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2. Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learning

Author

Barnett, Alina Jade, Guo, Zhicheng, Jing, Jin, Ge, Wendong, Kaplan, Peter W., Kong, Wan Yee, Karakis, Ioannis, Herlopian, Aline, Jayagopal, Lakshman Arcot, Taraschenko, Olga, Selioutski, Olga, Osman, Gamaleldin, Goldenholz, Daniel, Rudin, Cynthia, and Westover, M. Brandon

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, I.2.6, I.4.9, I.5.4
Abstract

In intensive care units (ICUs), critically ill patients are monitored with electroencephalograms (EEGs) to prevent serious brain injury. The number of patients who can be monitored is constrained by the availability of trained physicians to read EEGs, and EEG interpretation can be subjective and prone to inter-observer variability. Automated deep learning systems for EEG could reduce human bias and accelerate the diagnostic process. However, black box deep learning models are untrustworthy, difficult to troubleshoot, and lack accountability in real-world applications, leading to a lack of trust and adoption by clinicians. To address these challenges, we propose a novel interpretable deep learning model that not only predicts the presence of harmful brainwave patterns but also provides high-quality case-based explanations of its decisions. Our model performs better than the corresponding black box model, despite being constrained to be interpretable. The learned 2D embedded space provides the first global overview of the structure of ictal-interictal-injury continuum brainwave patterns. The ability to understand how our model arrived at its decisions will not only help clinicians to diagnose and treat harmful brain activities more accurately but also increase their trust and adoption of machine learning models in clinical practice; this could be an integral component of the ICU neurologists' standard workflow., Comment: 24 pages including appendices, 9 figures, published at NEJM AI

Published
2022
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8. The Ultraviolet Index is well estimated by the terrestrial irradiance at 310nm

Author

Kaplan, Peter D. and Dumont, Emmanuel L. P.

Subjects
Physics - Atmospheric and Oceanic Physics
Abstract

Ultraviolet (UV) exposure significantly contributes to non-melanoma skin cancer. In the context of health, UV exposure is the product of time and the UV Index (UVI), a weighted sum of the irradiance I(lambda) over all wavelengths from lambda = 250 to 400nm. In our analysis of the United States Environmental Protection Agency's UV-Net database of over four-hundred thousand spectral irradiance measurements taken over several years, we found that the UVI is well estimated by UVI = 77 I(310nm). To better understand this result, we applied an optical atmospheric model of the terrestrial irradiance spectra and found that it applies across a wide range of conditions., Comment: 3 pages + 4 Figures

Published
2021

13. Improving Clinician Performance in Classifying EEG Patterns on the Ictal–Interictal Injury Continuum Using Interpretable Machine Learning

Author

Barnett, Alina Jade, primary, Guo, Zhicheng, additional, Jing, Jin, additional, Ge, Wendong, additional, Kaplan, Peter W., additional, Kong, Wan Yee, additional, Karakis, Ioannis, additional, Herlopian, Aline, additional, Jayagopal, Lakshman Arcot, additional, Taraschenko, Olga, additional, Selioutski, Olga, additional, Osman, Gamaleldin, additional, Goldenholz, Daniel, additional, Rudin, Cynthia, additional, and Westover, M. Brandon, additional

Published
2024
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17. Deep active learning for interictal ictal injury continuum EEG patterns

Author

Ge, Wendong, Jing, Jin, An, Sungtae, Herlopian, Aline, Ng, Marcus, Struck, Aaron F., Appavu, Brian, Johnson, Emily L., Osman, Gamaleldin, Haider, Hiba A., Karakis, Ioannis, Kim, Jennifer A., Halford, Jonathan J., Dhakar, Monica B., Sarkis, Rani A., Swisher, Christa B., Schmitt, Sarah, Lee, Jong Woo, Tabaeizadeh, Mohammad, Rodriguez, Andres, Gaspard, Nicolas, Gilmore, Emily, Herman, Susan T., Kaplan, Peter W., Pathmanathan, Jay, Hong, Shenda, Rosenthal, Eric S., Zafar, Sahar, Sun, Jimeng, and Brandon Westover, M.

Published
2021
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28. Consensus Statement on Continuous EEG in Critically Ill Adults and Children, Part II

Author

Herman, Susan T, Abend, Nicholas S, Bleck, Thomas P, Chapman, Kevin E, Drislane, Frank W, Emerson, Ronald G, Gerard, Elizabeth E, Hahn, Cecil D, Husain, Aatif M, Kaplan, Peter W, LaRoche, Suzette M, Nuwer, Marc R, Quigg, Mark, Riviello, James J, Schmitt, Sarah E, Simmons, Liberty A, Tsuchida, Tammy N, and Hirsch, Lawrence J

Subjects
Neurosciences, Clinical Research, Clinical Trials and Supportive Activities, Generic health relevance, Good Health and Well Being, Adult, Brain Diseases, Child, Critical Care, Critical Illness, Electroencephalography, Female, Humans, Male, Monitoring, Physiologic, EEG, EEG monitoring, Quantitative EEG, Seizure, Nonconvulsive seizure, Status epilepticus, Nonconvulsive status, epilepticus, Intensive care unit, Critical care, Adults, Children, Critical Care Continuous EEG Task Force of the American Clinical Neurophysiology Society, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
Abstract

IntroductionCritical Care Continuous EEG (CCEEG) is a common procedure to monitor brain function in patients with altered mental status in intensive care units. There is significant variability in patient populations undergoing CCEEG and in technical specifications for CCEEG performance.MethodsThe Critical Care Continuous EEG Task Force of the American Clinical Neurophysiology Society developed expert consensus recommendations on the use of CCEEG in critically ill adults and children.RecommendationsThe consensus panel describes the qualifications and responsibilities of CCEEG personnel including neurodiagnostic technologists and interpreting physicians. The panel outlines required equipment for CCEEG, including electrodes, EEG machine and amplifier specifications, equipment for polygraphic data acquisition, EEG and video review machines, central monitoring equipment, and network, remote access, and data storage equipment. The consensus panel also describes how CCEEG should be acquired, reviewed and interpreted. The panel suggests methods for patient selection and triage; initiation of CCEEG; daily maintenance of CCEEG; electrode removal and infection control; quantitative EEG techniques; EEG and behavioral monitoring by non-physician personnel; review, interpretation, and reports; and data storage protocols.ConclusionRecommended qualifications for CCEEG personnel and CCEEG technical specifications will facilitate standardization of this emerging technology.

Published
2015

46. Standardized computer-based organized reporting of EEG: SCORE – Second version

Author

Beniczky, Sándor, Aurlien, Harald, Brøgger, Jan C., Hirsch, Lawrence J., Schomer, Donald L., Trinka, Eugen, Pressler, Ronit M., Wennberg, Richard, Visser, Gerhard H., Eisermann, Monika, Diehl, Beate, Lesser, Ronald P., Kaplan, Peter W., Nguyen The Tich, Sylvie, Lee, Jong Woo, Martins-da-Silva, Antonio, Stefan, Hermann, Neufeld, Miri, Rubboli, Guido, Fabricius, Martin, Gardella, Elena, Terney, Daniella, Meritam, Pirgit, Eichele, Tom, Asano, Eishi, Cox, Fieke, van Emde Boas, Walter, Mameniskiene, Ruta, Marusic, Petr, Zárubová, Jana, Schmitt, Friedhelm C., Rosén, Ingmar, Fuglsang-Frederiksen, Anders, Ikeda, Akio, MacDonald, David B., Terada, Kiyohito, Ugawa, Yoshikazu, Zhou, Dong, and Herman, Susan T.

Published
2017
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47. Integrating Research, Quality Improvement, and Medical Education for Better Handoffs and Safer Care: Disseminating, Adapting, and Implementing the I-PASS Program

Author

Landrigan, Christopher P., Spector, Nancy D., Starmer, Amy J., Sectish, Theodore C., Srivastava, Rajendu, West, Daniel C., Menon, Aravind Ajakumar, Ali, Arshia, Allair, Brenda K., Allen, April D., Almaddah, Nureddin, Alminde, Claire, Alvarado-Little, Wilma, Anson, Elizabeth, Ashland, Michele, Atsatt, Marisa, Aylor, Megan, Baird, Jennifer D., Bale, James F., Jr, Balmer, Dorene, Barber, Aisha, Barton, Kevin, Bates, Kimberly, Beck, Carolyn, Berchelmann, Kathleen, Bhan, Renuka, Bismilla, Zia, Blankenburg, Rebecca L., Boa-Hocbo, Aileen, Bordin-Wosk, Talya, Brooks, Michelle, Calaman, Sharon, Campe, Julie, Campos, Maria Lucia, Chandler, Debra, Cheung, Yvonne, Choudhary, Amanda, Christensen, Eileen, Clark, Katherine, Coffey, Maitreya, Coghlan-McDonald, Sally, Cohen, Ellen, Cole, F. Sessions, Corless, Elizabeth, Cray, Sharon, Da Silva, Roxi, Dahale, Devesh, Dalal, Anuj K., Destino, Lauren A., Doroshow, Jonathan, Dreyer, Benard, duPont, Katharine, Eagle, Steven, Edgar-Zarate, Courtney, Etzenhouser, Angie, Everhart, Jennifer L., Feraco, Angela M., Ferrer, Alexandra, Galardy, Paul, Garcia, Briana M., Gaspar-Oishi, Maria, Goldstein, Jenna, Good, Brian P., Graham, Dionne A, Growdon, Amanda S., Gubler, LeAnn, Guiot, Amy, Hanlon, Charin, Hanna-Attisha, Mona, Harris, Roben, Haskell, Helen, Hecht, Melvyn, Hehn, Rebecca S, Held, Justin, Hepps, Jennifer H., Hillier, Debra, Hiraoka, Mark, Howell, Eric, Hrach, Christine, Hughes, Helen, Huskins, Charles, Kaatz, Scott, Kanala, Vishnu, Kantrowitz, Michael, Kaplan, Peter, Kashiwagi, Deanne, Kaul, Rajat, Keohane, Carol A., Kern, Jeremy, Khan, Alisa, Khan, Nazia Naz S., Kitch, Barry, Kocolas, Irene, Kothari, Lara, Kruvand, Elizabeth, Kuzma, Nicholas, Lacy, Mary, Lane, Michele, Langrish, Kate, Ledford, Christy J.W., LeRoux, Laura, Lewis, Kheyandra, Lipsitz, Stuart, Litterer, Katherine P., Lopreiato, Joseph O., Lyons, Aimee, Mack, Avram, Mallouk, Meghan, Maloney, Christopher G., Mangan, Amanda, Mann, Keith, Markle, Peggy, Marrese, Christine, Marseille, David, Maynard, Greg, Mehta, Bijal, Mendoza, Fernando, Mercer, Alexandra N., Micalizzi, Dale, Mims, Lisa, Mittal, Vineeta, Mueller, Stephanie, Ngo, Thuy L., Nolan, Ann, Nyenpan, Christopher, Obermeyer, Maria, O'Donnell, Katherine, O'Toole, Jennifer K., Ottolini, Mary, Owolabi, Mobola, Patel, Aarti, Patel, Rajesh, Patel, Shilpa J., Perron, Catherine, Pickler, Rita, Popa, Alina, Reppert, Patrick, Riss, Robert, Roesch, Justin, Rogers, Jayne E., Rosenbluth, Glenn, Rothschild, Jeffrey M., Sanders, Lee, Sauder, Kimberly, Schnipper, Jeff, Schnock, Kumiko O, Scholtz, Amy, Seltz, Barry, Serra, Theresa, Serwint, Janet, Shah, Samir, Sharma, Meesha, Sheppard, Kathleen, Simpkin, Arabella, Sloan, Karin A, Solan, Lauren G., Southgate, Michael W, Spackman, Jaime Blank, Stevens, Scott M., Stevenson, Adam, Stevenson, Windy, Subramony, Anupama, Surkis, William, Thangarasu, Sudhagar, Thompson, E. Douglas, Toole, Cheryl, Trueman, Laura, Trujillo, Tanner, Tse, Lisa, Turmelle, Michael P., Vaniawala, Vishwas, Wagner, Tamara, Warnick, Cindy, Webster, John, Webster, Mary, Weinerman, Adina, Welch, Chelsea, White, Andrew J., Wien, Matthew F., Winn, Ariel S., Wintch, Stephanie, Yin, H. Shonna, Yoon, Catherine S., Yu, Clifton E., Zampino, Dominick, and Zigmont, Katherine R.

Published
2017
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48. American Clinical Neurophysiology Societyʼs Standardized Critical Care EEG Terminology: 2021 Version

Author

Hirsch, Lawrence J., Fong, Michael W.K., Leitinger, Markus, LaRoche, Suzette M., Beniczky, Sandor, Abend, Nicholas S., Lee, Jong Woo, Wusthoff, Courtney J., Hahn, Cecil D., Westover, M. Brandon, Gerard, Elizabeth E., Herman, Susan T., Haider, Hiba Arif, Osman, Gamaleldin, Rodriguez-Ruiz, Andres, Maciel, Carolina B., Gilmore, Emily J., Fernandez, Andres, Rosenthal, Eric S., Claassen, Jan, Husain, Aatif M., Yoo, Ji Yeoun, So, Elson L., Kaplan, Peter W., Nuwer, Marc R., van Putten, Michel, Sutter, Raoul, Drislane, Frank W., Trinka, Eugen, and Gaspard, Nicolas

Published
2021
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49. Electroencephalography in encephalopathy and encephalitis.

Author

Morris, Hollie, Kaplan, Peter W., and Kane, Nick

Subjects
*DIAGNOSIS of brain diseases, *ENCEPHALITIS diagnosis, *CREUTZFELDT-Jakob disease, *CEREBRAL anoxia-ischemia, *ELECTROENCEPHALOGRAPHY, *IMMUNOTHERAPY, *STATUS epilepticus, *NEUROLOGICAL disorders, *HEPATIC encephalopathy, *AUTOIMMUNE diseases, *SEIZURES (Medicine), *VIRAL encephalitis, *PHENOTYPES
Abstract

Electroencephalography (EEG) is a useful adjunct to clinical neurological examination, particularly as it may detect subtle or subclinical disturbance of cerebral function and it allows monitoring of cerebral activity over time. Continuous EEG combined with quantitative analysis and machine learning may help identify changes in real time, before the emergence of clinical signs and response to interventions. EEG is rarely pathognomonic in encephalopathy/encephalitis but when interpreted correctly and within the clinical context, certain phenotypes may indicate a specific pathophysiology (eg, lateralised periodic discharges in HSV-1, generalised periodic discharges in sporadic Creutzfeldt-Jakob disease, and extreme delta brushes in anti-n-methyl-D-aspartate receptor autoimmune encephalitis). EEG is included in some specialist guidelines for disease assessment, monitoring and prognostication (ie, hepatic, cancer immunotherapy, viral, prion, autoimmune encephalitis and hypoxic ischaemic encephalopathy). EEG is invaluable for confirming or excluding non-convulsive seizures or status epilepticus, particularly in critically ill patients, and in understanding new concepts such as epileptic encephalopathy and the ictal-interictal continuum. [ABSTRACT FROM AUTHOR]

Published
2024
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