Relevant bibliographies by topics / Anaesthesia; EEG / Journal articles
To see the other types of publications on this topic, follow the link: Anaesthesia; EEG.

Journal articles on the topic 'Anaesthesia; EEG'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Anaesthesia; EEG.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Pomfrett, C. J. D. "The EEG during anaesthesia." Current Anaesthesia & Critical Care 9, no. 3 (June 1998): 117–22. http://dx.doi.org/10.1016/s0953-7112(98)80004-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Harris, Charissa, Peter John White, Virginia L. Mohler, and Sabrina Lomax. "Electroencephalography Can Distinguish between Pain and Anaesthetic Intervention in Conscious Lambs Undergoing Castration." Animals 10, no. 3 (March 4, 2020): 428. http://dx.doi.org/10.3390/ani10030428.

Full text
Abstract:
Australian sheep routinely undergo painful surgical husbandry procedures without anaesthesia or analgesia. Electroencephalography (EEG) has been shown to be a successful measure of pain in livestock under a general anaesthetic. The aim of this study was to compare this EEG model to that of conscious lambs undergoing castration with and without local anaesthesia. Sixteen merino crossbred ram lambs 6 to 8 weeks of age (13.81kg ± 1.97) were used in the study. Lambs were randomly allocated to 1 of 4 treatment groups: (1) Conscious EEG and surgical castration with no anaesthetic intervention (CON; n = 4); (2) Conscious EEG and surgical castration with pre-operative applied intra-testicular lignocaine injection (CON + LIG; n = 4); (3) surgical castration under minimal anaesthesia (MAM; n = 4); (4) and surgical castration with pre-operative lignocaine injection (2 mL lignocaine hydrochloride 20 mg/mL, under minimal anaesthesia (MAM + LIG; n = 4). Distinct differences in the EEG parameters Ptot, F50 and F95 between pre-and post-castration in conscious lambs were demonstrated in this study (p < 0.01). Further, CON and CON + LIG treatments were distinguishable using F50 and F95 measures (p = 0.02, p = 0.04, respectively). Significant changes in the EEG output of MAM animals were identified pre- to post-castration (p < 0.01). The EEG output of MAM and MAM + LIG were similar. EEG was successful in differentiating lambs treated with pain relief in a conscious state after castration by examining F50 and F95, which may suggest the suitability of conscious EEG pain measurement.
APA, Harvard, Vancouver, ISO, and other styles
3

Johnson, B. W., J. W. Sleigh, I. J. Kirk, and M. L. Williams. "High-density EEG Mapping during General Anaesthesia with Xenon and Propofol: A Pilot Study." Anaesthesia and Intensive Care 31, no. 2 (April 2003): 155–63. http://dx.doi.org/10.1177/0310057x0303100203.

Full text
Abstract:
Anaesthetic-induced spatial inhomogeneities of the electrencephalogram(EEG) using “high density” electrode mapping have not previously been reported. We measured the scalp EEG with a dense electrode (128-channel) montage during the course of light general anaesthesia with xenon and then propofol in normal human subjects. EEG was measured during induction and recovery of general anaesthesia in five normal subjects, and we obtained analysable data from three of these subjects. EEG topographies were plotted on a realistic head surface. Scalp fields were spatially de-blurred using a realistic head model and projected onto an averaged cortical surface Both xenon and propofol elicited large increases in midline frontal theta-band EEG power. Propofol reliably elicited orbitofrontal delta activity. Xenon, but not propofol, caused large increases in delta over the posterior cortex. Increased gamma power was observed for both anaesthetic agents at midline electrodes over the posterior cortex, but not anteriorly. Anaesthesia-induced delta and theta waves were differentially distributed along the anterior-posterior axis of the brain in a manner that corresponds well to the anatomy of putative neuronal generators. The distribution of anaesthetic-induced changes in fast gamma-band power seems to reflect functional differences between the posterior and anterior aspects of the cerebral cortex. These preliminary observations were consistent within our small sample, indicating that larger studies of anaesthetic effects using high-density recordings are warranted.
APA, Harvard, Vancouver, ISO, and other styles
4

Simons, A. J. R., E. H. J. F. Boezeman, and R. A. F. Pronk. "Automatic EEG monitoring of anaesthesia." Baillière's Clinical Anaesthesiology 3, no. 3 (December 1989): 623–46. http://dx.doi.org/10.1016/s0950-3501(89)80022-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Grasso, Chiara, Vanessa Marchesini, and Nicola Disma. "Applications and Limitations of Neuro-Monitoring in Paediatric Anaesthesia and Intravenous Anaesthesia: A Narrative Review." Journal of Clinical Medicine 10, no. 12 (June 15, 2021): 2639. http://dx.doi.org/10.3390/jcm10122639.

Full text
Abstract:
Safe management of anaesthesia in children has been one of the top areas of research over the last decade. After the large volume of articles which focused on the putative neurotoxic effect of anaesthetic agents on the developing brain, the attention and research efforts shifted toward prevention and treatment of critical events and the importance of peri-anaesthetic haemodynamic stability to prevent negative neurological outcomes. Safetots.org is an international initiative aiming at raising the attention on the relevance of a high-quality anaesthesia in children undergoing surgical and non-surgical procedures to guarantee a favourable outcome. Children might experience hemodynamic instability for many reasons, and how the range of normality within brain autoregulation is maintained is still unknown. Neuro-monitoring can guide anaesthesia providers in delivering optimal anaesthetic drugs dosages and also correcting underling conditions that can negatively affect the neurological outcome. In particular, it is referred to EEG-based monitoring and monitoring for brain oxygenation.
APA, Harvard, Vancouver, ISO, and other styles
6

Whyte, Simon David, and Peter Driscoll Booker. "Monitoring depth of anaesthesia by EEG." BJA CEPD Reviews 3, no. 4 (August 2003): 106–10. http://dx.doi.org/10.1093/bjacepd/mkg106.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Mori, K. "The EEG and awareness during anaesthesia." Anaesthesia 42, no. 11 (November 1987): 1153–55. http://dx.doi.org/10.1111/j.1365-2044.1987.tb05219.x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Gaskell, A., R. D. Sanders, and J. Sleigh. "Using EEG markers to titrate anaesthesia." British Journal of Anaesthesia 121, no. 1 (July 2018): 327–29. http://dx.doi.org/10.1016/j.bja.2018.04.003.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Berkel, B., Z. Alanoglu, Y. Ates, O. SelviCan, and F. Tuzuner. "Quantative EEG monitored anaesthesia; cost comparison of three anaesthetic techniques management." European Journal of Anaesthesiology 24, Supplement 39 (June 2007): 17–18. http://dx.doi.org/10.1097/00003643-200706001-00064.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Gupta, Nidhi, and Gyaninder Singh. "Electroencephalography-based monitors." Journal of Neuroanaesthesiology and Critical Care 02, no. 03 (December 2015): 168–78. http://dx.doi.org/10.4103/2348-0548.165030.

Full text
Abstract:
AbstractAn electroencephalogram (EEG), detects changes and abnormalities in the electrical activity of the brain and thus provides a way to dynamically assess brain function. EEG may be used to diagnose and manage a number of clinical conditions such as epilepsy, convulsive and non-convulsive status epilepticus, encephalitis, barbiturate coma, brain death, etc., EEG provides a large amount of information to the anaesthesiologist for routine clinical practice as depth of anaesthesia monitors and detection of sub-clinical seizures; and also for understanding the complex mechanisms of anaesthesia-induced alteration of consciousness. In the initial years, the routine clinical applicability of EEG was hindered by the complexity of the raw EEG signal. However, with technological advancement, several EEG-derived dimensionless indices have been developed that correlate with the depth of the hypnotic component of anaesthesia and are easy to interpret. Similarly, with the development of quantitative EEG tools, the routine use of continuous EEG is ever expanding in the Intensive Care Units. This review, describe various commonly used EEG-based monitors and their clinical applicability in the field of anaesthesia and critical care.
APA, Harvard, Vancouver, ISO, and other styles
11

Bithal, Parmod. "Anaesthetic considerations for evoked potentials monitoring." Journal of Neuroanaesthesiology and Critical Care 01, no. 01 (April 2014): 002–12. http://dx.doi.org/10.4103/2348-0548.124832.

Full text
Abstract:
AbstractIntra-operative neurophysiologic monitoring (IONM) under anaesthesia has achieved popularity because it helps prevent/ minimize neurologic morbidity from surgical manipulations of various neurologic structures. Neurologic functions in an anaesthetised patient can be monitored either by electroencephalography (EEG) or by evoked potentials. Whereas, EEG is difficult to analyse, evoked potentials, in contrast, are easy to interpret, they are either present or absent, delayed or not delayed, with normal or abnormal wave. The goal of IONM is to identify changes in nervous system function prior to irreversible damage. Many factors need consideration when selecting an anaesthetic regimen for intra-operative monitoring of evoked potentials. The very pathophysiological condition or the potential risks of the contemplated surgical procedure, which require evoked potentials monitoring, may place constraints on anaesthetic management as well. With the availability of numerous anaesthetic techniques, an appropriate plan for managing both anaesthesia and IONM in a patient should be organised. It is extremely essential not to alter the pharmacological state of the patient to avoid any changes in the recording of evoked responses. While an anaesthesiologist may alter plans for a patient in order to facilitate IONM, monitoring team too, sometimes may be required to modify plans for monitoring when a particular anaesthetic agent or technique is strongly indicated or contraindicated. At times, compromise may be required between an anaesthesia technique and a monitoring technique. To serve patients’ best interest, it is critical to have a team approach and good communication among the neurophysiologist, anaesthesiologist and surgeon.
APA, Harvard, Vancouver, ISO, and other styles
12

Hartikainen, K., M. Rorarius, and V. Jäntti. "Reactivity of EEG burst suppression during anaesthesia." International Journal of Psychophysiology 25, no. 1 (January 1997): 28. http://dx.doi.org/10.1016/s0167-8760(97)85407-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
13

NAHM, G. STOCKMANNS, J. PETERSEN, H, W. "Concept for an intelligent anaesthesia EEG monitor." Medical Informatics and the Internet in Medicine 24, no. 1 (January 1999): 1–9. http://dx.doi.org/10.1080/146392399298492.

Full text
APA, Harvard, Vancouver, ISO, and other styles
14

Wen, Peng. "Consciousness, EEG and depth of anaesthesia monitoring." Australasian Physical & Engineering Sciences in Medicine 35, no. 4 (December 2012): 389–92. http://dx.doi.org/10.1007/s13246-012-0176-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
15

Lehmann, Heidi S., Ngaio J. Beausoleil, Kavitha Kongara, Preet M. Singh, Gabrielle C. Musk, and Craig B. Johnson. "The Effect of Different Concentrations of Halothane Anaesthesia on the Electroencephalograph of Rock Doves (Columba livia)." Birds 2, no. 2 (June 8, 2021): 207–16. http://dx.doi.org/10.3390/birds2020015.

Full text
Abstract:
Anaesthetic agents and doses used can significantly impact cerebrocortical responsiveness as assessed by electroencephalography (EEG). The objective of this study was to evaluate the effect of three different halothane concentrations on the EEG of Rock Doves using measures of frequency distribution and burst suppression. Eight healthy Rock Doves (Columba livia) were anaesthetized with halothane in oxygen, their tracheas intubated and their lungs mechanically ventilated. Five minutes of EEG were recorded at three multiples of minimum anaesthetic concentration (MAC), 1× MAC (1.6%), 1.5× MAC (2.4%) and 2× MAC (3.2%), presented in ascending then descending order. Fast Fourier transformation of the raw EEG record gave the median frequency (F50), spectral edge frequency (F95) and the total power (Ptot). Burst suppression, expressed as inactive compared to active EEG (%), was calculated on a representative two-minute section of the raw EEG. Data were analysed using repeated-measures one-way ANOVA with Tukey post hoc correction for comparison of 1×, 1.5× and 2× MAC. Three of eight birds demonstrated negligible (<1%) burst suppression. No effect of halothane concentration on burst suppression incidence was seen. A significant decrease in all measured frequency variables (F50, p = 0.04; F95p = 0.02; Ptotp < 0.0001) occurred between 1× and 2× MAC. Halothane anaesthesia at MAC multiples of 1×, 1.5× and 2× in the Rock Dove can be considered suitable where cortical responsiveness is desired.
APA, Harvard, Vancouver, ISO, and other styles
16

Hensel, M., S. Wolter, and W. J. Kox. "EEG controlled rapid opioid withdrawal under general anaesthesia." British Journal of Anaesthesia 84, no. 2 (February 2000): 236–38. http://dx.doi.org/10.1093/oxfordjournals.bja.a013408.

Full text
APA, Harvard, Vancouver, ISO, and other styles
17

Yli-Hankala, Arvi, Ville Jäntti, Ilmari Pyykkö, and Leena Lindgren. "Vibration stimulus induced EEG bursts in isoflurane anaesthesia." Electroencephalography and Clinical Neurophysiology 87, no. 4 (October 1993): 215–20. http://dx.doi.org/10.1016/0013-4694(93)90021-m.

Full text
APA, Harvard, Vancouver, ISO, and other styles
18

Bankman, I., and I. Gath. "Feature extraction and clustering of EEG during anaesthesia." Medical & Biological Engineering & Computing 25, no. 4 (July 1987): 474–77. http://dx.doi.org/10.1007/bf02443373.

Full text
APA, Harvard, Vancouver, ISO, and other styles
19

Filligoi, G. C., B. Makovec, M. Gagliardi, S. W. Henneberg, P. Lindholm, S. Cerutti, L. Capitanio, and E. W. Jensen. "On-line Analysis of AEP and EEG for Monitoring Depth of Anaesthesia." Methods of Information in Medicine 36, no. 04/05 (October 1997): 311–14. http://dx.doi.org/10.1055/s-0038-1636873.

Full text
Abstract:
Abstract:Achieving and monitoring adequate depth of anaesthesia is a challenge to the anaesthetist. With the introduction of muscle relaxing agents, the traditional signs of awareness are often obscured or difficult to interpret. These signs include blood pressure, heart rate, pupil size, etc. However, these factors do not describe the depth of anaesthesia, (DA), in a cerebral activity sense, hence there is a desire to achieve a better measure of the DA. Auditory Evoked Potentials (AEP) provide two aspects relevant to - anaesthesia: (1) they have identifiable anatomical significance and, (2) their characteristics reflect the way in which the brain reacts to a stimulus. However, AEP is embedded in noise from the ongoing EEG background activity. Hence, processing is needed to improve the signal to noise ratio. The methods applied were moving time averaging (MTA) and ARX-modeling. The EEG was collected from the left hemisphere and analysed by FFT to 1 sec epochs and the spectral edge frequency was calculated. Both the changes in ARX extracted AEP and the spectral edge frequency of the EEG correlated well with the time interval between propofol induction and onset of anaesthesia measured by clinical signs (i.e., cessation of eye-lash reflex). The MTA extracted AEP was significantly slower in tracing the transition from consciousness to unconsciousness.
APA, Harvard, Vancouver, ISO, and other styles
20

MÄKINEN, SAKU, KAISA HARTIKAINEN, JARL-THURE ERIKSSON, and VILLE JÄNTTI. "SPONTANEOUS AND EVOKED CORTICAL DYNAMICS DURING DEEP ANAESTHESIA." International Journal of Neural Systems 07, no. 04 (September 1996): 481–87. http://dx.doi.org/10.1142/s0129065796000464.

Full text
Abstract:
In this paper we have studied cortical dynamics as assessed using graphical methods during deep anaesthesia. Graphical analysis was carried out by autocorrelation functions and return maps with different lags. During moderate and deep anaesthesia, the electroencephalogram (EEG) shows a burst suppression pattern, consisting of abruptly-occurring high amplitude bursts alternating with periods of relative silence. Deep anaesthesia with burst suppression pattern provides a simple model of brain activity when the noise that is usually present in a subject who is awake is suppressed. During anaesthesia-induced EEG suppression, the brain reacts to different external stimuli with bursts. In respect to sensory processing during anaesthesia, it is interesting to know whether these bursts have different dynamics depending on the stimuli used. We have used graphical analysis to reveal the possible differences in bursts evoked by different stimuli. Externally evoked bursts were induced by auditory, electric and visual stimuli. The EEG studied in this paper consists of 25 bursts from one subject. We have estimated the autocorrelation function for each burst and used the formation gained from such autocorrelation coefficients as the grounds for determining different lags for return maps. The graphical methods used revealed differences in dynamics and topology of bursts as evoked by different stimuli. Spontaneous bursts clearly had different dynamics from evoked burst; which could not be seen directly from the raw EEG data. This study suggests that graphical analysis is a useful tool to obtain information about the dynamics of neuronal processes behind cortical responses during deep anaesthesia.
APA, Harvard, Vancouver, ISO, and other styles
21

McMeniman, W. J., and G. J. Purcell. "Neurological Monitoring during Anaesthesia and Surgery." Anaesthesia and Intensive Care 16, no. 3 (August 1988): 358–67. http://dx.doi.org/10.1177/0310057x8801600319.

Full text
Abstract:
The ability to monitor the electrical activity of the central nervous system and to record responses to stimulation allows for a more immediate assessment of the functional integrity of the nervous system during anaesthesia than do conventional techniques. These monitoring methods, however, have been slow to find acceptance in clinical practice. The reasons include the difficulty with standardization and reproducibility of results from such monitoring techniques as the electroencephalogram (EEG) and evoked potentials, along with the level of expertise necessary for accurate interpretation of the voluminous data collected. Anaesthetic agents along with variations in physiological parameters can markedly alter the recordings not to mention the influence of diathermy, other electrical devices, muscle activity and artifact. Because of these inherent difficulties, most anaesthetists still rely on optimising such physiological parameters as arterial, venous and intracranial pressures, oxygen and carbon dioxide tensions, to ensure the functional integrity of the nervous system. This brief review explores the potential areas of application of electrophysiologic monitoring in surgery and anaesthesia. 1–5
APA, Harvard, Vancouver, ISO, and other styles
22

Kreuzer, Matthias, Eberhard F. Kochs, Gerhard Schneider, and Denis Jordan. "Non-stationarity of EEG during wakefulness and anaesthesia: advantages of EEG permutation entropy monitoring." Journal of Clinical Monitoring and Computing 28, no. 6 (January 18, 2014): 573–80. http://dx.doi.org/10.1007/s10877-014-9553-y.

Full text
APA, Harvard, Vancouver, ISO, and other styles
23

JÄaUntti, V., A. Yli-Hankala, G. A. Baer, and T. Porkkala. "Slow potentials of EEG burst suppression pattern during anaesthesia." Acta Anaesthesiologica Scandinavica 37, no. 1 (January 1993): 121–23. http://dx.doi.org/10.1111/j.1399-6576.1993.tb03612.x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
24

Schultz, A., U. Grouven, I. Zander, F. A. Beger, M. Siedenberg, and B. Schultz. "Age-related effects in the EEG during propofol anaesthesia." Acta Anaesthesiologica Scandinavica 48, no. 1 (December 12, 2003): 27–34. http://dx.doi.org/10.1111/j.1399-6576.2004.00258.x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
25

Becker, Klaus, Gerhard Schneider, Matthias Eder, Andreas Ranft, Eberhard F. Kochs, Walter Zieglgänsberger, and Hans-Ulrich Dodt. "Anaesthesia Monitoring by Recurrence Quantification Analysis of EEG Data." PLoS ONE 5, no. 1 (January 26, 2010): e8876. http://dx.doi.org/10.1371/journal.pone.0008876.

Full text
APA, Harvard, Vancouver, ISO, and other styles
26

Hayashi, K., K. Indo, and T. Sawa. "Anaesthesia-dependent oscillatory EEG features in the super-elderly." Clinical Neurophysiology 131, no. 9 (September 2020): 2150–57. http://dx.doi.org/10.1016/j.clinph.2020.05.027.

Full text
APA, Harvard, Vancouver, ISO, and other styles
27

Obert, David P., Catrin Schweizer, Sebastian Zinn, Stephan Kratzer, Darren Hight, Jamie Sleigh, Gerhard Schneider, Paul S. García, and Matthias Kreuzer. "The influence of age on EEG-based anaesthesia indices." Journal of Clinical Anesthesia 73 (October 2021): 110325. http://dx.doi.org/10.1016/j.jclinane.2021.110325.

Full text
APA, Harvard, Vancouver, ISO, and other styles
28

Stasiowski, Michał, Anna Duława, Izabela Szumera, Radosław Marciniak, Ewa Niewiadomska, Wojciech Kaspera, Lech Krawczyk, Piotr Ładziński, Beniamin Oskar Grabarek, and Przemysław Jałowiecki. "Variations in Values of State, Response Entropy and Haemodynamic Parameters Associated with Development of Different Epileptiform Patterns during Volatile Induction of General Anaesthesia with Two Different Anaesthetic Regimens Using Sevoflurane in Comparison with Intravenous Induct: A Comparative Study." Brain Sciences 10, no. 6 (June 12, 2020): 366. http://dx.doi.org/10.3390/brainsci10060366.

Full text
Abstract:
Background and Objectives: Raw electroencephalographic (EEG) signals are rarely used to monitor the depth of volatile induction of general anaesthesia (VIGA) with sevoflurane, even though EEG-based indices may show aberrant values. We aimed to identify whether response (RE) and state entropy (SE) variations reliably reflect the actual depth of general anaesthesia in the presence of different types of epileptiform patterns (EPs) in EEGs during induction of general anaesthesia. Materials and Methods: A randomized, prospective clinical study was performed with 60 patients receiving VIGA using sevoflurane with the increasing concentrations (group VIMA) or the vital capacity (group VCRII) technique or an intravenous single dose of propofol (group PROP). Facial electromyography (fEMG), fraction of inspired sevoflurane (FiAA), fraction of expired sevoflurane (FeAA), minimal alveolar concentration (MAC) of sevoflurane, RE and SE, and standard electroencephalographic evaluations were performed in these patients. Results: In contrast to periodic epileptiform discharges, erroneous SE and RE values in the patients’ EEGs were associated with the presence of polyspikes (PS) and rhythmic polyspikes (PSR), which were more likely to indicate toxic depth rather than false emergence from anaesthesia with no changes in the FiAA, FeAA, and MAC of sevoflurane. Conclusion: Calculated RE and SE values may be misleading during VIGA when EPs are present in patients’ EEGs. During VIGA with sevoflurane, we recommend monitoring raw EEG data in scientific studies to correlate it with potentially erroneous RE and SE values and the end-tidal concentration of sevoflurane in everyday clinical practice, when monitoring raw EEG is not available, because they can mislead anaesthesiologists to reduce sevoflurane levels in the ventilation gas and result in unintentional true emergence from anaesthesia. Further studies are required to investigate the behaviour of EEG-based indices during rapid changes in sevoflurane concentrations at different stages of VIGA and the influence of polyspikes and rhythmic polyspikes on the transformation of EEG signals into a digital form.
APA, Harvard, Vancouver, ISO, and other styles
29

Liu, Quan, Li Ma, Shou-Zen Fan, Maysam F. Abbod, and Jiann-Shing Shieh. "Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries." PeerJ 6 (May 23, 2018): e4817. http://dx.doi.org/10.7717/peerj.4817.

Full text
Abstract:
Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients’ anaesthetic level during surgeries.
APA, Harvard, Vancouver, ISO, and other styles
30

Langford, R. M., and C. E. Thomsen. "The Value to the Anaesthetist of Monitoring Cerebral Activity." Methods of Information in Medicine 33, no. 01 (1994): 133–38. http://dx.doi.org/10.1055/s-0038-1634989.

Full text
Abstract:
Abstract:The administration rate of general anaesthetic drugs is at present guided by clinical experience, and indirect indicators such as haemodynamic parameters. In the presence of muscle relaxants most of the clinical signs of inadequate anaesthesia are lost and accidental awareness may occur. A number of monitoring modalities, primarily based on analysis of the electroencephalogram (EEG), have been proposed for measurement of the anaesthetic depth. Moreover intraoperative cerebral monitoring may also provide the anaesthetist with early warning of cerebral ischaemia, or information on specific neurological pathways. To facilitate this, it is essential to combine analysis of the spontaneous EEG with recording of evoked potentials, to assess both cortical and subcortical activity/events. None of the reviewed methods, however promising, can alone meet all of the requirements for intraoperative monitoring of cerebral function. We suggest that the future direction should be to integrate several modalities in a single device, to provide valuable new information, upon which to base clinical management decisions.
APA, Harvard, Vancouver, ISO, and other styles
31

Viertiö-Oja, H. E., V. Jäntti, P. Talja, H. Tolvanen-Laakso, and A. Yli-Hankala. "Fractal spectrum, bispectrum, complexity, and entropy of EEG during anaesthesia." European Journal of Anaesthesiology 17, Supplement 19 (2000): 83. http://dx.doi.org/10.1097/00003643-200000002-00269.

Full text
APA, Harvard, Vancouver, ISO, and other styles
32

Leclercq, S., C. Hanart, A. De Villé, S. De Hert, and P. Van der Linden. "EEG spectral entropy during anaesthesia in children: halothane versus sevoflurane." European Journal of Anaesthesiology 24, Supplement 39 (June 2007): 128. http://dx.doi.org/10.1097/00003643-200706001-00477.

Full text
APA, Harvard, Vancouver, ISO, and other styles
33

Jordan, D., E. Kochs, J. Blum, and G. Schneider. "Influence of high frequency components on EEG-parameters in anaesthesia." European Journal of Anaesthesiology 25, Sup 44 (May 2008): 5. http://dx.doi.org/10.1097/00003643-200805001-00014.

Full text
APA, Harvard, Vancouver, ISO, and other styles
34

Jäntti, V., E. Sonkajärvi, E. Heikkinen, R. Remes, and S. Rytky. "Recording EEG burst suppression in sevoflurane anaesthesia with depth electrodes." European Journal of Anaesthesiology 27 (June 2010): 57. http://dx.doi.org/10.1097/00003643-201006121-00180.

Full text
APA, Harvard, Vancouver, ISO, and other styles
35

KANAYA, NORIAKI, MASAYASU NAKAYAMA, SATOSHI FUJITA, and AKIYOSHI NAMIKI. "Haemodynamic and EEG Changes During Rapid-Sequence Induction of Anaesthesia." Survey of Anesthesiology 39, no. 4 (August 1995): 249. http://dx.doi.org/10.1097/00132586-199508000-00040.

Full text
APA, Harvard, Vancouver, ISO, and other styles
36

Kanaya, Noriaki, Masayasu Nakayama, Satoshi Fujita, and Akiyoshi Namiki. "Haemodynamic and EEG changes during rapid-sequence induction of anaesthesia." Canadian Journal of Anaesthesia 41, no. 8 (August 1994): 699–702. http://dx.doi.org/10.1007/bf03015624.

Full text
APA, Harvard, Vancouver, ISO, and other styles
37

Ortolani, O., A. Conti, A. Di Filippo, C. Adembri, E. Moraldi, A. Evangelisti, M. Maggini, and S. J. Roberts. "EEG signal processing in anaesthesia. Use of a neural network technique for monitoring depth of anaesthesia." British Journal of Anaesthesia 88, no. 5 (May 2002): 644–48. http://dx.doi.org/10.1093/bja/88.5.644.

Full text
APA, Harvard, Vancouver, ISO, and other styles
38

Bartel, PR, FJ Smith, and PJ Becker. "A comparison of EEG spectral entropy with conventional quantitative EEG at varying depths of sevoflurane anaesthesia." Southern African Journal of Anaesthesia and Analgesia 11, no. 3 (August 2005): 89–93. http://dx.doi.org/10.1080/22201173.2005.10872405.

Full text
APA, Harvard, Vancouver, ISO, and other styles
39

SHIEH, JIANN-SHING, SHOU-ZEN FAN, and WEN-LONG SHI. "THE INTELLIGENT ARCHITECTURE FOR SIMULATION OF INHALATIONAL ANAESTHESIA." Biomedical Engineering: Applications, Basis and Communications 16, no. 05 (October 25, 2004): 272–80. http://dx.doi.org/10.4015/s1016237204000384.

Full text
Abstract:
In order to simulate the whole operation during inhalational anaesthesia, an intelligent architecture for the monitoring, control and modelling includes four blocks, which are a hierarchical structure for monitoring depth of anaesthesia (DOA), drug controller, patient model, and vaporizer model. In the first block, using the electroencephalograph (EEG) signals (i.e., bispectral index (BIS)), end-tidal anesthetic agents (Etaa) as the first level for primary DOA, and the systolic arterial pressure (SAP) and heart rate (HR) as the second level for secondary DOA, a hierarchical structure is therefore used to merge these two levels to decide DOA via fuzzy model. Block 2 is a drug controller that controls the drug infusion to the patient using a fuzzy logic controller. In the third block, a four-input and four-output neural network has been designed for patient model. Finally, in the fourth block, a single input and output neural network has also been designed for vaporizer model. Simulating the 13 clinical patients' status with different set points of DOA (defined from 0 ∼ 100), the average of drug consumptions of the anaesthetic gas is 0.93±0.26, 0.69+0.24, and 0.74 ± 0.21 % when setting the set point of DOA at 50 (i.e., anaesthetic ok), 30 (i.e., anaesthetic small deep), and 10 (i.e., anaesthetic deep), respectively. In comparison with the routine standard practice group (i.e., 0.69±0.30 %) using the same inhalational gas, there is a significant difference in DOA at 50 (p < 0.05) but no any significant differences in DOA at either 30 or 10 (p > 0.05). We conclude that the routine standard practice of clinical trials is always controlled DOA at 30 and 10 which are lesser drugs used than DOA at 50.
APA, Harvard, Vancouver, ISO, and other styles
40

Yli-Hankala, A., H. Eskola, and S. Kaukinen. "EEG spectral power during halothane anaesthesia. A comparison of spectral bands in the monitoring of anaesthesia level." Acta Anaesthesiologica Scandinavica 33, no. 4 (May 1989): 304–8. http://dx.doi.org/10.1111/j.1399-6576.1989.tb02913.x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
41

GAJRAJ, R. J., M. DOI, H. MANTZARIDIS, and G. N. C. KENNY. "Comparison of Bispectral EEG Analysis and Auditory Evoked Potentials for Monitoring Depth Of Anaesthesia During Propofol Anaesthesia." Survey of Anesthesiology 44, no. 2 (April 2000): 115. http://dx.doi.org/10.1097/00132586-200004000-00054.

Full text
APA, Harvard, Vancouver, ISO, and other styles
42

Gajraj, R. J., M. Doi, H. Mantzaridis, and G. N. Kenny. "Comparison of bispectral EEG analysis and auditory evoked potentials for monitoring depth of anaesthesia during propofol anaesthesia." British Journal of Anaesthesia 82, no. 5 (May 1999): 672–78. http://dx.doi.org/10.1093/bja/82.5.672.

Full text
APA, Harvard, Vancouver, ISO, and other styles
43

Nieminen, K., S. Westerèn-Punnonen, H. Kokki, H. Yppärilä, A. Hyvärinen, and J. Partanen. "Sevoflurane anaesthesia in children after induction of anaesthesia with midazolam and thiopental does not cause epileptiform EEG." British Journal of Anaesthesia 89, no. 6 (December 2002): 853–56. http://dx.doi.org/10.1093/bja/aef290.

Full text
APA, Harvard, Vancouver, ISO, and other styles
44

Rytky, S., A.-M. Huotari, S. Alahuhta, R. Remes, K. Suominen, and V. Jäntti. "Tibial nerve somatosensory evoked potentials during EEG suppression in sevoflurane anaesthesia." Clinical Neurophysiology 110, no. 9 (September 1999): 1655–58. http://dx.doi.org/10.1016/s1388-2457(99)00129-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
45

Pritchett, Stacey, Eugene Zilberg, Zheng Xu, Paul Myles, Ian Brown, and David Burton. "Peak and averaged bicoherence for different EEG patterns during general anaesthesia." BioMedical Engineering OnLine 9, no. 1 (2010): 76. http://dx.doi.org/10.1186/1475-925x-9-76.

Full text
APA, Harvard, Vancouver, ISO, and other styles
46

Veselis, R. A. "What about β? Relationship between pain and EEG spindles during anaesthesia." British Journal of Anaesthesia 115 (July 2015): i3—i5. http://dx.doi.org/10.1093/bja/aev223.

Full text
APA, Harvard, Vancouver, ISO, and other styles
47

Lickvantzev, V., V. Soubbotin, O. Petrov, A. Volovik, and N. Burov. "Different EEG analysis of low flow xenon anaesthesia during laporoscopic surgery." European Journal of Anaesthesiology 17, Supplement 19 (2000): 20. http://dx.doi.org/10.1097/00003643-200000002-00066.

Full text
APA, Harvard, Vancouver, ISO, and other styles
48

Hutt, Axel, Jérémie Lefebvre, Darren Hight, and Jamie Sleigh. "Suppression of underlying neuronal fluctuations mediates EEG slowing during general anaesthesia." NeuroImage 179 (October 2018): 414–28. http://dx.doi.org/10.1016/j.neuroimage.2018.06.043.

Full text
APA, Harvard, Vancouver, ISO, and other styles
49

Hanart, C., S. Leclercq, M. Gerin, S. De Hert, and P. Van der Linden. "EEG spectral entropy during anaesthesia in children: effects of nitrous oxide." European Journal of Anaesthesiology 24, Supplement 39 (June 2007): 128–29. http://dx.doi.org/10.1097/00003643-200706001-00478.

Full text
APA, Harvard, Vancouver, ISO, and other styles
50

Leclercq, S., F. De Groote, A. De Villé, S. De Hert, and P. Van der Linden. "EEG spectral entropy during sevoflurane anaesthesia in children: influence of age." European Journal of Anaesthesiology 24, Supplement 39 (June 2007): 129. http://dx.doi.org/10.1097/00003643-200706001-00479.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the bibliographies on the topic 'Anaesthesia; EEG' for other source types:
Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography