Adaptive segmentation of burst-suppression pattern in isoflurane and enflurane anesthesia

Lipping, Tarmo; Jäntti, V.; Yli‐Hankala, Arvi; Hartikainen, Kaisa M.

doi:10.1007/bf02332690

Cited by 11 publications

(10 citation statements)

References 10 publications

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“…At least eight previous publications have presented methods for automated segmentation of burst suppression EEG patterns, based on a variety of EEG features and classification methods, including the nonlinear energy operator, spectral feature clustering, and neural-network classification of adaptive Hilbert-transformed EEG features (Thomsen et al, 1991; Lipping et al, 1995; Arnold et al, 1996; Griessbach et al, 1997; Sherman et al, 1997; Leistritz et al, 1999; Atit et al, 1999). These prior efforts are summarized and improved on in Särkelä et al (2002), the method most closely related to our method.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, the results may not generalize to burst suppression EEG patterns of critically ill adults. Furthermore, validation of these methods against human expert clinical EEG segmentation has been limited (Thomsen et al, 1991; Lipping et al, 1995; Arnold et al, 1996; Griessbach et al, 1997; Sherman et al, 1997; Leistritz et al, 1999; Atit et al, 1999; Särkelä et al, 2002). …”

Section: Introductionmentioning

confidence: 99%

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

Westover

Shafi

Ching

et al. 2013

Journal of Neuroscience Methods

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Westover

Shafi

Ching

et al. 2013

Journal of Neuroscience Methods

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“…The detection algorithm was trained on these 20 EEGs and showed high agreement compared to human annotations. Numerous other methods exist in literature that use various mathematical features to detect BSP (Thomsen et al, 1991; Lipping et al, 1995; Bruhn et al, 2000, 2006; Jaggi et al, 2003; Liang et al, 2014) but include a limited number of patients.…”

Section: Introductionmentioning

confidence: 99%

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

Fürbass

Herta

Koren

et al. 2016

Clinical Neurophysiology

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Objective To develop a computational method to detect and quantify burst suppression patterns (BSP) in the EEGs of critical care patients. A multi-center validation study was performed to assess the detection performance of the method. Methods The fully automatic method scans the EEG for discontinuous patterns and shows detected BSP and quantitative information on a trending display in real-time. The method is designed to work without setting any patient specific parameters and to be insensitive to EEG artifacts and periodic patterns. For validation a total of 3982 h of EEG from 88 patients were analyzed from three centers. Each EEG was annotated by two reviewers to assess the detection performance and the inter-rater agreement. Results Average inter-rater agreement between pairs of reviewers was κ = 0.69. On average 22% of the review segments included BSP. An average sensitivity of 90% and a specificity of 84% were measured on the consensus annotations of two reviewers. More than 95% of the periodic patterns in the EEGs were correctly suppressed. Conclusion A fully automatic method to detect burst suppression patterns was assessed in a multi-center study. The method showed high sensitivity and specificity. Significance Clinically applicable burst suppression detection method validated in a large multi-center study.

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“…However, the duration of the suppression and burst epochs that were compared were only 2–6 s. A study in rabbits comparing 1 min each of EEG burst suppression during propofol and isoflurane anesthesia reported higher amplitude bursts during isoflurane anesthesia (Hartikainen et al, 1995a ). Another comparison between the burst suppression patterns of isoflurane and enflurane found that suppressions were shorter in duration for enflurane (Lipping et al, 1995 ). Burst suppression caused by hypoxic-ischemic encephalopathy has also been reported to have a higher variability in individual suppression durations compared to pentobarbital-induced burst suppression (Beydoun et al, 1991 ).…”

Section: Introductionmentioning

confidence: 99%

Propofol and sevoflurane induce distinct burst suppression patterns in rats

Kenny

Westover

Ching

et al. 2014

Front. Syst. Neurosci.

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Burst suppression is an EEG pattern characterized by alternating periods of high-amplitude activity (bursts) and relatively low amplitude activity (suppressions). Burst suppression can arise from several different pathological conditions, as well as from general anesthesia. Here we review current algorithms that are used to quantify burst suppression, its various etiologies, and possible underlying mechanisms. We then review clinical applications of anesthetic-induced burst suppression. Finally, we report the results of our new study showing clear electrophysiological differences in burst suppression patterns induced by two common general anesthetics, sevoflurane and propofol. Our data suggest that the circuit mechanisms that generate burst suppression activity may differ among general anesthetics.

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Adaptive segmentation of burst-suppression pattern in isoflurane and enflurane anesthesia

Cited by 11 publications

References 10 publications

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

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

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

Propofol and sevoflurane induce distinct burst suppression patterns in rats

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