Health
Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography
Fisher, R. S. et al. ILAE official report: A practical clinical definition of epilepsy. Epilepsia 55, 475–482 (2014).
Tatum, W. O. et al. Clinical utility of EEG in diagnosing and monitoring epilepsy in adults. Clin. Neurophysiol. 129, 1056–1082 (2018).
Pillai, J. & Sperling, M. R. Interictal EEG and the diagnosis of epilepsy. Epilepsia 47, 14–22 (2006).
Baldin, E., Hauser, W. A., Buchhalter, J. R., Hesdorffer, D. C. & Ottman, R. Yield of epileptiform electroencephalogram abnormalities in incident unprovoked seizures: A population-based study. Epilepsia 55, 1389–1398 (2014).
Bouma, H. K., Labos, C., Gore, G. C., Wolfson, C. & Keezer, M. R. The diagnostic accuracy of routine electroencephalography after a first unprovoked seizure. Eur. J. Neurol. 23, 455–463 (2016).
Jing, J. et al. Interrater reliability of experts in identifying interictal epileptiform discharges in electroencephalograms. JAMA Neurol. 77, 49–57 (2020).
Amin, U. & Benbadis, S. R. The role of EEG in the erroneous diagnosis of epilepsy. J. Clin. Neurophysiol. 36, 294–297 (2019).
Chadwick, D. & Smith, D. The misdiagnosis of epilepsy. BMJ 324, 495–496 (2002).
Seneviratne, U., Cook, M. & D’Souza, W. The electroencephalogram of idiopathic generalized epilepsy. Epilepsia 53, 234–248 (2012).
Seneviratne, U., Boston, R. C., Cook, M. & D’Souza, W. EEG correlates of seizure freedom in genetic generalized epilepsies. Neurol. Clin. Pract. 7, 35–44 (2017).
Guida, M., Iudice, A., Bonanni, E. & Giorgi, F. S. Effects of antiepileptic drugs on interictal epileptiform discharges in focal epilepsies: An update on current evidence. Expert Rev. Neurother. 15, 947–959 (2015).
Arntsen, V., Sand, T., Syvertsen, M. R. & Brodtkorb, E. Prolonged epileptiform EEG runs are associated with persistent seizures in juvenile myoclonic epilepsy. Epilepsy Res. 134, 26–32 (2017).
Acharya, U. R., Vinitha Sree, S., Swapna, G., Martis, R. J. & Suri, J. S. Automated EEG analysis of epilepsy: A review. Knowl.-Based Syst. 45, 147–165 (2013).
Woldman, W. et al. Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised. Sci. Rep. 10, 7043 (2020).
Chowdhury, F. A. et al. Revealing a brain network endophenotype in families with idiopathic generalised epilepsy. PLoS ONE 9, e110136 (2014).
Varatharajah, Y. et al. Quantitative analysis of visually reviewed normal scalp EEG predicts seizure freedom following anterior temporal lobectomy. Epilepsia 63, 1630–1642 (2022).
Abela, E. et al. Slower alpha rhythm associates with poorer seizure control in epilepsy. Ann. Clin. Transl. Neurol. 6(2), 333–343 (2019).
Larsson, P. G. & Kostov, H. Lower frequency variability in the alpha activity in EEG among patients with epilepsy. Clin. Neurophysiol. 116, 2701–2706 (2005).
Pegg, E. J., Taylor, J. R. & Mohanraj, R. Spectral power of interictal EEG in the diagnosis and prognosis of idiopathic generalized epilepsies. Epilepsy Behav. 112, 107427 (2020).
Larsson, P. G., Eeg-Olofsson, O. & Lantz, G. Alpha frequency estimation in patients with epilepsy. Clin. EEG Neurosci. 43(2), 97–104 (2012).
Miyauchi, T., Endo, K., Yamaguchi, T. & Hagimoto, H. Computerized analysis of EEG background activity in epileptic patients. Epilepsia 32, 870–881 (1991).
Diaz, G. F. et al. Generalized background qEEG abnormalities in localized symptomatic epilepsy. Electroencephalogr. Clin. Neurophysiol. 106(6), 501–507 (1998).
Urigüen, J. A., García-Zapirain, B., Artieda, J., Iriarte, J. & Valencia, M. Comparison of background EEG activity of different groups of patients with idiopathic epilepsy using Shannon spectral entropy and cluster-based permutation statistical testing. PLoS ONE 12, e0184044 (2017).
Sathyanarayana, A. et al. Measuring the effects of sleep on epileptogenicity with multifrequency entropy. Clin. Neurophysiol. 132, 2012–2018 (2021).
Luo, K. & Luo, D. An EEG feature-based diagnosis model for epilepsy. in 2010 International Conference on Computer Application and System Modeling (ICCASM 2010) vol. 8 V8–592-V8–594 (2010).
Faiman, I., Smith, S., Hodsoll, J., Young, A. H. & Shotbolt, P. Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review. Epilepsy Behav. 121, 108047 (2021).
Engel, J. Jr., Bragin, A. & Staba, R. Nonictal EEG biomarkers for diagnosis and treatment. Epilepsia Open 3, 120–126 (2018).
Dash, D. et al. Update on minimal standards for electroencephalography in Canada: A review by the Canadian Society of Clinical Neurophysiologists. Can. J. Neurol. Sci./J. Can. des Sci. Neurologiques 44, 631–642 (2017).
Jas, M., Engemann, D. A., Bekhti, Y., Raimondo, F. & Gramfort, A. Autoreject: Automated artifact rejection for MEG and EEG data. Neuroimage 159, 417–429 (2017).
Gandhi, T., Panigrahi, B. K. & Anand, S. A comparative study of wavelet families for EEG signal classification. Neurocomputing 74, 3051–3057 (2011).
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B 67, 301–320 (2005).
Ke, G. et al. LightGBM: A highly efficient gradient boosting decision tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems (ed. Ke, G.) 3149–3157 (Curran Associates Inc, 2017).
Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res. 11, 2079–2107 (2010).
LeDell, E., Petersen, M. & van der Laan, M. Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electron. J. Stat. 9, 1583–1607 (2015).
DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44, 837–845 (1988).
Collins, G. S., Reitsma, J. B., Altman, D. G. & Moons, K. G. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. Ann. Intern. Med. https://doi.org/10.7326/M14-0697 (2015).
Clarke, S. et al. Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy. Epilepsy Behav. 121, 106556. https://doi.org/10.1016/j.yebeh.2019.106556 (2019).
Drake, M. E., Padamadan, H. & Newell, S. A. Interictal quantitative EEG in epilepsy. Seizure Eur. J. Epilepsy 7, 39–42 (1998).
Mammone, N. & Morabito, F. C. Analysis of absence seizure EEG via Permutation Entropy spatio-temporal clustering. Int. Jt. Conf. Neural Netw. https://doi.org/10.1109/ijcnn.2011.6033390 (2011).
Lijmer, J. G. et al. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA 282, 1061–1066 (1999).
Pepe, M. S., Feng, Z., Janes, H., Bossuyt, P. M. & Potter, J. D. Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: Standards for study design. J. Natl. Cancer Inst. 100, 1432–1438 (2008).
Zelig, D. et al. Paroxysmal slow wave events predict epilepsy following a first seizure. Epilepsia 63, 190–198 (2022).
Douw, L. et al. ‘Functional connectivity’ is a sensitive predictor of epilepsy diagnosis after the first seizure. PLoS ONE 5, e10839 (2010).
Futoma, J., Simons, M., Panch, T., Doshi-Velez, F. & Celi, L. A. The myth of generalisability in clinical research and machine learning in health care. Lancet Digital Health 2, e489–e492 (2020).
Krumholz, A. et al. Evidence-based guideline: Management of an unprovoked first seizure in adults. Neurology 84, 1705 (2015).
Gloss, D. et al. Antiseizure medication withdrawal in seizure-free patients: Practice advisory update summary: Report of the AAN guideline subcommittee. Neurology 97, 1072–1081 (2021).
Selvitelli, M. F., Walker, L. M., Schomer, D. L. & Chang, B. S. The relationship of interictal epileptiform discharges to clinical epilepsy severity: A study of routine electroencephalograms and review of the literature. J. Clin. Neurophysiol. 27, 87–92 (2010).
Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P. & Lin, C. Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60, 59–70 (2012).
Jollans, L. et al. Quantifying performance of machine learning methods for neuroimaging data. Neuroimage 199, 351–365 (2019).
Fisher, R. S. Bad information in epilepsy care. Epilepsy Behav. 67, 133–134 (2017).
Buchhalter, J. et al. EEG parameters as endpoints in epilepsy clinical trials—An expert panel opinion paper. Epilepsy Res. 187, 107028 (2022).
Jabès, A. et al. Age-related differences in resting-state EEG and allocentric spatial working memory performance. Front. Aging Neurosci. https://doi.org/10.3389/fnagi.2021.704362 (2021).
Blume, W. T. Drug effects on EEG. J. Clin. Neurophysiol. 23, 306–311 (2006).
Nguyen Michel, V.-H. et al. The yield of routine EEG in geriatric patients: A prospective hospital-based study. Neurophysiologie Clinique/Clin. Neurophysiol. 40, 249–254 (2010).
Bučková, B., Brunovský, M., Bareš, M. & Hlinka, J. Predicting sex from EEG: Validity and generalizability of deep-learning-based interpretable classifier. Front. Neurosci. https://doi.org/10.3389/fnins.2020.589303 (2020).
Ahmadi, N., Pei, Y., Carrette, E., Aldenkamp, A. P. & Pechenizkiy, M. EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features. Brain Inf. 7, 6 (2020).
Raj, V. K. et al. Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy. Seizure 61, 8–13 (2018).
Schmidt, H., Petkov, G., Richardson, M. P. & Terry, J. R. Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity. PLoS Comput. Biol. 10, e1003947–e1003947 (2014).
Verhoeven, T. et al. Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes. NeuroImage Clin. 17, 10–15 (2018).
Song, C. et al. A feature tensor-based epileptic detection model based on improved edge removal approach for directed brain networks. Front. Neurosci. 14, 557095 (2020).
Ouyang, C.-S., Yang, R.-C., Wu, R.-C., Chiang, C.-T. & Lin, L.-C. Determination of antiepileptic drugs withdrawal through EEG Hjorth parameter analysis. Int. J. Neur. Syst. 30, 2050036 (2020).
Guerrero, M. C., Parada, J. S. & Espitia, H. E. EEG signal analysis using classification techniques: Logistic regression, artificial neural networks, support vector machines, and convolutional neural networks. Heliyon 7, (2021).
Bosl, W. J., Loddenkemper, T. & Nelson, C. A. Nonlinear EEG biomarker profiles for autism and absence epilepsy. Neuropsychiatr. Electrophysiol. 3, 1 (2017).
Kural, M. A. et al. Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts. Epilepsia 63, 1064–1073 (2022).
Jing, J. et al. Development of expert-level automated detection of epileptiform discharges during electroencephalogram interpretation. JAMA Neurol. https://doi.org/10.1001/jamaneurol.2019.3485 (2019).
Gelety, T. J., Burgess, R. J., Drake, M. E. Jr., Ford, C. E. & Brown, M. E. Computerized spectral analysis of the interictal EEG in epilepsy. Clin. Electroencephalogr. 16, 94–97 (1985).
Varatharajah, Y. et al. Characterizing the electrophysiological abnormalities in visually reviewed normal EEGs of drug-resistant focal epilepsy patients. Brain Commun. 3, fcab102 (2021).
Andrzejak, R. G. et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys. Rev. E 64, 061907 (2001).
Acharya, U. R., Fujita, H., Sudarshan, V. K., Bhat, S. & Koh, J. E. W. Application of entropies for automated diagnosis of epilepsy using EEG signals: A review. Knowl.-Based Syst. 88, 85–96 (2015).
Pyrzowski, J., Sieminski, M., Sarnowska, A., Jedrzejczak, J. & Nyka, W. M. Interval analysis of interictal EEG: Pathology of the alpha rhythm in focal epilepsy. Sci. Rep. 5, 16230 (2015).
|
Sources 2/ https://www.nature.com/articles/s41598-023-39799-8 The mention sources can contact us to remove/changing this article |
What Are The Main Benefits Of Comparing Car Insurance Quotes Online
LOS ANGELES, CA / ACCESSWIRE / June 24, 2020, / Compare-autoinsurance.Org has launched a new blog post that presents the main benefits of comparing multiple car insurance quotes. For more info and free online quotes, please visit https://compare-autoinsurance.Org/the-advantages-of-comparing-prices-with-car-insurance-quotes-online/ The modern society has numerous technological advantages. One important advantage is the speed at which information is sent and received. With the help of the internet, the shopping habits of many persons have drastically changed. The car insurance industry hasn't remained untouched by these changes. On the internet, drivers can compare insurance prices and find out which sellers have the best offers. View photos The advantages of comparing online car insurance quotes are the following: Online quotes can be obtained from anywhere and at any time. Unlike physical insurance agencies, websites don't have a specific schedule and they are available at any time. Drivers that have busy working schedules, can compare quotes from anywhere and at any time, even at midnight. Multiple choices. Almost all insurance providers, no matter if they are well-known brands or just local insurers, have an online presence. Online quotes will allow policyholders the chance to discover multiple insurance companies and check their prices. Drivers are no longer required to get quotes from just a few known insurance companies. Also, local and regional insurers can provide lower insurance rates for the same services. Accurate insurance estimates. Online quotes can only be accurate if the customers provide accurate and real info about their car models and driving history. Lying about past driving incidents can make the price estimates to be lower, but when dealing with an insurance company lying to them is useless. Usually, insurance companies will do research about a potential customer before granting him coverage. Online quotes can be sorted easily. Although drivers are recommended to not choose a policy just based on its price, drivers can easily sort quotes by insurance price. Using brokerage websites will allow drivers to get quotes from multiple insurers, thus making the comparison faster and easier. For additional info, money-saving tips, and free car insurance quotes, visit https://compare-autoinsurance.Org/ Compare-autoinsurance.Org is an online provider of life, home, health, and auto insurance quotes. This website is unique because it does not simply stick to one kind of insurance provider, but brings the clients the best deals from many different online insurance carriers. In this way, clients have access to offers from multiple carriers all in one place: this website. On this site, customers have access to quotes for insurance plans from various agencies, such as local or nationwide agencies, brand names insurance companies, etc. "Online quotes can easily help drivers obtain better car insurance deals. All they have to do is to complete an online form with accurate and real info, then compare prices", said Russell Rabichev, Marketing Director of Internet Marketing Company. CONTACT: Company Name: Internet Marketing CompanyPerson for contact Name: Gurgu CPhone Number: (818) 359-3898Email: [email protected]: https://compare-autoinsurance.Org/ SOURCE: Compare-autoinsurance.Org View source version on accesswire.Com:https://www.Accesswire.Com/595055/What-Are-The-Main-Benefits-Of-Comparing-Car-Insurance-Quotes-Online View photos
to request, modification Contact us at Here or [email protected]
 |
 |
 |
