Search Results for « Electroencephalography »

Notwithstanding recent advances in neuroimaging, EEG remains a major technique for investigation of the brain. Its main applications are in assessment of cerebral function rather than for detecting structural abnormalities. The principal clinical applications are in epilepsy, states of altered consciousness including postanoxic and traumatic coma, the parasomnias, dementias, toxic confusional states, cerebral infections, and various other encephalopathies. Abnormalities in EEG reflect general pathophysiological processes, raised intracranial pressure, cerebral anoxia, or oedema, epileptogenesis etc, and show little specificity for a particular disease. Consequently, they need to be interpreted in a particular clinical context; the use of routine EEG examination for screening purposes is rarely of value. Conversely, the investigation becomes most cost effective when applied to specific problems--for instance, monitoring serial changes in postanoxic coma or during open heart surgery, differential diagnosis (by telemetric ictal recordings) of epileptic and non-epileptic attacks, and providing early prediction of outcome after stroke. High technological standards and an individualised problem solving approach are prerequisites of a cost effective, reliable clinical EEG service. These are most likely to be achieved by a considered, selective referral policy, the use where necessary of prolonged complex procedures such as telemetry, and the avoidance of routine examinations of dubious clinical relevance.

AbstractAbstract The widely used electroencephalogram-based indices for depth-of-anesthesia monitoring assume that the same index value defines the same level of unconsciousness for all anesthetics. In contrast, we show that different anesthetics act at different molecular targets and neural circuits to produce distinct brain states that are readily visible in the electroencephalogram. We present a two-part review to educate anesthesiologists on use of the unprocessed electroencephalogram and its spectrogram to track the brain states of patients receiving anesthesia care. Here in part I, we review the biophysics of the electroencephalogram and the neurophysiology of the electroencephalogram signatures of three intravenous anesthetics: propofol, dexmedetomidine, and ketamine, and four inhaled anesthetics: sevoflurane, isoflurane, desflurane, and nitrous oxide. Later in part II, we discuss patient management using these electroencephalogram signatures. Use of these electroencephalogram signatures suggests a neurophysiologically based paradigm for brain state monitoring of patients receiving anesthesia care. The authors review the neurophysiology of the electroencephalogram signatures of the anesthetics: propofol, dexmedetomidine, ketamine, sevoflurane, isoflurane, desflurane, and nitrous oxide. These signatures provide a neurophysiologically based paradigm for brain state monitoring of patients receiving anesthesia care.

To assess centrally mediated analgesic mechanisms in clinical trials with pain patients, objective standardized methods such as electroencephalography (EEG) has many advantages. The aim of this review is to provide the reader with an overview of present findings in analgesics assessed with spontaneous EEG and evoked brain potentials (EPs) in humans. Furthermore, EEG methodologies will be discussed with respect to translation from animals to humans and future perspectives in predicting analgesic efficacy. We searched PubMed with MeSH terms 'analgesics', 'electroencephalography' and 'evoked potentials' for relevant articles. Combined with a search in their reference lists 15 articles on spontaneous EEG and 55 papers on EPs were identified. Overall, opioids produced increased activity in the delta band in the spontaneous EEG, but increases in higher frequency bands were also seen. The EP amplitudes decreased in the majority of studies. Anticonvulsants used as analgesics showed inconsistent results. The N-methyl-D-aspartate receptor antagonist ketamine showed an increase in the theta band in spontaneous EEG and decreases in EP amplitudes. Tricyclic antidepressants increased the activity in the delta, theta and beta bands in the spontaneous EEG while EPs were inconsistently affected. Weak analgesics were mainly investigated with EPs and a decrease in amplitudes was generally observed. This review reveals that both spontaneous EEG and EPs are widely used as biomarkers for analgesic drug effects. Methodological differences are common and a more uniform approach will further enhance the value of such biomarkers for drug development and prediction of treatment response in individual patients.

Some practical and theoretical aspects of the use of electroencephalography in criminology are reviewed. Examples are given of various factors in the EEG pattern and in certain activation techniques showing correlation with certain attributes of personality structure. Briefly presented are EEG studies of psychopathic populations and those of convicted criminals, including murderers. The relatively high incidence, among murderers, of clinical epileptics and persons with EEG's showing epileptic implications, is discussed, as well as the significance of the 14 and 6 cycles per second positive spike complex, which is still a controversial issue. In spite of widely divergent findings, it is generally agreed that there is a relatively high incidence of abnormal EEG's among criminal and delinquent populations, though without a specific and consistent EEG pattern characterizing these groups; that the most often seen abnormalities are the presence of excessive theta activity, focal temporal lobe pathology, and instability of the patterns, often epileptic in nature; that the age factor needs special consideration in the EEG appraisal of criminal or delinquent populations, and that aggressive behaviour, continuous or episodic, appears to be the common denominator in the personality structure of these populations.

CONTEXT: Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. OBJECTIVE: In this work, we review 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations. METHODS: Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to (1) the data, (2) the preprocessing methodology, (3) the DL design choices, (4) the results, and (5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. RESULTS: Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours, while the number of samples seen during training by a network varies from a few dozens to several millions, depending on how epochs are extracted. Interestingly, we saw that more than half the studies used publicly available data and that there has also been a clear shift from intra-subject to inter-subject approaches over the last few years. About [Formula: see text] of the studies used convolutional neural networks (CNNs), while [Formula: see text] used recurrent neural networks (RNNs), most often with a total of 3-10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was [Formula: see text] across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. SIGNIFICANCE: To help the community progress and share work more effectively, we provide a list of recommendations for future studies and emphasize the need for more reproducible research. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly. A planned follow-up to this work will be an online public benchmarking portal listing reproducible results.