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Journal articles on the topic 'Eeg'

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Author: Grafiati

Published: 4 June 2021

Last updated: 14 September 2024

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1

Camfield, Peter, Kevin Gordon, Carol Camfield, John Tibbies, Joseph Dooley, and Bruce Smith. "EEG Results are Rarely the Same if Repeated within Six Months in Childhood Epilepsy." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 22, no. 4 (November 1995): 297–300. http://dx.doi.org/10.1017/s0317167100039512.

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AbstractObjectiveTo assess the reliability of interictal spike discharge in routine electroencephalography (EEG) testing in children.MethodEEG results of all children diagnosed in Nova Scotia with epilepsy onset between 1977–85 (excluding myoclonic, akinetic-atonic and absence) were reviewed. The results of the EEG at time of diagnosis (EEG1) were compared with those of a second EEG (EEG2) within 6 months.ResultsOf 504 children with epilepsy, 159 had both EEG1 and EEG2. EEG2 was more likely ordered if EEG1 was normal or showed focal slowing but less likely if EEG1 contained sleep (p < 0.05). EEG1 and EEG2 were both normal in 23%. If EEG1 was abnormal, there was a 40–70% discordance for the type of abnormality on EEG2. Abnormalities were present on both EEG1 and EEG2 in 67 cases. Of the 42/67 with major focal abnormalities on EEG1, 7 had only generalized spike wave on EEG2. Of the 17/67 with only generalized spike wave on EEG 1, 7 showed only major focal abnormalities on EEG2. Statistical testing showed low Kappa scores indicating low reliability.ConclusionsThe interictal EEG in childhood epilepsy appears to be an unstable test. A repeat EEG within 6 months of a first EEG may yield different and sometimes conflicting information.

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2

Reilly, Richard B., and T. Clive Lee. "Electrograms (ECG, EEG, EMG, EOG)." Technology and Health Care 18, no. 6 (November 19, 2010): 443–58. http://dx.doi.org/10.3233/thc-2010-0604.

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3

Hawkins, Margaret. "ECG for the EEG Technologist." American Journal of EEG Technology 32, no. 1 (March 1992): 46–57. http://dx.doi.org/10.1080/00029238.1992.11080391.

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4

Kamata, K., T. Ylinen, N. P. Subramaniyam, A. Yli-Hankala, A. J. Aho, and V. Jäntti. "ECG artifact in EEG monitoring." European Journal of Anaesthesiology 29 (June 2012): 51. http://dx.doi.org/10.1097/00003643-201206001-00165.

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5

SASAKI, Minoru, and Kyoung ho Choi. "Removal of artifacts From EEG in a normal subject." Proceedings of the Conference on Information, Intelligence and Precision Equipment : IIP 2002 (2002): 181–84. http://dx.doi.org/10.1299/jsmeiip.2002.181.

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6

Zaiwalla, Zenobia. "To EEG or not EEG." Paediatrics and Child Health 28, no. 6 (June 2018): 289–92. http://dx.doi.org/10.1016/j.paed.2018.04.013.

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7

Guevara, Miguel Angel, and María Corsi-Cabrera. "EEG coherence or EEG correlation?" International Journal of Psychophysiology 23, no. 3 (October 1996): 145–53. http://dx.doi.org/10.1016/s0167-8760(96)00038-4.

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8

Ikeda, Akio. "WS1.9. Advances in EEG Analysis – Wide-Band EEG, Dense-Array EEG and Quantitative EEG." Clinical Neurophysiology 132, no. 8 (August 2021): e53. http://dx.doi.org/10.1016/j.clinph.2021.02.072.

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9

Boesebeck, Frank. "Digitales EEG und sinnvolle EEG-Montagen in der EEG-Routinediagnostik." Das Neurophysiologie-Labor 30, no. 1 (August 2008): 1–13. http://dx.doi.org/10.1016/j.neulab.2008.04.005.

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10

Yang, Chia-Yen, Pin-Chen Chen, and Wen-Chen Huang. "Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models." Sensors 23, no. 5 (February 23, 2023): 2458. http://dx.doi.org/10.3390/s23052458.

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Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal resolution. In contrast to electrocardiography (ECG), EEG can be uncomfortable and inconvenient for patients. Moreover, deep-learning techniques require a large dataset and a long time for training from scratch. Therefore, in this study, EEG–EEG or EEG–ECG transfer learning strategies were applied to explore their effectiveness for the training of simple cross-domain convolutional neural networks (CNNs) used in seizure prediction and sleep staging systems, respectively. The seizure model detected interictal and preictal periods, whereas the sleep staging model classified signals into five stages. The patient-specific seizure prediction model with six frozen layers achieved 100% accuracy for seven out of nine patients and required only 40 s of training time for personalization. Moreover, the cross-signal transfer learning EEG–ECG model for sleep staging achieved an accuracy approximately 2.5% higher than that of the ECG model; additionally, the training time was reduced by >50%. In summary, transfer learning from an EEG model to produce personalized models for a more convenient signal can both reduce the training time and increase the accuracy; moreover, challenges such as data insufficiency, variability, and inefficiency can be effectively overcome.

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11

Michel, V., L. Mazzola, M. Lemesle, and L. Vercueil. "Long-term EEG in adults: Sleep-deprived EEG (SDE), ambulatory EEG (Amb-EEG) and long-term video-EEG recording (LTVER)." Neurophysiologie Clinique/Clinical Neurophysiology 45, no. 1 (March 2015): 47–64. http://dx.doi.org/10.1016/j.neucli.2014.11.004.

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12

Issa, Mohamed F., Gergely Tuboly, György Kozmann, and Zoltan Juhasz. "Automatic ECG Artefact Removal from EEG Signals." Measurement Science Review 19, no. 3 (June 1, 2019): 101–8. http://dx.doi.org/10.2478/msr-2019-0016.

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Abstract Electroencephalography (EEG) signals are frequently contaminated by ocular, muscle, and cardiac artefacts whose removal normally requires manual inspection or the use of reference channels (EOG, EMG, ECG). We present a novel, fully automatic method for the detection and removal of ECG artefacts that works without a reference ECG channel. Independent Component Analysis (ICA) is applied to the measured data and the independent components are examined for the presence of QRS waveforms using an adaptive threshold-based QRS detection algorithm. Detected peaks are subsequently classified by a rule-based classifier as ECG or non-ECG components. Components manifesting ECG activity are marked for removal, and then the artefact-free signal is reconstructed by removing these components before performing the inverse ICA. The performance of the proposed method is evaluated on a number of EEG datasets and compared to results reported in the literature. The average sensitivity of our ECG artefact removal method is above 99 %, which is better than known literature results.

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13

Chi, Yu M., Patrick Ng, and Gert Cauwenberghs. "Wireless noncontact ECG and EEG biopotential sensors." ACM Transactions on Embedded Computing Systems 12, no. 4 (June 2013): 1–19. http://dx.doi.org/10.1145/2485984.2485991.

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14

Von Rosenberg, Wilhelm, Theerasak Chanwimalueang, Valentin Goverdovsky, David Looney, David Sharp, and Danilo P. Mandic. "Smart Helmet: Wearable Multichannel ECG and EEG." IEEE Journal of Translational Engineering in Health and Medicine 4 (2016): 1–11. http://dx.doi.org/10.1109/jtehm.2016.2609927.

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15

Todd, Michael M. "EEGs, EEG Processing, and the Bispectral Index." Anesthesiology 89, no. 4 (October 1, 1998): 815–17. http://dx.doi.org/10.1097/00000542-199810000-00002.

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16

Stephenson, J. B. P. "Need for ECG channel in ambulatory EEG." Electroencephalography and Clinical Neurophysiology 61, no. 3 (September 1985): S137. http://dx.doi.org/10.1016/0013-4694(85)90535-8.

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17

Thatcher, Robert W. "NORMATIVE EEG DATABASES AND EEG BIOFEEDBACK." Journal of Neurotherapy 2, no. 4 (March 1998): 8–39. http://dx.doi.org/10.1300/j184v02n04_02.

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18

Zhirong, M. "EEG hysterical provocative test (EEG - HPT)." Electroencephalography and Clinical Neurophysiology 103, no. 1 (July 1997): 217. http://dx.doi.org/10.1016/s0013-4694(97)89047-9.

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19

Schenk, S., K. Lamm, H. G�ndel, and K. H. Ladwig. "Neurofeedbackgest�tztes EEG-?- und EEG-?-Training." HNO 53, no. 1 (January 2005): 29–38. http://dx.doi.org/10.1007/s00106-004-1066-4.

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20

Mandekar, Swati, Abigail Holland, Moritz Thielen, Mehdi Behbahani, and Mark Melnykowycz. "Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG." Sensors 22, no. 4 (February 17, 2022): 1568. http://dx.doi.org/10.3390/s22041568.

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Wearable EEG has gained popularity in recent years driven by promising uses outside of clinics and research. The ubiquitous application of continuous EEG requires unobtrusive form-factors that are easily acceptable by the end-users. In this progression, wearable EEG systems have been moving from full scalp to forehead and recently to the ear. The aim of this study is to demonstrate that emerging ear-EEG provides similar impedance and signal properties as established forehead EEG. EEG data using eyes-open and closed alpha paradigm were acquired from ten healthy subjects using generic earpieces fitted with three custom-made electrodes and a forehead electrode (at Fpx) after impedance analysis. Inter-subject variability in in-ear electrode impedance ranged from 20 kΩ to 25 kΩ at 10 Hz. Signal quality was comparable with an SNR of 6 for in-ear and 8 for forehead electrodes. Alpha attenuation was significant during the eyes-open condition in all in-ear electrodes, and it followed the structure of power spectral density plots of forehead electrodes, with the Pearson correlation coefficient of 0.92 between in-ear locations ELE (Left Ear Superior) and ERE (Right Ear Superior) and forehead locations, Fp1 and Fp2, respectively. The results indicate that in-ear EEG is an unobtrusive alternative in terms of impedance, signal properties and information content to established forehead EEG.

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21

Niedermeyer, E. "Leitfaden für die EEG-Praxis. Ein Bildkompendium (EEG Atlas, illustrated EEG textbook)." Electroencephalography and Clinical Neurophysiology 87, no. 4 (October 1993): 260. http://dx.doi.org/10.1016/0013-4694(93)90033-r.

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22

Thatcher, R. W., D. North, and C. Biver. "EEG and intelligence: Relations between EEG coherence, EEG phase delay and power." Clinical Neurophysiology 116, no. 9 (September 2005): 2129–41. http://dx.doi.org/10.1016/j.clinph.2005.04.026.

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23

Cai, Mengpu, Junxiang Chen, Chengcheng Hua, Guilin Wen, and Rongrong Fu. "EEG emotion recognition using EEG-SWTNS neural network through EEG spectral image." Information Sciences 680 (October 2024): 121198. http://dx.doi.org/10.1016/j.ins.2024.121198.

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24

Barry, Robert J., and Frances M. De Blasio. "EEG frequency PCA in EEG-ERP dynamics." Psychophysiology 55, no. 5 (December 11, 2017): e13042. http://dx.doi.org/10.1111/psyp.13042.

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25

Hoppe, Matthias. "EEG-Befundung einschließlich Darstellung des normalen EEG." Das Neurophysiologie-Labor 40, no. 1 (March 2018): 14–43. http://dx.doi.org/10.1016/j.neulab.2017.11.002.

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26

Ramantani, Georgia, Louis Maillard, and Laurent Koessler. "Correlation of invasive EEG and scalp EEG." Seizure 41 (October 2016): 196–200. http://dx.doi.org/10.1016/j.seizure.2016.05.018.

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27

Nuwer, M. R. "S42-1 Digital EEG and EEG Standards." Clinical Neurophysiology 121 (October 2010): S61. http://dx.doi.org/10.1016/s1388-2457(10)60257-x.

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28

Brogger, Jan, Tom Eichele, Eivind Aanestad, Henning Olberg, Ina Hjelland, and Harald Aurlien. "Visual EEG reviewing times with SCORE EEG." Clinical Neurophysiology Practice 3 (2018): 59–64. http://dx.doi.org/10.1016/j.cnp.2018.03.002.

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29

Ivanov, А. А. "Overview of mathematical EEG analysis. Quantitative EEG." Epilepsy and paroxysmal conditions 15, no. 2 (July 9, 2023): 171–92. http://dx.doi.org/10.17749/2077-8333/epi.par.con.2023.154.

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The purpose of this article is to familiarize medical specialists involved in registration and analysis of electroencephalographic (EEG) studies using methods of mathematical processing and analysis for recorded EEG data. Understanding the principles of how quantitative EEG analysis tools work should help medical personnel to properly use their capabilities and ultimately improve quality of medical care. Here, we discuss basic and innovative mathematical tools for EEG processing and analysis.

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30

Salmi, T. K., J. Voipio, and P. Kauppinen. "PO08-MO-04 Innovation in emergency neurology: recording of EEG with an EEG-to-ECG adapter." Journal of the Neurological Sciences 285 (October 2009): S190. http://dx.doi.org/10.1016/s0022-510x(09)70731-2.

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31

Bénar, Christian-G., Christophe Grova, Eliane Kobayashi, Andrew P. Bagshaw, Yahya Aghakhani, François Dubeau, and Jean Gotman. "EEG–fMRI of epileptic spikes: Concordance with EEG source localization and intracranial EEG." NeuroImage 30, no. 4 (May 2006): 1161–70. http://dx.doi.org/10.1016/j.neuroimage.2005.11.008.

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32

Pittau, Francesca, Pierre LeVan, Friederike Moeller, Taha Gholipour, Claire Haegelen, Rina Zelmann, François Dubeau, and Jean Gotman. "Changes preceding interictal epileptic EEG abnormalities: Comparison between EEG/fMRI and intracerebral EEG." Epilepsia 52, no. 6 (April 19, 2011): 1120–29. http://dx.doi.org/10.1111/j.1528-1167.2011.03072.x.

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33

Heckmann, J. G., P. Portwich, F. Kerling, H. Stefan, and B. Neundörfer. "Simultaneous EEG and ECG recording of sinus arrest." Intensive Care Medicine 27, no. 8 (June 20, 2001): 1432. http://dx.doi.org/10.1007/s001340100997.

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34

Gilchrist, J. M. "Arrhythmogenic seizures: Diagnosis by simultaneous EEG/ECG recording." Neurology 35, no. 10 (October 1, 1985): 1503. http://dx.doi.org/10.1212/wnl.35.10.1503.

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35

Chakrabarti, Dhritiman, and Sonia Bansal. "ECG contamination of EEG signals: effect on entropy." Journal of Clinical Monitoring and Computing 30, no. 1 (April 22, 2015): 119–22. http://dx.doi.org/10.1007/s10877-015-9694-7.

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36

Kalyani P, Jasmine. "EEG Patterns of Unclear Significance & Nonepileptiform EEG Abnormalities: Highlights." International Journal of Science and Research (IJSR) 12, no. 7 (July 5, 2023): 2251–64. http://dx.doi.org/10.21275/sr23618220804.

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37

Bobby, Mrs J. Sofia. "EEG Radio Telemetry." International journal of Emerging Trends in Science and Technology 04, no. 06 (June 9, 2017): 5221–27. http://dx.doi.org/10.18535/ijetst/v4i6.01.

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38

Boßelmann, Christian, and Holger Lerche. "Elektroenzephalografie (EEG)." Neurologie up2date 04, no. 04 (December 2021): 314–22. http://dx.doi.org/10.1055/a-1645-6477.

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39

Tatum, William O. "EEG Essentials." CONTINUUM: Lifelong Learning in Neurology 28, no. 2 (April 2022): 261–305. http://dx.doi.org/10.1212/con.0000000000001129.

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40

C., M. "EEG mapping." Neurology 41, no. 6 (June 1, 1991): 951. http://dx.doi.org/10.1212/wnl.41.6.951-b.

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41

Miskin, Chandrabhaga, Karen S. Carvalho, Ignacio Valencia, Agustin Legido, and Divya S. Khurana. "EEG Duration." Journal of Child Neurology 30, no. 13 (March 26, 2015): 1767–69. http://dx.doi.org/10.1177/0883073815579969.

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42

Swatzyna, Ronald J., Gerald P. Kozlowski, and Jay D. Tarnow. "Pharmaco-EEG." Clinical EEG and Neuroscience 46, no. 3 (November 23, 2014): 192–96. http://dx.doi.org/10.1177/1550059414556120.

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43

Boczko, M. L. "Quantitative EEG." Neurology 45, no. 9 (September 1, 1995): 1785. http://dx.doi.org/10.1212/wnl.45.9.1785.

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44

Wusthoff, Courtney J. "Neonatal EEG." Journal of Clinical Neurophysiology 33, no. 5 (October 2016): 375. http://dx.doi.org/10.1097/wnp.0000000000000298.

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45

Kochs, E. "EEG Monitoring." European Journal of Anaesthesiology 15, Supplement 17 (January 1998): 65–66. http://dx.doi.org/10.1097/00003643-199801001-00044.

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46

Alhambra, Marabella A., Timothy P. Fowler, and Antonio A. Alhambra. "EEG Biofeedback:." Journal of Neurotherapy 1, no. 2 (August 1995): 39–43. http://dx.doi.org/10.1300/j184v01n02_03.

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47

Bhatia, Manvir. "Neonatal EEG." Journal of Neonatology 20, no. 2 (June 2006): 147–58. http://dx.doi.org/10.1177/0973217920060207.

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48

Wazir, Sanjay, and Praveen Kumar. "EEG Machine." Journal of Neonatology 20, no. 2 (June 2006): 187–92. http://dx.doi.org/10.1177/0973217920060214.

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49

Kaiser, David A. "Ranking EEG." Journal of Neurotherapy 12, no. 1 (August 11, 2008): 1–3. http://dx.doi.org/10.1080/10874200802219822.

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50

Nuwer, Marc R. "Quantitative EEG." Journal of Clinical Neurophysiology 5, no. 1 (January 1988): 1–44. http://dx.doi.org/10.1097/00004691-198801000-00001.

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