Electroencephalogram Based Detection of Deep Sedation in ICU Patients Using Atomic Decomposition

Nagaraj, Sunil Belur; McClain, Lauren; Boyle, Emily J.; Ramaswamy, Sowmya M.; Biswal, Siddharth; Akeju, Oluwaseun; Purdon, Patrick L.; Westover, M. Brandon

doi:10.1109/tbme.2018.2813265

Cited by 27 publications

(8 citation statements)

References 44 publications

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“…CC = clustering; APL = average path length; DOL = density of; NC = number of clusters; SE = spectral entropy; rDelta, rTheta, rAlpha and rBeta are the relative power of each respective band. [12]. In the present study, SVM analysis support the correlation results since the ND presented the best performance as compare with the other measures, with equivalent percentages to the model that includes all the measures.…”

Section: Discussionsupporting

confidence: 75%

Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach

et al. 2019

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Objective. Sedation of neurocritically ill patients is one of the most challenging situation in ICUs. Quantitative knowledge on the sedation effect on brain activity in that complex scenario could help to uncover new markers for sedation assessment. Hence, we aim to evaluate the existence of changes of diverse EEG-derived measures in deeply-sedated (RASS-Richmond agitation-sedation scale −4 and −5) neurocritically ill patients, and also whether sedation doses are related with those eventual changes. Approach. We performed an observational prospective cohort study in the intensive care unit of the Hospital de la Princesa. Twentysix adult patients suffered from traumatic brain injury and subarachnoid hemorrhage were included in the present study. Long-term continuous electroencephalographic (EEG) recordings (2141 h) and hourly annotated information were used to determine the relationship between intravenous sedation infusion doses and network and spectral EEG measures. To do that, two different strategies were followed: assessment of the statistical dependence between both variables using the Spearman correlation rank and by performing an automatic classification method based on a machine learning algorithm. Main results. More than 60% of patients presented a correlation greater than 0.5 in at least one of the calculated EEG measures with the sedation dose. The automatic classification method presented an accuracy of 84.3% in discriminating between different sedation doses. In both cases the nodes' degree was the most relevant measurement. Significance. The results presented here provide evidences of brain activity changes during deep sedation linked to sedation doses. Particularly, the capability of network EEG-derived measures in discriminating between different sedation doses could be the framework for the development of accurate methods for sedation levels assessment.

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Section: Discussionsupporting

confidence: 75%

Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach

et al. 2019

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“…This approach was used previously to predict the sedation levels in ICU patients. 33 We used this approach for two reasons: first, because MOAA/S scores are not continuous response assessments, but performed intermittently, there is a limited number of assessments in individual scores creating an imbalanced data set, and second, to minimise score 'annotation noise' attributable to inter-observer variability during the sedation-level assessment. Using a multi-class classification or a multinomial regression may not be efficient because of the annotation noise and limited data set in intermediate sedation states, which will again provide a discrete score.…”

Section: Continuous Sedation-level Assessmentmentioning

confidence: 99%

Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers

Ramaswamy

Kuizenga

Weerink

et al. 2019

British Journal of Anaesthesia

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Background: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine learning algorithm could reliably predict levels of sedation, independent of the sedative drug used. Methods: In total, 102 subjects receiving propofol (N¼36; 16 male/20 female), sevoflurane (N¼36; 16 male/20 female), or dexmedetomidine (N¼30; 15 male/15 female) were included in this study of healthy volunteers. Sedation level was assessed using the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) score. We used 44 QEEG features estimated from the EEG data in a logistic regression algorithm, and an elastic-net regularisation method was used for feature selection. The area under the receiver operator characteristic curve (AUC) was used to assess the performance of the logistic regression model. Results: The performances obtained when the system was trained and tested as drug-dependent mode to distinguish between awake and sedated states (mean AUC [standard deviation]) were propofol¼0.97 (0.03), sevoflurane¼0.74 (0.25), and dexmedetomidine¼0.77 (0.10). The drug-independent system resulted in mean AUC¼0.83 (0.17) to discriminate between the awake and sedated states. Conclusions: The incorporation of large numbers of QEEG features and machine learning algorithms is feasible for nextgeneration monitors of sedation level. Different QEEG features were selected for propofol, sevoflurane, and dexmedetomidine groups, but the sedation-level estimator maintained a high performance for predicting MOAA/S independent of the drug used. Clinical trial registration: NCT 02043938; NCT 03143972.

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“…The modest performance confirms the heterogeneity between cohorts, protocols and centers. Nagaraj et al 20 used atomic decomposition and a support vector machine classifier to classify RASS −5, −4 vs. −1, 0 based on 44 patients—a subset of our dataset. They achieved AUC at 0.91.…”

Section: Discussionmentioning

confidence: 99%

Automated tracking of level of consciousness and delirium in critical illness using deep learning

et al. 2019

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Over- and under-sedation are common in the ICU, and contribute to poor ICU outcomes including delirium. Behavioral assessments, such as Richmond Agitation-Sedation Scale (RASS) for monitoring levels of sedation and Confusion Assessment Method for the ICU (CAM-ICU) for detecting signs of delirium, are often used. As an alternative, brain monitoring with electroencephalography (EEG) has been proposed in the operating room, but is challenging to implement in ICU due to the differences between critical illness and elective surgery, as well as the duration of sedation. Here we present a deep learning model based on a combination of convolutional and recurrent neural networks that automatically tracks both the level of consciousness and delirium using frontal EEG signals in the ICU. For level of consciousness, the system achieves a median accuracy of 70% when allowing prediction to be within one RASS level difference across all patients, which is comparable or higher than the median technician–nurse agreement at 59%. For delirium, the system achieves an AUC of 0.80 with 69% sensitivity and 83% specificity at the optimal operating point. The results show it is feasible to continuously track level of consciousness and delirium in the ICU.

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Electroencephalogram Based Detection of Deep Sedation in ICU Patients Using Atomic Decomposition

Abstract: With further refinement and external validation, the proposed system may be able to assist clinical staff with continuous surveillance of sedation levels in mechanically ventilated critically ill ICU patients.

Cited by 27 publications

References 44 publications

Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach

Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach

Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers

Automated tracking of level of consciousness and delirium in critical illness using deep learning

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