Linear and nonlinear EEG indexes in relation to the severity of coma

Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage

"Coma" is defined as an inability to obey commands, to speak, or to open the eyes. So, a coma is a state of unarousable unconsciousness. In a clinical setting, the ability to respond to a command is often used to infer consciousness. Evaluation of the patient's level of consciousness (LeOC) is important for neurological evaluation. The Glasgow Coma Scale (GCS) is the most widely used and popular scoring system for neurological evaluation and is used to assess a patient's level of consciousness. The aim of this study is the evaluation of GCSs with an objective approach based on numerical results. So, EEG signals were recorded from 39 patients in a coma state with a new procedure proposed by us in a deep coma state (GCS: between 3 and 8). The EEG signals were divided into four sub-bands as alpha, beta, delta, and theta, and their power spectral density was calculated. As a result of power spectral analysis, 10 different features were extracted from EEG signals in the time and frequency domains. The features were statistically analyzed to differentiate the different LeOC and to relate with the GCS. Additionally, some machine learning algorithms have been used to measure the performance of the features for distinguishing patients with different GCSs in a deep coma. This study demonstrated that GCS 3 and GCS 8 patients were classified from other levels of consciousness in terms of decreased theta activity. To the best of our knowledge, this is the first study to classify patients in a deep coma (GCS between 3 and 8) with 96.44% classification performance.

State-Dependent and Bandwidth-Specific Effects of Ketamine and Propofol on Electroencephalographic Complexity in Rats

There is an ongoing debate as to whether ketamine anesthesia suppresses neurophysiologic complexity at doses sufficient for surgical anesthesia, with previous human studies reporting surrogates of both suppressed and preserved levels of cortical complexity. However, these studies have not assessed cortical dynamics in higher gamma frequencies, which have previously been demonstrated to correlate with the level of consciousness during anesthesia. In this study, we used Lempel-Ziv complexity (LZc) to characterize frontal and parietal electroencephalographic complexity (0.5–175 Hz, 0.5–55 Hz, 65–175 Hz) before, during, and after ketamine or propofol anesthesia in the rat. To control for the potential influence of spectral changes in complexity estimation, LZc was normalized with phase-shuffled surrogate data. We demonstrate that ketamine and propofol anesthesia were characterized by a significant reduction in broadband (0.5–175 Hz) LZc. Further analysis showed that while the reduction of LZc during ketamine anesthesia was significant in 65–175 Hz range, during propofol anesthesia, a significant decrease was observed in 0.5–55 Hz bandwidth. LZc in broadband and 0.5–55 Hz range showed a significant increase during emergence from ketamine anesthesia. Phase-shuffled normalized LZc revealed that (1) decrease in complexity during ketamine and propofol anesthesia—not increase in complexity during emergence—were dissociable from the influence of spectral changes, and (2) reduced LZc during ketamine anesthesia was present across all three bandwidths. Ketamine anesthesia was characterized by reduced complexity in high gamma bandwidth, as reflected in both raw and phase-shuffled normalized LZc, which suggests that reduced high gamma complexity is a neurophysiological feature of ketamine anesthesia.

Early Consciousness Disorder in Acute Large Hemispheric Infarction: An Analysis Based on Quantitative EEG and Brain Network Characteristics

BACKGROUND

Large hemispheric infarction (LHI) is an ischemic stroke affecting at least two-thirds of the middle cerebral artery territory, with or without involvement of the anterior cerebral artery or posterior cerebral artery, and approximately 77% of LHI patients have early consciousness disorder (ECD). We constructed a functional brain network for LHI patients with an acute consciousness disorder to identify new diagnostic markers related to ECDs by analyzing brain network characteristics and mechanisms.

METHODS

Between August 1, 2017, and September 30, 2018, patients with acute (< 1 month) LHI were admitted to the neurocritical care unit at Xuanwu Hospital of Capital Medical University. Electroencephalography (EEG) data were recorded, and the MATLAB platform (2017b) was used to calculate spectral power, entropy, coherence and phase synchronization. The quantitative EEG and brain network characteristics of different consciousness states and different frequency bands were analyzed (α = 0.05). EEG data were recorded 38 times in 30 patients, 25 of whom were in the ECD group, while 13 patients were in the conscious group.

RESULTS

(1) Spectral power analysis: The conscious group had higher beta relative spectral power across the whole brain, higher alpha spectral power in the frontal-parietal lobe on the infarction contralateral side, and lower theta and delta spectral power in the central-occipital lobe on the infarction contralateral side than the ECD group. (2) Entropy analysis: The conscious group had higher approximate entropy (ApEn) and permutation entropy (PeEn) across the whole brain than the ECD group. (3) Coherence: The conscious group had higher alpha coherence in nearly the whole brain and higher beta coherence in the bilateral frontal-parietal and parietal-occipital lobes than the ECD group. (4) Phase synchronization: The conscious group had higher alpha and beta synchronization in nearly the whole brain, particularly in the frontal-parietal and parietal-occipital lobes, than the ECD group. (5) Graph theory: The conscious group had higher small-worldness in each frequency band than the ECD group.

CONCLUSION

In patients with LHI, higher levels of consciousness were associated with more alpha and beta oscillations and fewer delta and theta oscillations. Higher ApEn, PeEn, total brain connectivity, and small-worldness and a wider signal distribution range corresponded to a higher consciousness level.

Electrophysiologic monitoring in acute brain injury

To determine the optimal use and indications of electroencephalography (EEG) in critical care management of acute brain injury (ABI). An electronic literature search was conducted for articles in English describing electrophysiological monitoring in ABI from January 1990 to August 2013. A total of 165 studies were included. EEG is a useful monitor for seizure and ischemia detection. There is a well-described role for EEG in convulsive status epilepticus and cardiac arrest (CA). Data suggest EEG should be considered in all patients with ABI and unexplained and persistent altered consciousness and in comatose intensive care unit (ICU) patients without an acute primary brain condition who have an unexplained impairment of mental status. There remain uncertainties about certain technical details, e.g., the minimum duration of EEG studies, the montage, and electrodes. Data obtained from both EEG and EP studies may help estimate prognosis in ABI patients, particularly following CA and traumatic brain injury. Data supporting these recommendations is sparse, and high quality studies are needed. EEG is used to monitor and detect seizures and ischemia in ICU patients and indications for EEG are clear for certain disease states, however, uncertainty remains on other applications.

An empirical EEG analysis in brain death diagnosis for adults

Electroencephalogram (EEG) is often used in the confirmatory test for brain death diagnosis in clinical practice. Because EEG recording and monitoring is relatively safe for the patients in deep coma, it is believed to be valuable for either reducing the risk of brain death diagnosis (while comparing other tests such as the apnea) or preventing mistaken diagnosis. The objective of this paper is to study several statistical methods for quantitative EEG analysis in order to help bedside or ambulatory monitoring or diagnosis. We apply signal processing and quantitative statistical analysis for the EEG recordings of 32 adult patients. For EEG signal processing, independent component analysis (ICA) was applied to separate the independent source components, followed by Fourier and time-frequency analysis. For quantitative EEG analysis, we apply several statistical complexity measures to the EEG signals and evaluate the differences between two groups of patients: the subjects in deep coma, and the subjects who were categorized as brain death. We report statistically significant differences of quantitative statistics with real-life EEG recordings in such a clinical study, and we also present interpretation and discussions on the preliminary experimental results.