase-amplitude coupling (PAC), and permutation entropy (PE). Main results: Under three estimated parameters (i.e., A, B, and a for propofol/sevoflurane or b for (S)-ketamine), the rEEG and sEEG had similar waveforms, time-frequency spectra, and PAC patterns during GA for the three drugs. The PE curves derived from rEEG and sEEG had high correlation coefficients (propofol: 0.97±0.03, sevoflurane: 0.96±0.03, (S)-ketamine: 0.98±0.02) and coefficients of determination (R2) (propofol: 0.86±0.03, sevoflurane: 0.68±0.30, (S)-ketamine: 0.70±0.18). Except for parameter A for sevoflurane, the estimated parameters for each drug in CNMM can differentiate wakefulness and non-wakefulness states. Compared with the simulation of three estimated parameters, the UKF-based CNMM had lower tracking accuracy under the simulation of four estimated parameters (i.e., A, B, a, and b) for three drugs.Significance: The results demonstrate that a combination of CNMM and UKF could track the neural activities during GA. The EPSP/IPSP and their time constant rate can interpert the anesthetic drug’s effect on the brain, and can be used as a new index for depth of anesthesia monitoring.

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“…Spectral entropy estimates the randomness of the signal based on the spectral amplitude over a specified frequency range [ 32 ]. Spectral entropy is calculated using the Shannon entropy formula, which is applied to the power spectral density of the EEG signal using Equation (7) [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

Hadiyoso

Zakaria

Ong

et al. 2023

Sensors

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Dementia is a term that represents a set of symptoms that affect the ability of the brain’s cognitive functions related to memory, thinking, behavior, and language. At worst, dementia is often called a major neurocognitive disorder or senile disease. One of the most common types of dementia after Alzheimer’s is vascular dementia. Vascular dementia is closely related to cerebrovascular disease, one of which is stroke. Post-stroke patients with recurrent onset have the potential to develop dementia. An accurate diagnosis is needed for proper therapy management to ensure the patient’s quality of life and prevent it from worsening. The gold standard diagnostic of vascular dementia is complex, includes psychological tests, complete memory tests, and is evidenced by medical imaging of brain lesions. However, brain imaging methods such as CT-Scan, PET-Scan, and MRI have high costs and cannot be routinely used in a short period. For more than two decades, electroencephalogram signal analysis has been an alternative in assisting the diagnosis of brain diseases associated with cognitive decline. Traditional EEG analysis performs visual observations of signals, including rhythm, power, and spikes. Of course, it requires a clinician expert, time consumption, and high costs. Therefore, a quantitative EEG method for identifying vascular dementia in post-stroke patients is discussed in this study. This study used 19 EEG channels recorded from normal elderly, post-stroke with mild cognitive impairment, and post-stroke with dementia. The QEEG method used for feature extraction includes relative power, coherence, and signal complexity; the evaluation performance of normal-mild cognitive impairment-dementia classification was conducted using Support Vector Machine and K-Nearest Neighbor. The results of the classification simulation showed the highest accuracy of 96% by Gaussian SVM with a sensitivity and specificity of 95.6% and 97.9%, respectively. This study is expected to be an additional criterion in the diagnosis of dementia, especially in post-stroke patients.

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A novel spectral entropy-based index for assessing the depth of anaesthesia

Cited by 15 publications

References 36 publications

Support-vector classification of low-dose nitrous oxide administration with multi-channel EEG power spectra

Support-vector classification of low-dose nitrous oxide administration with multi-channel EEG power spectra

Tracking the effects of propofol, sevoflurane and (S)-ketamine anesthesia using an unscented Kalman filter-based neural mass model

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

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