Quantification of EEG reactivity in comatose patients

OBJECTIVE: EEG reactivity is an important predictor of outcome in comatose patients. However, visual analysis of reactivity is prone to subjectivity and may benefit from quantitative approaches. METHODS: In EEG segments recorded during reactivity testing in 59 comatose patients, 13 quantitative EEG parameters were used to compare the spectral characteristics of 1-minute segments before and after the onset of stimulation (spectral temporal symmetry). Reactivity was quantified with probability values estimated using combinations of these parameters. The accuracy of probability values as a reactivity classifier was evaluated against the consensus assessment of three expert clinical electroencephalographers using visual analysis. RESULTS: The binary classifier assessing spectral temporal symmetry in four frequency bands (delta, theta, alpha and beta) showed best accuracy (Median AUC: 0.95) and was accompanied by substantial agreement with the individual opinion of experts (Gwet’s AC1: 65–70%), at least as good as inter-expert agreement (AC1: 55%). Probability values also reflected the degree of reactivity, as measured by the inter-experts’ agreement regarding reactivity for each individual case. CONCLUSION: Automated quantitative EEG approaches based on probabilistic description of spectral temporal symmetry reliably quantify EEG reactivity. SIGNIFICANCE: Quantitative EEG may be useful for evaluating reactivity in comatose patients, offering increased objectivity

Cognition, emotional state, and quality of life of survivors after cardiac arrest with rhythmic and periodic EEG patterns

Aim: Rhythmic and periodic patterns (RPPs) on the electroencephalogram (EEG) in comatose patients after cardiac arrest have been associated with high case fatality rates. A good neurological outcome according to the Cerebral Performance Categories (CPC) has been reported in up to 10% of cases. Data on cognitive, emotional, and quality of life outcomes are lacking. We aimed to provide insight into these outcomes at one-year follow-up. Methods: We assessed outcome of surviving comatose patients after cardiac arrest with RPPs included in the ‘treatment of electroencephalographic status epilepticus after cardiopulmonary resuscitation’ (TELSTAR) trial at one-year follow-up, including the CPC for functional neurological outcome, a cognitive assessment, the hospital anxiety and depression scale (HADS) for emotional outcomes, and the 36-item short-form health survey (SF-36) for quality of life. Cognitive impairment was defined as a score of more than 1.5 SD below the mean on = 2 (sub)tests within a cognitive domain. Results: Fourteen patients were included (median age 58 years, 21% female), of whom 13 had a cognitive impairment. Eleven of 14 were impaired in memory, 9/14 in executive functioning, and 7/14 in attention. The median scores on the HADS and SF-36 were all worse than expected. Based on the CPC alone, 8/14 had a good outcome (CPC 1–2). Conclusion: Nearly all cardiac arrest survivors with RPPs during the comatose state have cognitive impairments at one-year follow-up. The incidence of anxiety and depression symptoms seem relatively high and quality of life relatively poor, despite ‘good’ outcomes according to the CPC

Application of a neural complexity measure to multichannel EEG

We apply a recently suggested measure for neural complexity (G. Tononi, O. Sporns, G.M. Edelman, Proc. Natl. Acad. Sci. 91 (1994) 5033), that is hypothesised to capture the interplay between two fundamental aspects of brain organisation, functional segregation and integration, to human EEG recordings. This measure is based on a weighted sum of entropy differences evaluated at different length scales of the system. A strong prediction is that this measure correlates with the conscious state of the subject, having lower values if consciousness is reduced (G. Tononi, G.M. Edelman, Science 282 (1998) 1846). It is found, however, that this neural complexity measure increases in neurological disorders where consciousness is severely reduced or absent. We discuss several possible explanations for this observation and suggest directions for future work

Reduction of TMS induced artefacts in EEG using principal component analysis

Co-registration of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is a new, promising method for assessing cortical excitability and connectivity. Using this technique, a TMS evoked potential (TEP) can be induced and registered with the EEG. However, the TEP contains an early, short lasting artifact due to the magnetic pulse, and a second artifact, which depends on the location of stimulation and can last up to 40 ms. Different causes for this second artifact have been suggested in literature. In this study, we used principal component analysis (PCA) to suppress both the first and second artifact in TMS-EEG data. Single pulse TMS was applied at the motor and visual cortex in 18 healthy subjects. PCA using singular value decomposition was applied on single trials to suppress the artifactual components. A large artifact suppression was realized after the removal of the first five PCA components, thereby revealing early TEP peaks, with only a small suppression of later TEP components. The spatial distribution of the second artifact suggests that it is caused by electrode movement due to activation of the temporal musculature. In conclusion, we showed that PCA can be used to reduce TMS-induced artifacts in EEG, thereby revealing components of the TMS evoked potential. © 2001-2011 IEEE