Real-time segmentation of burst suppression patterns in critical care EEG monitoring

Abstract

Objective Develop a real-time algorithm to automatically discriminate suppressions from non-suppressions (bursts) in electroencephalograms of critically ill adult patients. Methods A real-time method for segmenting adult ICU EEG data into bursts and suppressions is presented based on thresholding local voltage variance. Results are validated against manual segmentations by two experienced human electroencephalographers. We compare inter-rater agreement between manual EEG segmentations by experts with inter-rater agreement between human vs automatic segmentations, and investigate the robustness of segmentation quality to variations in algorithm parameter settings. We further compare the results of using these segmentations as input for calculating the burst suppression probability (BSP), a continuous measure of depth-of-suppression. Results Automated segmentation was comparable to manual segmentation, i.e. algorithm-vs-human agreement was comparable to human-vs-human agreement, as judged by comparing raw EEG segmentations or the derived BSP signals. Results were robust to modest variations in algorithm parameter settings. Conclusions Our automated method satisfactorily segments burst suppression data across a wide range adult ICU EEG patterns. Performance is comparable to or exceeds that of manual segmentation by human electroencephalographers. Significance Automated segmentation of burst suppression EEG patterns is an essential component of quantitative brain activity monitoring in critically ill and anesthetized adults. The segmentations produced by our algorithm provide a basis for accurate tracking of suppression depth.

Monitoring burst suppression in critically ill patients: Multi-centric evaluation of a novel method

“…A detection method based on the line length feature using the EEG of 10 pre-term infants was presented in Koolen et al (2014). An automatic classification method for burst and suppression events was validated in (Westover et al, 2013) on 20 critical care EEG recordings that were selected based on clinical EEG reports. The detection algorithm was trained on these 20 EEGs and showed high agreement compared to human annotations.…”

The human burst suppression electroencephalogram of deep hypothermia

“…Segmentation of EEG recordings into burst and suppression periods was performed in a semi-automated manner, using an adaptation of previously described methods(Brandon Westover et al 2013; Lewis et al 2013). Analysis focused in each patient on the period of EEG recording starting immediately before induction of deep hypothermia (<34 °C) and ending after return to near normothermia (>34 °C).…”

Spatial variation in automated burst suppression detection in pharmacologically induced coma

“…Next, a previously validated algorithm for burst suppression segmentation is used to generate the binary signals representing suppressions [8]. This algorithm detects suppression by thresholding a recursive estimate of the local signal variance as expressed in the following equations: $${\mu }_t=\beta {\mu }_{t-1}+false(1-\beta false)x_t$$ $${\sigma }_t^2=\beta {\sigma }_{t-1}^2+false(1-\beta false){false(x_t-{\mu }_{t}false)}^2$$ $$z_t=\delta false[{\sigma }_t^2<\theta false]$$ where x t is the EEG signal at time t, μ t is the mean, σ t is the variance, z t is the current value of the binary signal produced, β is a parameter called the “forgetting factor”, δ[․] is the indicator function (equal to 1 if the inequality is satisfied and 0 otherwise) and θ is the classification threshold.…”

“…Many groups have discussed methods of automated burst suppression detection but no work to our knowledge has explicitly addressed how multi-channel EEG data affects burst suppression detection and monitoring [8]–[12]. Research effort has been directed at describing how suppression patterns can be successfully extracted automatically, usually by first identifying one or more features that distinguish bursts from suppressions (e.g.…”

“…None of these studies explicitly addressed how data from multiple channels are optimally combined. In work that did utilize EEG signals across multiple channels, the authors described integrating instantaneous amplitude across all channels, stating that a priori , this should help to overcome contamination of channels by artifacts, but no further systematic analysis of the impact on detector behavior was performed [8], [12]. …”