Academic literature on the topic 'Clinical EEG data'
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Journal articles on the topic "Clinical EEG data":
Járdánházy, T., I. Somogyi, and T. Asztalos. "Compression methods for EEG spectral data." Electroencephalography and Clinical Neurophysiology 87, no. 2 (August 1993): S133. http://dx.doi.org/10.1016/0013-4694(93)91489-n.
Banquet, J. P., W. Guenther, and D. Breitling. "Multidimensional factorial methods for EEG data." Electroencephalography and Clinical Neurophysiology 61, no. 3 (September 1985): S231. http://dx.doi.org/10.1016/0013-4694(85)90874-0.
Antony, Mary Judith, Baghavathi Priya Sankaralingam, Shakir Khan, Abrar Almjally, Nouf Abdullah Almujally, and Rakesh Kumar Mahendran. "Brain–Computer Interface: The HOL–SSA Decomposition and Two-Phase Classification on the HGD EEG Data." Diagnostics 13, no. 17 (September 3, 2023): 2852. http://dx.doi.org/10.3390/diagnostics13172852.
Gu, Yuqiao, Geir Halnes, Hans Liljenström, and Björn Wahlund. "A cortical network model for clinical EEG data analysis." Neurocomputing 58-60 (June 2004): 1187–96. http://dx.doi.org/10.1016/j.neucom.2004.01.184.
Goldenholz, Daniel M., Joseph J. Tharayil, Rubin Kuzniecky, Philippa Karoly, William H. Theodore, and Mark J. Cook. "Simulating clinical trials with and without intracranial EEG data." Epilepsia Open 2, no. 2 (January 18, 2017): 156–61. http://dx.doi.org/10.1002/epi4.12038.
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.
Salam, Abdus, Selina Husna Banu, Abu Nayeem, and Zobaida Sultana Susan. "Clinical Finding of Electroencephalographic (EEG) Data in Adults: A Retrospective study." Journal of Shaheed Suhrawardy Medical College 6, no. 1 (March 7, 2017): 14–17. http://dx.doi.org/10.3329/jssmc.v6i1.31486.
Noachtar, Soheyl, Jan Remi, and Elisabeth Kaufmann. "EEG-Update." Klinische Neurophysiologie 53, no. 04 (November 29, 2022): 243–52. http://dx.doi.org/10.1055/a-1949-1691.
Cincotti, F., C. Babiloni, C. Miniussi, F. Carducci, D. Moretti, S. Salinari, R. Pascual-Marqui, P. M. Rossini, and F. Babiloni. "EEG Deblurring Techniques in a Clinical Context." Methods of Information in Medicine 43, no. 01 (2004): 114–17. http://dx.doi.org/10.1055/s-0038-1633846.
Kutafina, Ekaterina, Alexander Brenner, Yannic Titgemeyer, Rainer Surges, and Stephan Jonas. "Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection." PeerJ 8 (May 1, 2020): e8969. http://dx.doi.org/10.7717/peerj.8969.
Dissertations / Theses on the topic "Clinical EEG data":
Abazid, Majd. "Topological study of the brain functional organization at the early stages of Alzheimer's disease using electroencephalography." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS026.
Electroencephalography (EEG) is still considered nowadays as a convenient neuroimaging technique in clinical applications, suitable for cognitively and physically disabled patients, as well as for serial tests. In fact, EEG is a non-invasive, cost-effective, and mobile technology. It is characterized by a high temporal resolution, which is crucial for the analysis of fast brain functional dynamics.There is a rich literature addressing the use of EEG to investigate brain activity alterations due to neurodegenerative diseases, especially Alzheimer's disease (AD). AD is a chronic neurodegenerative disease that leads to progressive decline of cognitive functions along with behavioral disorders and insidious loss of autonomy in daily living activities. We observe a growing interest in the earlier stages of the disease since curative treatments are still lacking. The preclinical stage of AD is asymptomatic, but the brain lesions due to AD are present. At this phase, the term of subjective cognitive impairment (SCI) has been recently defined. In the prodromal stage, mild cognitive impairment (MCI) patients show measurable memory impairments but their functional capacity is maintained. SCI and MCI patients are at high risk of developing AD.This thesis investigates the early diagnosis of AD at preclinical and prodromal stages using resting-state EEG, and addresses brain network analysis by studying the functional connectivity over several clinical stages of cognitive decline (SCI, MCI and Mild AD). To this end, we conduct a retrospective study using a clinical database that contains EEG signals recorded in real-life conditions.We first propose to exploit an entropy measure, termed “epoch-based entropy” (EpEn), as a measure of functional connectivity, that relies on a refined statistical modeling of EEG signals based on Hidden Markov Models. This measure characterizes the spatiotemporal changes in EEG signals by quantifying the information content of EEG signals, both at the time and spatial levels.Furthermore, we conduct a topological brain network analysis over the three stages of cognitive decline by employing the Graph Theory. The novelty of our work is twofold. Actually, this is the first work that: (i) addresses EEG brain network analysis over SCI, MCI and Mild AD stages simultaneously, and (ii) combines EpEn to Graph Theory since we have shown its effectiveness in quantifying the complete spatiotemporal alteration due to AD.In this thesis, we decided to invest the largest amount of EEG information for brain network analysis, by exploiting several frequency ranges (delta, theta, alpha, beta), several electrodes locations (instead of regions), and several network density scales (multiple graph thresholding). Therefore, another issue tackled in this thesis concerns the identification of relevant EEG markers to discriminate automatically between SCI, MCI and AD patients in the context of graph analysis framework. To this end, we propose an automatic hierarchical method for EEG analysis, which allows the extraction of relevant markers from large amount of information based on a single EEG connectivity measure.Finally, we also assess the correlation between the relevant EEG markers and the clinical markers at our disposal (MMSE, RL/RI-16, BREF)
Matlis, Sean Eben Hill. "Functional network and spectral analysis of clinical EEG data to identify quantitative biomarkers and classify brain disorders." Thesis, 2016. https://hdl.handle.net/2144/19059.
Zeman, Philip Michael. "Feasibility of Multi-Component Spatio-Temporal Modeling of Cognitively Generated EEG Data and its Potential Application to Research in Functional Anatomy and Clinical Neuropathology." Thesis, 2009. http://hdl.handle.net/1828/5010.
Graduate
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Hiah, Pier Juhng, and 連培中. "Data Stream Mining Technology for ECG Signals of Chronic Pain: Real-Time Tracking and Clinical Correlation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/67yzx2.
國立交通大學
電機資訊國際學程
105
Evaluating and tracking the progress of treatment for chronic pain is challenging because pain is a subjective experience and can be measured only by self-report. Electrocardiography (ECG) has been proven to be a promising source of physiological biomarkers for chronic pain. Previous studies had demonstrated that heart rate variability (HRV) could be associated with different types of pain and also pain perception. This study aims to identify the relationship between HRV indices and chronic pain through collecting resting ECG data and subjective pain severity from patients with chronic migraine and fibromyalgia before and after treatments. In addition, resting ECG data from healthy controls were also collected for comparison. The results derived from time, frequency, and non-linear analyses showed that the HRV of chronic patients were generally lower than that of healthy control subjects. Besides, the HRV of the chronic pain patients in the responder group significantly increased after the medical treatment, indicating that a useful biomarker of the treatment efficacy. Among 10 HRV indices, the non-linear Poincaré plot analysis is a promising HRV indices in monitoring pain severity as well as determining treatment efficacy. Finally, a data stream mining platform was developed for real-time streaming and analyzing of multimodal data. This platform is presented such that they can be used as an aid for biofeedback treatment of chronic pain in the future.
Books on the topic "Clinical EEG data":
Clinical applications of computer analysis of EEG and other neurophysiological signals. Amsterdam: Elsevier, 1986.
Vanhatalo, Sampsa, and J. Matias Palva. Infraslow EEG Activity. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0032.
Sutter, Raoul, Peter W. Kaplan, and Donald L. Schomer. Historical Aspects of Electroencephalography. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0001.
Herring, Christina. Neuromodulation in Psychiatric Disorders. Edited by Anthony J. Bazzan and Daniel A. Monti. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190690557.003.0013.
Thomas, James, and Tanya Monaghan. Clinical data interpretation. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199593972.003.0019.
Katirji, Bashar. Electromyography in Clinical Practice. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190603434.001.0001.
Stanley, Barbara, and Antonia New, eds. Borderline Personality Disorder. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199997510.001.0001.
Staedtke, Verena, and Eric H. Kossoff. Epilepsy Syndromes in Childhood. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0074.
Poddubnyy, Denis, and Hildrun Haibel. Treatment: DMARDs. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198734444.003.0021.
Kirollos, Ramez, Adel Helmy, Simon Thomson, and Peter Hutchinson, eds. Oxford Textbook of Neurological Surgery. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198746706.001.0001.
Book chapters on the topic "Clinical EEG data":
Gerlá, Vaclav, Lenka Lhotska, Matej Murgas, Vladana Djordjevic Radisavljevic, Vladimir Krajca, and Vaclav Kremen. "An Incremental Approach to Clinical EEG Data Classification." In IFMBE Proceedings, 489–92. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11128-5_122.
Hammond, E. J., C. P. Barber, and B. J. Wilder. "Flash Visual Evoked Potential Topographic Mapping: Normative and Clinical Data." In Topographic Brain Mapping of EEG and Evoked Potentials, 265–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-72658-3_27.
Hammond, E. J., C. P. Barber, and B. J. Wilder. "Scalp Topography of Red LED Flash-Evoked Potentials: Normal and Clinical Data." In Topographic Brain Mapping of EEG and Evoked Potentials, 373–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-72658-3_42.
Chowdhury, Linkon, Bristy Roy Chowdhury, V. Rajinikanth, and Nilanjan Dey. "A Framework to Evaluate and Classify the Clinical-Level EEG Signals with Epilepsy." In Proceedings of International Conference on Data Science and Applications, 111–21. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7561-7_8.
Dasgupta, Abhijit, Losiana Nayak, Ritankar Das, Debasis Basu, Preetam Chandra, and Rajat K. De. "Feature Selection and Fuzzy Rule Mining for Epileptic Patients from Clinical EEG Data." In Lecture Notes in Computer Science, 87–95. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69900-4_11.
Molina, Edward, Ricardo Salazar-Cabrera, and Diego M. López. "NeuroEHR: Open Source Telehealth System for the Management of Clinical Data, EEG and Remote Diagnosis of Epilepsy." In Communications in Computer and Information Science, 418–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00350-0_35.
Herff, Christian, and Dean J. Krusienski. "Extracting Features from Time Series." In Fundamentals of Clinical Data Science, 85–100. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99713-1_7.
Silva, Hugo, André Lourenço, Ana Fred, and Joaquim Filipe. "Clinical Data Privacy and Customization via Biometrics Based on ECG Signals." In Lecture Notes in Computer Science, 121–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25364-5_12.
Storås, Andrea M., Michael A. Riegler, Trine B. Haugen, Vajira Thambawita, Steven A. Hicks, Hugo L. Hammer, Radhika Kakulavarapu, Pål Halvorsen, and Mette H. Stensen. "Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure." In Communications in Computer and Information Science, 111–21. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17030-0_9.
Chukwu, Emmanuel C., and Pedro A. Moreno-Sánchez. "Enhancing Arrhythmia Diagnosis with Data-Driven Methods: A 12-Lead ECG-Based Explainable AI Model." In Communications in Computer and Information Science, 242–59. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_16.
Conference papers on the topic "Clinical EEG data":
Dasgupta, Abhijit, Ritankar Das, Losiana Nayak, and Rajat K. De. "Analyzing epileptogenic brain connectivity networks using clinical EEG data." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359791.
Huang, Zexin, Liyong Han, Zhihua Huang, Zhixiong Lin, and Chenghua Wang. "Automated data set construction system for clinical EEG research." In 2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2023. http://dx.doi.org/10.1109/cisp-bmei60920.2023.10373261.
Binnie, C. D. "Long term EEG recording and its role in clinical practice." In IEE Colloquium on Data Logging of Physiological Signals. IEE, 1995. http://dx.doi.org/10.1049/ic:19951385.
Yang, S., S. Lopez, M. Golmohammadi, I. Obeid, and J. Picone. "Semi-automated annotation of signal events in clinical EEG data." In 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2016. http://dx.doi.org/10.1109/spmb.2016.7846855.
Aghaeeaval, Mahsa, Nathaniel Bendahan, Zaitoon Shivji, Carter McInnis, Amoon Jamzad, Lysa Boisse Lomax, Garima Shukla, Parvin Mousavi, and Gavin P. Winston. "Prediction of patient survival following postanoxic coma using EEG data and clinical features." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9629946.
Doborjeh, Maryam Gholami, and Nikola Kasabov. "Personalised modelling on integrated clinical and EEG Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network system." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727358.
Smid, Jerusa, Ricardo Nitrini, Vilma Martins, Michele Landemberger, Helio Gomes, Nathalie Canedo Canedo, and Leila Chimelli. "THE BRAZILIAN SURVEILLANCE FOR PRION DISEASE: CURRENT DATA." In XIII Meeting of Researchers on Alzheimer's Disease and Related Disorders. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1980-5764.rpda014.
M. Alves, Lorraine, Klaus F. Côco, Mariane L. de Souza, and Patrick M. Ciarelli. "Graph Theory Analysis of Microstates in Attention-Deficit Hyperactivity Disorder." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1481.
Sazonova, O. B., and E. M. Troshina. "REFLECTION IN THE EEG OF DISORDERS OF CEREBRAL HEMODYNAMICS IN CHRONIC CEREBRAL ISCHEMIA IN CHILDREN." In NOVEL TECHNOLOGIES IN MEDICINE, BIOLOGY, PHARMACOLOGY AND ECOLOGY. Institute of information technology, 2022. http://dx.doi.org/10.47501/978-5-6044060-2-1.368-375.
Zhavoronkova, Ludmila Alexeevna, Olga Arsen’evna Maksakova, Elena Mikhailovna Кushnir, and Irina Gennadievna Skorjatina. "DIAGNOSTIC AND REHABILITATION OPPORTUNITIES OF DUAL-TASKS FOR BRAIN TRAUMA." In International conference New technologies in medicine, biology, pharmacology and ecology (NT +M&Ec ' 2020). Institute of information technology, 2020. http://dx.doi.org/10.47501/978-5-6044060-0-7.06.
Reports on the topic "Clinical EEG data":
Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42562.
de Carvalho, Clístenes Crístian, Ioannis Kapsokalyvas, and Kariem El-Boghdadly. Second-generation supraglottic airways vs endotracheal tubes in adults undergoing abdominopelvic surgeries: a protocol for a systematic review with pairwise meta-analyses of randomised clinical trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0041.
Meng, kairui, yulin You, lijuan Chen, and yicheng Liu. A meta analysis on the efficacy of Chengqi Decoction in the treatment of ARDS/ALI. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0040.
Leavy, Michelle B., Danielle Cooke, Sarah Hajjar, Erik Bikelman, Bailey Egan, Diana Clarke, Debbie Gibson, Barbara Casanova, and Richard Gliklich. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Report on Registry Configuration. Agency for Healthcare Research and Quality (AHRQ), November 2020. http://dx.doi.org/10.23970/ahrqepcregistryoutcome.
Totten, Annette, Dana M. Womack, Marian S. McDonagh, Cynthia Davis-O’Reilly, Jessica C. Griffin, Ian Blazina, Sara Grusing, and Nancy Elder. Improving Rural Health Through Telehealth-Guided Provider-to-Provider Communication. Agency for Healthcare Research and Quality, December 2022. http://dx.doi.org/10.23970/ahrqepccer254.
McCausland, Rachel, Joann Fontanarosa, and Ravi Patel. Nonemergent Percutaneous Coronary Intervention Versus Optimal Medical Treatment for Stable Ischemic Heart Disease: A Rapid Response Literature Review. Agency for Healthcare Research and Quality (AHRQ), August 2023. http://dx.doi.org/10.23970/ahrqepcrapidcoronary.
Newman-Toker, David E., Susan M. Peterson, Shervin Badihian, Ahmed Hassoon, Najlla Nassery, Donna Parizadeh, Lisa M. Wilson, et al. Diagnostic Errors in the Emergency Department: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), December 2022. http://dx.doi.org/10.23970/ahrqepccer258.
Rankin, Nicole, Deborah McGregor, Candice Donnelly, Bethany Van Dort, Richard De Abreu Lourenco, Anne Cust, and Emily Stone. Lung cancer screening using low-dose computed tomography for high risk populations: Investigating effectiveness and screening program implementation considerations: An Evidence Check rapid review brokered by the Sax Institute (www.saxinstitute.org.au) for the Cancer Institute NSW. The Sax Institute, October 2019. http://dx.doi.org/10.57022/clzt5093.
Peterson, Bradley S., Joey Trampush, Margaret Maglione, Maria Bolshakova, Morah Brown, Mary Rozelle, Aneesa Motala, et al. ADHD Diagnosis and Treatment in Children and Adolescents. Agency for Healthcare Research and Quality (AHRQ), March 2024. http://dx.doi.org/10.23970/ahrqepccer267.
Gong, Xuan, Zhou Chen, Kui Yang, Chuntao Li, Songshan Feng, Mingyu Zhang, Zhixiong Liu, Hongshu Zhou, and Zhenyan Li. Endoscopic Transsphenoidal Surgery for Infra-Diaphragmatic Craniopharyngiomas: Impact of Diaphragm Sellae Competence on Hypothalamic Injury. International Journal of Surgery, May 2024. http://dx.doi.org/10.60122/j.ijs.2024.20.03.