Assessment of anaesthetic depth by clustering analysis and autoregressive modelling of electroencephalograms

Thomsen, Carsten; Rosenfalck, A.; Christensen, Kristoffer

doi:10.1016/0169-2607(91)90038-u

Cited by 18 publications

(17 citation statements)

References 12 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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“…Many features of brain electrophysiology have been investigated and reported to correlate with different conscious processes or even the level of consciousness. Spectral power differences have been commonly found at characteristic frequency bands; notably, lack of consciousness has been associated with increased power at low frequencies (delta waves: <4 Hz) in multiple contexts, including sleep stage depth, dream recall within a sleep stage, and anaesthetic depth (Chellappa et al, 2011;Evans, 2003;Hobson & Pace-Schott, 2002;Murphy et al, 2011;Scarpelli et al, 2017;Siclari et al, 2017Siclari et al, , 2018Thomsen, Rosenfalck, & Nørregaard Christensen, 1991). Higher levels of consciousness (or arousal) have also been suggested to correlate with a lower spectral exponent (Colombo et al, 2019), higher signal entropy or complexity (Bein, 2006;Bruhn, Röpcke, & Hoeft, 2000;D'Andola et al, 2017;Hudetz, Liu, Pillay, Boly, & Tononi, 2016;King et al, 2013;Liang et al, 2013;Ouyang, Li, Liu, & Li, 2013;Sarasso et al, 2015;Schartner et al, 2015), stronger phase coherence between brain areas (Bola et al, 2017;Lee et al, 2017;Mikulan et al, 2017), and more causally integrated brain areas (Barrett et al, 2012;D'Andola et al, 2017;Fasoula, Attal, & Schwartz, 2013).…”

Section: Measures Of Consciousnessmentioning

confidence: 99%

“…First, the Analysis Team considered eight different methods of analysis based on the previous EEG literature on levels of consciousness (e.g., sleep, anaesthesia, brain injury), listed with detailed methods in Supplementary Document 5. These were spectral power at established frequency bands, spectral power at fine frequency resolution, autocorrelation features as described by Thomsen et al (1991), permutation entropy, approximate entropy, EOG root mean square (RMS) activity, EMG RMS activity, and spectral power in temporo-occipito-parietal areas (Siclari et al, 2014). As more features would be expected to result in overfitting (Domingos, 2012), the Analysis Team aimed to select only a few features fit for purpose.…”

Section: Forced 2 Clustersmentioning

confidence: 99%

The Dream Catcher experiment: Blinded analyses disconfirm markers of dreaming consciousness in EEG spectral power

Wong

Noreika

Móró

et al. 2019

Preprint

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The Dream Catcher test defines the criteria for a genuine discovery of the neural constituents of phenomenal consciousness. Passing the test implies that some patterns of purely brain-based data directly correspond to the subjective features of phenomenal experience, which would help to bridge the explanatory gap between consciousness and brain. Here, we conducted the Dream Catcher test for the first time in a graded and simplified form, capturing its core idea. The experiment involved a Data Team, who measured participants’ brain activity during sleep and collected dream reports, and a blinded Analysis Team, who was challenged to predict better than chance, based solely on brain measurements, whether or not a participant had a dream experience. Using a serial-awakening paradigm, the Data Team prepared 54 one-minute polysomnograms of NREM sleep—27 of dreamful sleep (3 from each of the 9 participants) and 27 of dreamless sleep—redacting from them all associated participant and dream information. The Analysis Team attempted to classify each recording as either dreamless or dreamful using an unsupervised machine learning classifier, based on hypothesis-driven, extracted features of EEG spectral power and electrode location. The procedure was repeated over five iterations with a gradual removal of blindness. At no level of blindness did the Analysis Team perform significantly better than chance, suggesting that EEG spectral power does not carry any signatures of phenomenal consciousness. Furthermore, we demonstrate an outright failure to replicate key findings of recently reported correlates of dreaming consciousness.HighlightsThe first reported attempt of the Dream Catcher test.The correlates of conscious experience may not lie in EEG spectral power.Reported markers of NREM dreaming consciousness misperformed in a blinded setting.Those markers also could not be confirmed in an unblinded setting.

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Assessment of anaesthetic depth by clustering analysis and autoregressive modelling of electroencephalograms

Cited by 18 publications

References 12 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

The Dream Catcher experiment: Blinded analyses disconfirm markers of dreaming consciousness in EEG spectral power

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