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Barfuss JD, Nascimento FA, Duhaime E, Kapur S, Karakis I, Ng M, Herlopian A, Lam A, Maus D, Halford JJ, Cash S, Brandon Westover M, Jing J. On-demand EEG education through competition - A novel, app-based approach to learning to identify interictal epileptiform discharges. Clin Neurophysiol Pract 2023; 8:177-186. [PMID: 37681118 PMCID: PMC10480673 DOI: 10.1016/j.cnp.2023.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 09/09/2023] Open
Abstract
Objective Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG. Methods We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings. Results Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice. Conclusions Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback. Significance This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.
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Affiliation(s)
- Jaden D. Barfuss
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fábio A. Nascimento
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | - Ioannis Karakis
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Marcus Ng
- Section of Neurology, Department of Internal Medicine, Health Sciences Centre, University of Manitoba, Winnipeg, MB, Canada
| | - Aline Herlopian
- Division of Epilepsy, Department of Neurology, Yale University, New Haven, CT, USA
| | - Alice Lam
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Sydney Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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2
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Harid NM, Jing J, Hogan J, Nascimento FA, Ouyang A, Zheng WL, Ge W, Zafar SF, Kim JA, Lam AD, Herlopian A, Maus D, Karakis I, Ng M, Hong S, Zhu Y, Kaplan PW, Cash S, Shafi M, Martz G, Halford JJ, Westover MB. Measuring expertise in identifying interictal epileptiform discharges. Epileptic Disord 2022; 24:496-506. [PMID: 35770748 PMCID: PMC9340812 DOI: 10.1684/epd.2021.1409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 09/08/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVE Interictal epileptiform discharges on EEG are integral to diagnosing epilepsy. However, EEGs are interpreted by readers with and without specialty training, and there is no accepted method to assess skill in interpretation. We aimed to develop a test to quantify IED recognition skills. METHODS A total of 13,262 candidate IEDs were selected from EEGs and scored by eight fellowship-trained reviewers to establish a gold standard. An online test was developed to assess how well readers with different training levels could distinguish candidate waveforms. Sensitivity, false positive rate and calibration were calculated for each reader. A simple mathematical model was developed to estimate each reader's skill and threshold in identifying an IED, and to develop receiver operating characteristics curves for each reader. We investigated the number of IEDs needed to measure skill level with acceptable precision. RESULTS Twenty-nine raters completed the test; nine experts, seven experienced non-experts and thirteen novices. Median calibration errors for experts, experienced non-experts and novices were -0.056, 0.012, 0.046; median sensitivities were 0.800, 0.811, 0.715; and median false positive rates were 0.177, 0.272, 0.396, respectively. The number of test questions needed to measure those scores was 549. Our analysis identified that novices had a higher noise level (uncertainty) compared to experienced non-experts and experts. Using calculated noise and threshold levels, receiver operating curves were created, showing increasing median area under the curve from novices (0.735), to experienced non-experts (0.852) and experts (0.891). SIGNIFICANCE Expert and non-expert readers can be distinguished based on ability to identify IEDs. This type of assessment could also be used to identify and correct differences in thresholds in identifying IEDs.
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Affiliation(s)
- Nitish M. Harid
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Jacob Hogan
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | | | - An Ouyang
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Wei-Long Zheng
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Sahar F. Zafar
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Jennifer A. Kim
- Department of Neurology, Yale School of Medicine, New Haven CT, USA
| | - Alice D. Lam
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Aline Herlopian
- Department of Neurology, Yale School of Medicine, New Haven CT, USA
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Ioannis Karakis
- Department of Neurology, Emory University School of Medicine, Atlanta GA, USA
| | - Marcus Ng
- Department of Neurology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing China
| | - Yu Zhu
- Xuanwu Hospital, Capital Medical University, Beijing China
| | - Peter W. Kaplan
- Department of Neurology, Johns Hopkins University School of Medicine, Bayview Medical Center, Baltimore, MD, USA
| | - Sydney Cash
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Mouhsin Shafi
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Gabriel Martz
- Department of Neurology, Hartford HealthCare Medical Group at Hartford Hospital, CT, USA
| | - Jonathan J. Halford
- Department of Neurology, Medical University of South Carolina, Charleston SC, USA
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3
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Yang JC, Paulk AC, Salami P, Lee SH, Ganji M, Soper DJ, Cleary D, Simon M, Maus D, Lee JW, Nahed BV, Jones PS, Cahill DP, Cosgrove GR, Chu CJ, Williams Z, Halgren E, Dayeh S, Cash SS. Microscale dynamics of electrophysiological markers of epilepsy. Clin Neurophysiol 2021; 132:2916-2931. [PMID: 34419344 DOI: 10.1016/j.clinph.2021.06.024] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 06/22/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Interictal discharges (IIDs) and high frequency oscillations (HFOs) are established neurophysiologic biomarkers of epilepsy, while microseizures are less well studied. We used custom poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) microelectrodes to better understand these markers' microscale spatial dynamics. METHODS Electrodes with spatial resolution down to 50 µm were used to record intraoperatively in 30 subjects. IIDs' degree of spread and spatiotemporal paths were generated by peak-tracking followed by clustering. Repeating HFO patterns were delineated by clustering similar time windows. Multi-unit activity (MUA) was analyzed in relation to IID and HFO timing. RESULTS We detected IIDs encompassing the entire array in 93% of subjects, while localized IIDs, observed across < 50% of channels, were seen in 53%. IIDs traveled along specific paths. HFOs appeared in small, repeated spatiotemporal patterns. Finally, we identified microseizure events that spanned 50-100 µm. HFOs covaried with MUA, but not with IIDs. CONCLUSIONS Overall, these data suggest that irritable cortex micro-domains may form part of an underlying pathologic architecture which could contribute to the seizure network. SIGNIFICANCE These results, supporting the possibility that epileptogenic cortex comprises a mosaic of irritable domains, suggests that microscale approaches might be an important perspective in devising novel seizure control therapies.
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Affiliation(s)
- Jimmy C Yang
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA; Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Pariya Salami
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Sang Heon Lee
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Mehran Ganji
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Daniel J Soper
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Daniel Cleary
- Department of Neurosurgery, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Mirela Simon
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd., Boston, MA 02115, USA
| | - Brian V Nahed
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Pamela S Jones
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Garth Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, 60 Fenwood Rd., Boston, MA 02115, USA
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Ziv Williams
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Eric Halgren
- Department of Radiology, University of California, San Diego; 9500 Gilman Dr.; La Jolla, CA 92093, USA
| | - Shadi Dayeh
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA.
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4
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Paulk AC, Yang JC, Cleary DR, Soper DJ, Halgren M, O’Donnell AR, Lee SH, Ganji M, Ro YG, Oh H, Hossain L, Lee J, Tchoe Y, Rogers N, Kiliç K, Ryu SB, Lee SW, Hermiz J, Gilja V, Ulbert I, Fabó D, Thesen T, Doyle WK, Devinsky O, Madsen JR, Schomer DL, Eskandar EN, Lee JW, Maus D, Devor A, Fried SI, Jones PS, Nahed BV, Ben-Haim S, Bick SK, Richardson RM, Raslan AM, Siler DA, Cahill DP, Williams ZM, Cosgrove GR, Dayeh SA, Cash SS. Microscale Physiological Events on the Human Cortical Surface. Cereb Cortex 2021; 31:3678-3700. [PMID: 33749727 PMCID: PMC8258438 DOI: 10.1093/cercor/bhab040] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 02/05/2021] [Accepted: 02/07/2021] [Indexed: 01/14/2023] Open
Abstract
Despite ongoing advances in our understanding of local single-cellular and network-level activity of neuronal populations in the human brain, extraordinarily little is known about their "intermediate" microscale local circuit dynamics. Here, we utilized ultra-high-density microelectrode arrays and a rare opportunity to perform intracranial recordings across multiple cortical areas in human participants to discover three distinct classes of cortical activity that are not locked to ongoing natural brain rhythmic activity. The first included fast waveforms similar to extracellular single-unit activity. The other two types were discrete events with slower waveform dynamics and were found preferentially in upper cortical layers. These second and third types were also observed in rodents, nonhuman primates, and semi-chronic recordings from humans via laminar and Utah array microelectrodes. The rates of all three events were selectively modulated by auditory and electrical stimuli, pharmacological manipulation, and cold saline application and had small causal co-occurrences. These results suggest that the proper combination of high-resolution microelectrodes and analytic techniques can capture neuronal dynamics that lay between somatic action potentials and aggregate population activity. Understanding intermediate microscale dynamics in relation to single-cell and network dynamics may reveal important details about activity in the full cortical circuit.
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Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jimmy C Yang
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Daniel R Cleary
- Departments of Neurosciences and Radiology, University of California San Diego, La Jolla, CA 92093, USA
- Department of Physics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Neurosurgery, University of California San Diego, La Jolla, CA 92093, USA
| | - Daniel J Soper
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Mila Halgren
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Sang Heon Lee
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Mehran Ganji
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Yun Goo Ro
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Hongseok Oh
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Lorraine Hossain
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Jihwan Lee
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Youngbin Tchoe
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Nicholas Rogers
- Department of Physics, University of California San Diego, La Jolla, CA 92093, USA
| | - Kivilcim Kiliç
- Departments of Neurosciences and Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Sang Baek Ryu
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Seung Woo Lee
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - John Hermiz
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Vikash Gilja
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - István Ulbert
- Research Centre for Natural Sciences, Institute of Cognitive Neuroscience and Psychology, 1519 Budapest, Hungary
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, H-1444 Budapest, Hungary
| | - Daniel Fabó
- Epilepsy Centrum, National Institute of Clinical Neurosciences, 1145 Budapest, Hungary
| | - Thomas Thesen
- Department of Biomedical Sciences, University of Houston College of Medicine, Houston, TX 77204, USA
- Comprehensive Epilepsy Center, New York University School of Medicine, New York City, NY 10016, USA
| | - Werner K Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, New York City, NY 10016, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, New York City, NY 10016, USA
| | - Joseph R Madsen
- Departments of Neurosurgery, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Donald L Schomer
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Emad N Eskandar
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
- Albert Einstein College of Medicine, Montefiore Medical Center, Department of Neurosurgery, Bronx, NY 10467, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Anna Devor
- Departments of Neurosciences and Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Shelley I Fried
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
- Boston VA Healthcare System, 150 South Huntington Avenue, Boston, MA 02130, USA
| | - Pamela S Jones
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Brian V Nahed
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Sharona Ben-Haim
- Department of Neurosurgery, University of California San Diego, La Jolla, CA 92093, USA
| | - Sarah K Bick
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Ahmed M Raslan
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Dominic A Siler
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Shadi A Dayeh
- Department of Neurosurgery, University of California San Diego, La Jolla, CA 92093, USA
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA 92093, USA
- Department of Nanoengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
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5
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Jing J, Sun H, Kim JA, Herlopian A, Karakis I, Ng M, Halford JJ, Maus D, Chan F, Dolatshahi M, Muniz C, Chu C, Sacca V, Pathmanathan J, Ge W, Dauwels J, Lam A, Cole AJ, Cash SS, Westover MB. Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation. JAMA Neurol 2020; 77:103-108. [PMID: 31633740 DOI: 10.1001/jamaneurol.2019.3485] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Importance Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability. Objective To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs. Design, Setting, and Participants A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built. Main Outcomes and Measures SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation. Results SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865). Conclusions and Relevance In this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.
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Affiliation(s)
- Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts.,School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Jennifer A Kim
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Aline Herlopian
- Department of Neurology, Yale University School of Medicine, New Haven, Connecticut
| | - Ioannis Karakis
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia
| | - Marcus Ng
- Department of Neurology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Jonathan J Halford
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Fonda Chan
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Marjan Dolatshahi
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Carlos Muniz
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Catherine Chu
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Valeria Sacca
- Department of Medical and Surgical Sciences, University Magna Graecia, Catanzaro, Italy
| | - Jay Pathmanathan
- Department of Neurology, University of Pennsylvania General Hospital, Boston, Massachusetts
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Justin Dauwels
- School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore
| | - Alice Lam
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Andrew J Cole
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
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6
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Jing J, Herlopian A, Karakis I, Ng M, Halford JJ, Lam A, Maus D, Chan F, Dolatshahi M, Muniz CF, Chu C, Sacca V, Pathmanathan J, Ge W, Sun H, Dauwels J, Cole AJ, Hoch DB, Cash SS, Westover MB. Interrater Reliability of Experts in Identifying Interictal Epileptiform Discharges in Electroencephalograms. JAMA Neurol 2020; 77:49-57. [PMID: 31633742 DOI: 10.1001/jamaneurol.2019.3531] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance The validity of using electroencephalograms (EEGs) to diagnose epilepsy requires reliable detection of interictal epileptiform discharges (IEDs). Prior interrater reliability (IRR) studies are limited by small samples and selection bias. Objective To assess the reliability of experts in detecting IEDs in routine EEGs. Design, Setting, and Participants This prospective analysis conducted in 2 phases included as participants physicians with at least 1 year of subspecialty training in clinical neurophysiology. In phase 1, 9 experts independently identified candidate IEDs in 991 EEGs (1 expert per EEG) reported in the medical record to contain at least 1 IED, yielding 87 636 candidate IEDs. In phase 2, the candidate IEDs were clustered into groups with distinct morphological features, yielding 12 602 clusters, and a representative candidate IED was selected from each cluster. We added 660 waveforms (11 random samples each from 60 randomly selected EEGs reported as being free of IEDs) as negative controls. Eight experts independently scored all 13 262 candidates as IEDs or non-IEDs. The 1051 EEGs in the study were recorded at the Massachusetts General Hospital between 2012 and 2016. Main Outcomes and Measures Primary outcome measures were percentage of agreement (PA) and beyond-chance agreement (Gwet κ) for individual IEDs (IED-wise IRR) and for whether an EEG contained any IEDs (EEG-wise IRR). Secondary outcomes were the correlations between numbers of IEDs marked by experts across cases, calibration of expert scoring to group consensus, and receiver operating characteristic analysis of how well multivariate logistic regression models may account for differences in the IED scoring behavior between experts. Results Among the 1051 EEGs assessed in the study, 540 (51.4%) were those of females and 511 (48.6%) were those of males. In phase 1, 9 experts each marked potential IEDs in a median of 65 (interquartile range [IQR], 28-332) EEGs. The total number of IED candidates marked was 87 636. Expert IRR for the 13 262 individually annotated IED candidates was fair, with the mean PA being 72.4% (95% CI, 67.0%-77.8%) and mean κ being 48.7% (95% CI, 37.3%-60.1%). The EEG-wise IRR was substantial, with the mean PA being 80.9% (95% CI, 76.2%-85.7%) and mean κ being 69.4% (95% CI, 60.3%-78.5%). A statistical model based on waveform morphological features, when provided with individualized thresholds, explained the median binary scores of all experts with a high degree of accuracy of 80% (range, 73%-88%). Conclusions and Relevance This study's findings suggest that experts can identify whether EEGs contain IEDs with substantial reliability. Lower reliability regarding individual IEDs may be largely explained by various experts applying different thresholds to a common underlying statistical model.
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Affiliation(s)
- Jin Jing
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston.,School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore
| | - Aline Herlopian
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston.,Department of Neurology, Yale School of Medicine, New Haven, Connecticut
| | - Ioannis Karakis
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia
| | - Marcus Ng
- Department of Neurology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Jonathan J Halford
- Department of Neurology, Medical University of South Carolina, Charleston
| | - Alice Lam
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
| | - Douglas Maus
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
| | - Fonda Chan
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
| | - Marjan Dolatshahi
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
| | - Carlos F Muniz
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
| | - Catherine Chu
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
| | - Valeria Sacca
- Department of Neurology, Department of Medical and Surgical Sciences, University "Magna Graecia" of Catanzaro, Italy
| | - Jay Pathmanathan
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston.,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia
| | - WenDong Ge
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
| | - Haoqi Sun
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
| | - Justin Dauwels
- School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore
| | - Andrew J Cole
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
| | - Daniel B Hoch
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
| | - Sydney S Cash
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
| | - M Brandon Westover
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston
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7
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Choi BD, Lee DK, Yang JC, Ayinon CM, Lee CK, Maus D, Carter BS, Barker FG, Jones PS, Nahed BV, Cahill DP, See RB, Simon MV, Curry WT. Receptor tyrosine kinase gene amplification is predictive of intraoperative seizures during glioma resection with functional mapping. J Neurosurg 2019; 132:1017-1023. [PMID: 30925466 DOI: 10.3171/2018.12.jns182700] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 12/26/2018] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Intraoperative seizures during craniotomy with functional mapping is a common complication that impedes optimal tumor resection and results in significant morbidity. The relationship between genetic mutations in gliomas and the incidence of intraoperative seizures has not been well characterized. Here, the authors performed a retrospective study of patients treated at their institution over the last 12 years to determine whether molecular data can be used to predict the incidence of this complication. METHODS The authors queried their institutional database for patients with brain tumors who underwent resection with intraoperative functional mapping between 2005 and 2017. Basic clinicopathological characteristics, including the status of the following genes, were recorded: IDH1/2, PIK3CA, BRAF, KRAS, AKT1, EGFR, PDGFRA, MET, MGMT, and 1p/19q. Relationships between gene alterations and intraoperative seizures were evaluated using chi-square and two-sample t-test univariate analysis. When considering multiple predictive factors, a logistic multivariate approach was taken. RESULTS Overall, 416 patients met criteria for inclusion; of these patients, 98 (24%) experienced an intraoperative seizure. Patients with a history of preoperative seizure and those treated with antiepileptic drugs prior to surgery were less likely to have intraoperative seizures (history: OR 0.61 [95% CI 0.38-0.96], chi-square = 4.65, p = 0.03; AED load: OR 0.46 [95% CI 0.26-0.80], chi-square = 7.64, p = 0.01). In a univariate analysis of genetic markers, amplification of genes encoding receptor tyrosine kinases (RTKs) was specifically identified as a positive predictor of seizures (OR 5.47 [95% CI 1.22-24.47], chi-square = 5.98, p = 0.01). In multivariate analyses considering RTK status, AED use, and either 2007 WHO tumor grade or modern 2016 WHO tumor groups, the authors found that amplification of the RTK proto-oncogene, MET, was most predictive of intraoperative seizure (p < 0.05). CONCLUSIONS This study describes a previously unreported association between genetic alterations in RTKs and the occurrence of intraoperative seizures during glioma resection with functional mapping. Future models estimating intraoperative seizure risk may be enhanced by inclusion of genetic criteria.
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Affiliation(s)
| | | | | | | | | | - Douglas Maus
- 2Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | | | | | | | | | | | - Reiner B See
- 2Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Mirela V Simon
- 2Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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8
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Lam AD, Maus D, Zafar SF, Cole AJ, Cash SS. SCOPE-mTL: A non-invasive tool for identifying and lateralizing mesial temporal lobe seizures prior to scalp EEG ictal onset. Clin Neurophysiol 2017; 128:1647-1655. [PMID: 28732342 DOI: 10.1016/j.clinph.2017.06.040] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 06/01/2017] [Accepted: 06/14/2017] [Indexed: 10/19/2022]
Abstract
OBJECTIVE In mesial temporal lobe (mTL) epilepsy, seizure onset can precede the appearance of a scalp EEG ictal pattern by many seconds. The ability to identify this early, occult mTL seizure activity could improve lateralization and localization of mTL seizures on scalp EEG. METHODS Using scalp EEG spectral features and machine learning approaches on a dataset of combined scalp EEG and foramen ovale electrode recordings in patients with mTL epilepsy, we developed an algorithm, SCOPE-mTL, to detect and lateralize early, occult mTL seizure activity, prior to the appearance of a scalp EEG ictal pattern. RESULTS Using SCOPE-mTL, 73% of seizures with occult mTL onset were identified as such, and no seizures that lacked an occult mTL onset were identified as having one. Predicted mTL seizure onset times were highly correlated with actual mTL seizure onset times (r=0.69). 50% of seizures with early mTL onset were lateralizable prior to scalp ictal onset, with 94% accuracy. CONCLUSIONS SCOPE-mTL can identify and lateralize mTL seizures prior to scalp EEG ictal onset, with high sensitivity, specificity, and accuracy. SIGNIFICANCE Quantitative analysis of scalp EEG can provide important information about mTL seizures, even in the absence of a visible scalp EEG ictal correlate.
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Affiliation(s)
- Alice D Lam
- Epilepsy Division, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Douglas Maus
- Epilepsy Division, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sahar F Zafar
- Epilepsy Division, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew J Cole
- Epilepsy Division, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sydney S Cash
- Epilepsy Division, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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9
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Viotto WH, Maus D. 0528 Hydrolysis of phosphates with a different chain length in water, milk and calcium caseinate. J Anim Sci 2016. [DOI: 10.2527/jam2016-0528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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10
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Grant AC, Abdel-Baki SG, Weedon J, Arnedo V, Chari G, Koziorynska E, Lushbough C, Maus D, McSween T, Mortati KA, Reznikov A, Omurtag A. EEG interpretation reliability and interpreter confidence: a large single-center study. Epilepsy Behav 2014; 32:102-7. [PMID: 24531133 PMCID: PMC3965251 DOI: 10.1016/j.yebeh.2014.01.011] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Revised: 01/16/2014] [Accepted: 01/20/2014] [Indexed: 10/25/2022]
Abstract
The intrarater and interrater reliability (I&IR) of EEG interpretation has significant implications for the value of EEG as a diagnostic tool. We measured both the intrarater reliability and the interrater reliability of EEG interpretation based on the interpretation of complete EEGs into standard diagnostic categories and rater confidence in their interpretations and investigated sources of variance in EEG interpretations. During two distinct time intervals, six board-certified clinical neurophysiologists classified 300 EEGs into one or more of seven diagnostic categories and assigned a subjective confidence to their interpretations. Each EEG was read by three readers. Each reader interpreted 150 unique studies, and 50 studies were re-interpreted to generate intrarater data. A generalizability study assessed the contribution of subjects, readers, and the interaction between subjects and readers to interpretation variance. Five of the six readers had a median confidence of ≥99%, and the upper quartile of confidence values was 100% for all six readers. Intrarater Cohen's kappa (κc) ranged from 0.33 to 0.73 with an aggregated value of 0.59. Cohen's kappa ranged from 0.29 to 0.62 for the 15 reader pairs, with an aggregated Fleiss kappa of 0.44 for interrater agreement. Cohen's kappa was not significantly different across rater pairs (chi-square=17.3, df=14, p=0.24). Variance due to subjects (i.e., EEGs) was 65.3%, due to readers was 3.9%, and due to the interaction between readers and subjects was 30.8%. Experienced epileptologists have very high confidence in their EEG interpretations and low to moderate I&IR, a common paradox in clinical medicine. A necessary, but insufficient, condition to improve EEG interpretation accuracy is to increase intrarater and interrater reliability. This goal could be accomplished, for instance, with an automated online application integrated into a continuing medical education module that measures and reports EEG I&IR to individual users.
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Affiliation(s)
- Arthur C. Grant
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, USA,Department of Physiology & Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY, USA,To whom correspondence should be addressed at: SUNY Downstate Medical Center, Comprehensive Epilepsy Center, 450 Clarkson Ave., Box 1275, Brooklyn, NY 11203, 718.270.2959 (tel), 718.270.4711 (fax),
| | | | - Jeremy Weedon
- The Scientific Computing Center, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Vanessa Arnedo
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Geetha Chari
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Ewa Koziorynska
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | | | - Douglas Maus
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, USA,Department of Physiology & Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Tresa McSween
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | | | - Alexandra Reznikov
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Ahmet Omurtag
- BioSignal Group, Corp. Brooklyn, NY, USA,Department of Biomedical Engineering, University of Houston, Houston, TX, USA
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11
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Smart O, Maus D, Marsh E, Dlugos D, Litt B, Meador K. Mapping and mining interictal pathological gamma (30-100 Hz) oscillations with clinical intracranial EEG in patients with epilepsy. Expert Syst Appl 2012; 39:7355-7370. [PMID: 23105174 PMCID: PMC3480232 DOI: 10.1016/j.eswa.2012.01.071] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Localizing an epileptic network is essential for guiding neurosurgery and antiepileptic medical devices as well as elucidating mechanisms that may explain seizure-generation and epilepsy. There is increasing evidence that pathological oscillations may be specific to diseased networks in patients with epilepsy and that these oscillations may be a key biomarker for generating and indentifying epileptic networks. We present a semi-automated method that detects, maps, and mines pathological gamma (30-100 Hz) oscillations (PGOs) in human epileptic brain to possibly localize epileptic networks. We apply the method to standard clinical iEEG (<100 Hz) with interictal PGOs and seizures from six patients with medically refractory epilepsy. We demonstrate that electrodes with consistent PGO discharges do not always coincide with clinically determined seizure onset zone (SOZ) electrodes but at times PGO-dense electrodes include secondary seizure-areas (SS) or even areas without seizures (NS). In 4/5 patients with epilepsy surgery, we observe poor (Engel Class 4) post-surgical outcomes and identify more PGO-activity in SS or NS than in SOZ. Additional studies are needed to further clarify the role of PGOs in epileptic brain.
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Affiliation(s)
- Otis Smart
- Intelligent Control Systems Laboratory, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Department of Neurosurgery, Emory University, Atlanta, GA 30322, USA
| | - Douglas Maus
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA
| | - Eric Marsh
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Dennis Dlugos
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Brian Litt
- Departments of Neurology and Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kimford Meador
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA
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12
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Blanco JA, Stead M, Krieger A, Stacey W, Maus D, Marsh E, Viventi J, Lee KH, Marsh R, Litt B, Worrell GA. Data mining neocortical high-frequency oscillations in epilepsy and controls. Brain 2011; 134:2948-59. [PMID: 21903727 DOI: 10.1093/brain/awr212] [Citation(s) in RCA: 114] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Transient high-frequency (100-500 Hz) oscillations of the local field potential have been studied extensively in human mesial temporal lobe. Previous studies report that both ripple (100-250 Hz) and fast ripple (250-500 Hz) oscillations are increased in the seizure-onset zone of patients with mesial temporal lobe epilepsy. Comparatively little is known, however, about their spatial distribution with respect to seizure-onset zone in neocortical epilepsy, or their prevalence in normal brain. We present a quantitative analysis of high-frequency oscillations and their rates of occurrence in a group of nine patients with neocortical epilepsy and two control patients with no history of seizures. Oscillations were automatically detected and classified using an unsupervised approach in a data set of unprecedented volume in epilepsy research, over 12 terabytes of continuous long-term micro- and macro-electrode intracranial recordings, without human preprocessing, enabling selection-bias-free estimates of oscillation rates. There are three main results: (i) a cluster of ripple frequency oscillations with median spectral centroid = 137 Hz is increased in the seizure-onset zone more frequently than a cluster of fast ripple frequency oscillations (median spectral centroid = 305 Hz); (ii) we found no difference in the rates of high frequency oscillations in control neocortex and the non-seizure-onset zone neocortex of patients with epilepsy, despite the possibility of different underlying mechanisms of generation; and (iii) while previous studies have demonstrated that oscillations recorded by parenchyma-penetrating micro-electrodes have higher peak 100-500 Hz frequencies than penetrating macro-electrodes, this was not found for the epipial electrodes used here to record from the neocortical surface. We conclude that the relative rate of ripple frequency oscillations is a potential biomarker for epileptic neocortex, but that larger prospective studies correlating high-frequency oscillations rates with seizure-onset zone, resected tissue and surgical outcome are required to determine the true predictive value.
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Affiliation(s)
- Justin A Blanco
- Department of Electrical and Computer Engineering, United States Naval Academy, Annapolis, MD 21402, USA.
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13
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Davis KA, Cantor C, Maus D, Herman ST. A neurological cause of recurrent choking during sleep. J Clin Sleep Med 2008; 4:586-587. [PMID: 19110889 PMCID: PMC2603537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
We describe a case of nocturnal choking episodes caused by insular seizures. Recurrent choking spells from sleep showed no response to treatment for sleep apnea or gastroesophageal reflux. Laryngoscopy revealed no abnormalities. Although continuous EEG monitoring during events was normal, ictal SPECT imaging showed increased radiotracer uptake in the left insular region, an area involved in sensation of the upper gastrointestinal tract. The episodes remitted after initiation of an antiepileptic drug. Obstructive sleep apnea is the most common cause for presentation to a sleep center, but seizures should remain in the differential diagnosis of nocturnal choking episodes.
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Affiliation(s)
- Kathryn A Davis
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
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14
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Affiliation(s)
- Kathryn A. Davis
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - Charles Cantor
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - Douglas Maus
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - Susan T. Herman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
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15
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Cucchiara BL, Messe SR, Taylor RA, Pacelli J, Maus D, Shah Q, Kasner SE. Is the ABCD Score Useful for Risk Stratification of Patients With Acute Transient Ischemic Attack? Stroke 2006; 37:1710-4. [PMID: 16763186 DOI: 10.1161/01.str.0000227195.46336.93] [Citation(s) in RCA: 102] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background and Purpose—
A 6-point scoring system (ABCD) was described recently for stratifying risk after transient ischemic attack (TIA). This score incorporates age (A), blood pressure (B), clinical features (C), and duration (D) of TIA. A score <4 reportedly indicates minimal short-term stroke risk. We evaluated this scoring system in an independent population.
Methods—
This was a prospective study of TIA patients (diagnosed by a neurologist using the classic <24-hour definition) hospitalized <48 hours from symptom onset. The primary outcome assessment consisted of dichotomization of patients into 2 groups. The high-risk group included patients with stroke or death within 90 days, ≥50% stenosis in a relevant artery, or a cardioembolic source warranting anticoagulation. All others were classified as low risk. Findings on diffusion-weighted MRI (DWI) were also evaluated when performed and patients classified as DWI+ or DWI−.
Results—
Over 3 years, 117 patients were enrolled. Median time from symptom onset to enrollment was 25.2 hours (interquartile range 19.8 to 30.2). Overall, 26 patients (22%) were classified as high risk, including 2 strokes, 2 deaths, 15 with ≥50% stenosis, and 10 with cardioembolic source. The frequency of high-risk patients increased with ABCD score (0 to 1 13%; 2 8%; 3 17%; 4 27%; 5 26%; 6 30%;
P
for trend=0.11). ABCD scores in the 2 patients with stroke were 3 and 6. Of those who underwent MRI, 15 of 61 (25%) were DWI+, but this correlated poorly with ABCD score (0 to 1 17%; 2 10%; 3 36%; 4 24%; 5 13%; 6 60%;
P
for trend=0.24).
Conclusions—
Although the ABCD score has some predictive value, patients with a score <4 still have a substantial probability of having a high-risk cause of cerebral ischemia or radiographic evidence of acute infarction despite transient symptoms.
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Affiliation(s)
- Brett L Cucchiara
- Department of Neurology, University of Pennsylvania Medical Center, Philadelphia, PA 19104, USA.
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16
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Maus D. [Sanitary establishments in sports facilities. With reference to smaller units]. Gesund Ing 1971; 92:80-2. [PMID: 5164028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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17
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Maus D. [Pneumatic refuse suction plants]. Gesund Ing 1971; 92:53-7. [PMID: 5164024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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