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van Maren E, Alnes SL, Ramos da Cruz J, Sobolewski A, Friedrichs-Maeder C, Wohler K, Barlatey SL, Feruglio S, Fuchs M, Vlachos I, Zimmermann J, Bertolote T, Z'Graggen WJ, Tzovara A, Donoghue J, Kouvas G, Schindler K, Pollo C, Baud MO. Feasibility, Safety, and Performance of Full-Head Subscalp EEG Using Minimally Invasive Electrode Implantation. Neurology 2024; 102:e209428. [PMID: 38843489 DOI: 10.1212/wnl.0000000000209428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Current practice in clinical neurophysiology is limited to short recordings with conventional EEG (days) that fail to capture a range of brain (dys)functions at longer timescales (months). The future ability to optimally manage chronic brain disorders, such as epilepsy, hinges upon finding methods to monitor electrical brain activity in daily life. We developed a device for full-head subscalp EEG (Epios) and tested here the feasibility to safely insert the electrode leads beneath the scalp by a minimally invasive technique (primary outcome). As secondary outcome, we verified the noninferiority of subscalp EEG in measuring physiologic brain oscillations and pathologic discharges compared with scalp EEG, the established standard of care. METHODS Eight participants with pharmacoresistant epilepsy undergoing intracranial EEG received in the same surgery subscalp electrodes tunneled between the scalp and the skull with custom-made tools. Postoperative safety was monitored on an inpatient ward for up to 9 days. Sleep-wake, ictal, and interictal EEG signals from subscalp, scalp, and intracranial electrodes were compared quantitatively using windowed multitaper transforms and spectral coherence. Noninferiority was tested for pairs of neighboring subscalp and scalp electrodes with a Bland-Altman analysis for measurement bias and calculation of the interclass correlation coefficient (ICC). RESULTS As primary outcome, up to 28 subscalp electrodes could be safely placed over the entire head through 1-cm scalp incisions in a ∼1-hour procedure. Five of 10 observed perioperative adverse events were linked to the investigational procedure, but none were serious, and all resolved. As a secondary outcome, subscalp electrodes advantageously recorded EEG percutaneously without requiring any maintenance and were noninferior to scalp electrodes for measuring (1) variably strong, stage-specific brain oscillations (alpha in wake, delta, sigma, and beta in sleep) and (2) interictal spikes peak-potentials and ictal signals coherent with seizure propagation in different brain regions (ICC >0.8 and absence of bias). DISCUSSION Recording full-head subscalp EEG for localization and monitoring purposes is feasible up to 9 days in humans using minimally invasive techniques and noninferior to the current standard of care. A longer prospective ambulatory study of the full system will be necessary to establish the safety and utility of this innovative approach. TRIAL REGISTRATION INFORMATION clinicaltrials.gov/study/NCT04796597.
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Affiliation(s)
- Ellen van Maren
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Sigurd L Alnes
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Janir Ramos da Cruz
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Aleksander Sobolewski
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Cecilia Friedrichs-Maeder
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Katharina Wohler
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Sabry L Barlatey
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Sandy Feruglio
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Markus Fuchs
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Ioannis Vlachos
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Jonas Zimmermann
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Tiago Bertolote
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Werner J Z'Graggen
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Athina Tzovara
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - John Donoghue
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - George Kouvas
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Kaspar Schindler
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Claudio Pollo
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
| | - Maxime O Baud
- From the NeuroTec (E.v.M., S.L.A., C.F.-M., K.W., S.F., M.F., A.T., K.S., M.O.B.), Center for Sleep-Wake-Epilepsy, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, and Institute of Computer Science (S.L.A., A.T.), University of Bern; Wyss Center for Bio and Neuroengineering (J.R.d.C., A.S., I.V., J.Z., T.B., G.K.), Geneva; Department of Neurosurgery (S.L.B., W.J.Z.G., C.P.), Inselspital Bern, University Hospital, University of Bern, Switzerland; and Department of Neuroscience (J.D.), Brown University, Providence, RI
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Awuah WA, Ahluwalia A, Darko K, Sanker V, Tan JK, Tenkorang PO, Ben-Jaafar A, Ranganathan S, Aderinto N, Mehta A, Shah MH, Lee Boon Chun K, Abdul-Rahman T, Atallah O. Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications. World Neurosurg 2024; 189:138-153. [PMID: 38789029 DOI: 10.1016/j.wneu.2024.05.104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 05/26/2024]
Abstract
Brain-computer interfaces (BCIs), a remarkable technological advancement in neurology and neurosurgery, mark a significant leap since the inception of electroencephalography in 1924. These interfaces effectively convert central nervous system signals into commands for external devices, offering revolutionary benefits to patients with severe communication and motor impairments due to a myriad of neurological conditions like stroke, spinal cord injuries, and neurodegenerative disorders. BCIs enable these individuals to communicate and interact with their environment, using their brain signals to operate interfaces for communication and environmental control. This technology is especially crucial for those completely locked in, providing a communication lifeline where other methods fall short. The advantages of BCIs are profound, offering autonomy and an improved quality of life for patients with severe disabilities. They allow for direct interaction with various devices and prostheses, bypassing damaged or nonfunctional neural pathways. However, challenges persist, including the complexity of accurately interpreting brain signals, the need for individual calibration, and ensuring reliable, long-term use. Additionally, ethical considerations arise regarding autonomy, consent, and the potential for dependence on technology. Despite these challenges, BCIs represent a transformative development in neurotechnology, promising enhanced patient outcomes and a deeper understanding of brain-machine interfaces.
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Affiliation(s)
| | - Arjun Ahluwalia
- School of Medicine, Queen's University Belfast, Belfast, United Kingdom
| | - Kwadwo Darko
- Department of Neurosurgery, Korle Bu Teaching Hospital, Accra, Ghana
| | - Vivek Sanker
- Department of Neurosurgery, Trivandrum Medical College, India
| | - Joecelyn Kirani Tan
- Faculty of Medicine, University of St Andrews, St. Andrews, Scotland, United Kingdom.
| | | | - Adam Ben-Jaafar
- University College Dublin, School of Medicine, Belfield, Dublin, Ireland
| | - Sruthi Ranganathan
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas Aderinto
- Internal Medicine Department, LAUTECH Teaching Hospital, Ogbomoso, Nigeria
| | - Aashna Mehta
- University of Debrecen-Faculty of Medicine, Debrecen, Hungary
| | | | | | | | - Oday Atallah
- Department of Neurosurgery, Hannover Medical School, Hannover, Germany
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Friedrichs-Maeder C, Proix T, Tcheng TK, Skarpaas T, Rao VR, Baud MO. Seizure Cycles under Pharmacotherapy. Ann Neurol 2024; 95:743-753. [PMID: 38379195 DOI: 10.1002/ana.26878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 12/25/2023] [Accepted: 12/31/2023] [Indexed: 02/22/2024]
Abstract
OBJECTIVE This study was undertaken to determine the effects of antiseizure medications (ASMs) on multidien (multiday) cycles of interictal epileptiform activity (IEA) and seizures and evaluate their potential clinical significance. METHODS We retrospectively analyzed up to 10 years of data from 88 of the 256 total adults with pharmacoresistant focal epilepsy who participated in the clinical trials of the RNS System, an intracranial device that keeps records of IEA counts. Following adjunctive ASM trials, we evaluated changes over months in (1) rates of self-reported disabling seizures and (2) multidien IEA cycle strength (spectral power for periodicity between 4 and 40 days). We used a survival analysis and the receiver operating characteristics to assess changes in IEA as a predictor of seizure control. RESULTS Among 56 (33.3%) of the 168 adjunctive ASM trials suitable for analysis, ASM introduction was followed by an average 50 to 70% decrease in multidien IEA cycle strength and a concomitant 50 to 70% decrease in relative seizure rate for up to 12 months. Individuals with a ≥50% decrease in IEA cycle strength in the first 3 months of an ASM trial had a higher probability of remaining seizure responders (≥50% seizure rate reduction, p < 10-7) or super-responders (≥90%, p < 10-8) over the next 12 months. INTERPRETATION In this large cohort, a decrease in multidien IEA cycle strength following initiation of an adjunctive ASM correlated with seizure control for up to 12 months, suggesting that fluctuations in IEA mirror "disease activity" in pharmacoresistant focal epilepsy and may have clinical utility as a biomarker to predict treatment response. ANN NEUROL 2024;95:743-753.
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Affiliation(s)
- Cecilia Friedrichs-Maeder
- Sleep-Wake-Epilepsy Center, NeuroTec, Center for Experimental Neurology, Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Timothée Proix
- Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | | | - Tara Skarpaas
- NeuroPace, Mountain View, California, USA; currently Jazz Pharmaceuticals, Palo Alto, California, USA
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, California, USA
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center, NeuroTec, Center for Experimental Neurology, Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
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Hirsch M, Novitskaya Y, Schulze‐Bonhage A. Value of ultralong-term subcutaneous EEG monitoring for treatment decisions in temporal lobe epilepsy: A case report. Epilepsia Open 2023; 8:1616-1621. [PMID: 37842739 PMCID: PMC10690663 DOI: 10.1002/epi4.12844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 10/09/2023] [Indexed: 10/17/2023] Open
Abstract
Treatment decisions in epilepsy critically depend on information on the course of the disease, its severity and options for specific local interventions. We here report a patient with pharmaco-resistant non-lesional temporal lobe epilepsy with evidence for predominant right temporal epileptogenesis. While seizure frequency had been grossly underestimated for many years, ultralong-term monitoring with a subcutaneous EEG device revealed actual seizure frequency (66 over 11 months vs four patient-documented seizures), providing objective data on treatment efficacy and additional supportive lateralizing information that played a decisive role for the choice of surgical treatment, which had been rejected by the patient prior to this information.
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Affiliation(s)
- Martin Hirsch
- Epilepsy Center, University Medical CenterUniversity of FreiburgFreiburgGermany
- European Reference Network EpiCareBronFrance
| | - Yulia Novitskaya
- Epilepsy Center, University Medical CenterUniversity of FreiburgFreiburgGermany
- European Reference Network EpiCareBronFrance
| | - Andreas Schulze‐Bonhage
- Epilepsy Center, University Medical CenterUniversity of FreiburgFreiburgGermany
- European Reference Network EpiCareBronFrance
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Schulze-Bonhage A, Bruno E, Brandt A, Shek A, Viana P, Heers M, Martinez-Lizana E, Altenmüller DM, Richardson MP, San Antonio-Arce V. Diagnostic yield and limitations of in-hospital documentation in patients with epilepsy. Epilepsia 2023; 64 Suppl 4:S4-S11. [PMID: 35583131 DOI: 10.1111/epi.17307] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/16/2022] [Accepted: 05/16/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To determine the diagnostic yield of in-hospital video-electroencephalography (EEG) monitoring to document seizures in patients with epilepsy. METHODS Retrospective analysis of electronic seizure documentation at the University Hospital Freiburg (UKF) and at King's College London (KCL). Statistical assessment of the role of the duration of monitoring, and subanalyses on presurgical patient groups and patients undergoing reduction of antiseizure medication. RESULTS Of more than 4800 patients with epilepsy undergoing in-hospital recordings at the two institutions since 2005, seizures with documented for 43% (KCL) and 73% (UKF).. Duration of monitoring was highly significantly associated with seizure recordings (p < .0001), and presurgical patients as well as patients with drug reduction had a significantly higher diagnostic yield (p < .0001). Recordings with a duration of >5 days lead to additional new seizure documentation in only less than 10% of patients. SIGNIFICANCE There is a need for the development of new ambulatory monitoring strategies to document seizures for diagnostic and monitoring purposes for a relevant subgroup of patients with epilepsy in whom in-hospital monitoring fails to document seizures.
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Affiliation(s)
- Andreas Schulze-Bonhage
- Epilepsy Center, University Medical Center, University of Freiburg, Freiburg, Germany
- European Reference Network EpiCARE
| | - Elisa Bruno
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Armin Brandt
- Epilepsy Center, University Medical Center, University of Freiburg, Freiburg, Germany
| | - Anthony Shek
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Pedro Viana
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Marcel Heers
- Epilepsy Center, University Medical Center, University of Freiburg, Freiburg, Germany
- European Reference Network EpiCARE
| | - Eva Martinez-Lizana
- Epilepsy Center, University Medical Center, University of Freiburg, Freiburg, Germany
- European Reference Network EpiCARE
| | | | - Mark Philip Richardson
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Victoria San Antonio-Arce
- Epilepsy Center, University Medical Center, University of Freiburg, Freiburg, Germany
- European Reference Network EpiCARE
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Attia TP, Viana PF, Nasseri M, Duun-Henriksen J, Biondi A, Winston JS, Martins IP, Nurse ES, Dümpelmann M, Worrell GA, Schulze-Bonhage A, Freestone DR, Kjaer TW, Brinkmann BH, Richardson MP. Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models. Epilepsia 2023; 64 Suppl 4:S114-S123. [PMID: 35441703 PMCID: PMC9582039 DOI: 10.1111/epi.17265] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/17/2022] [Accepted: 04/18/2022] [Indexed: 11/29/2022]
Abstract
This study describes a generalized cross-patient seizure-forecasting approach using recurrent neural networks with ultra-long-term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short-term memory (LSTM) deep-learning classifiers. Electrographic seizures were identified by a board-certified epileptologist. One-minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise-added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo-prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave-one-patient-out cross-validation method, and the area under the receiver-operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave-one-signal-out method with repeated training and testing for each classifier. Cross-patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution (p < .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross-patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two-channel ultra-long-term sqEEG.
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Affiliation(s)
- Tal Pal Attia
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
| | - Pedro F. Viana
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
- Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Mona Nasseri
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
- School of Engineering, University of North Florida, Jacksonville, Florida, USA
| | | | - Andrea Biondi
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Joel S. Winston
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Isabel P. Martins
- Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Ewan S. Nurse
- Seer Medical Pty Ltd., Melbourne, Victoria, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center, Faculty of Medicine, University Medical Center, University of Freiburg, Freiburg, Germany
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center, Faculty of Medicine, University Medical Center, University of Freiburg, Freiburg, Germany
| | - Dean R. Freestone
- Seer Medical Pty Ltd., Melbourne, Victoria, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Troels W. Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P. Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK
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7
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Statsenko Y, Babushkin V, Talako T, Kurbatova T, Smetanina D, Simiyu GL, Habuza T, Ismail F, Almansoori TM, Gorkom KNV, Szólics M, Hassan A, Ljubisavljevic M. Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach. Biomedicines 2023; 11:2370. [PMID: 37760815 PMCID: PMC10525492 DOI: 10.3390/biomedicines11092370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 09/29/2023] Open
Abstract
Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.
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Affiliation(s)
- Yauhen Statsenko
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Medical Imaging Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain P.O. Box 15551, United Arab Emirates
- Big Data Analytics Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Vladimir Babushkin
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Tatsiana Talako
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Department of Oncohematology, Minsk Scientific and Practical Center for Surgery, Transplantology and Hematology, 220089 Minsk, Belarus
| | - Tetiana Kurbatova
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Darya Smetanina
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Gillian Lylian Simiyu
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Tetiana Habuza
- Big Data Analytics Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Fatima Ismail
- Pediatric Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Taleb M. Almansoori
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Klaus N.-V. Gorkom
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Miklós Szólics
- Neurology Division, Medicine Department, Tawam Hospital, Al Ain P.O. Box 15258, United Arab Emirates
- Internal Medicine Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Ali Hassan
- Neurology Division, Medicine Department, Tawam Hospital, Al Ain P.O. Box 15258, United Arab Emirates
| | - Milos Ljubisavljevic
- Physiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates;
- Neuroscience Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain P.O. Box 15551, United Arab Emirates
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8
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Damalerio RB, Lim R, Gao Y, Zhang TT, Cheng MY. Development of Low-Contact-Impedance Dry Electrodes for Electroencephalogram Signal Acquisition. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094453. [PMID: 37177657 PMCID: PMC10181682 DOI: 10.3390/s23094453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/24/2023] [Accepted: 04/30/2023] [Indexed: 05/15/2023]
Abstract
Dry electroencephalogram (EEG) systems have a short set-up time and require limited skin preparation. However, they tend to require strong electrode-to-skin contact. In this study, dry EEG electrodes with low contact impedance (<150 kΩ) were fabricated by partially embedding a polyimide flexible printed circuit board (FPCB) in polydimethylsiloxane and then casting them in a sensor mold with six symmetrical legs or bumps. Silver-silver chloride paste was used at the exposed tip of each leg or bump that must touch the skin. The use of an FPCB enabled the fabricated electrodes to maintain steady impedance. Two types of dry electrodes were fabricated: flat-disk electrodes for skin with limited hair and multilegged electrodes for common use and for areas with thick hair. Impedance testing was conducted with and without a custom head cap according to the standard 10-20 electrode arrangement. The experimental results indicated that the fabricated electrodes exhibited impedance values between 65 and 120 kΩ. The brain wave patterns acquired with these electrodes were comparable to those acquired using conventional wet electrodes. The fabricated EEG electrodes passed the primary skin irritation tests based on the ISO 10993-10:2010 protocol and the cytotoxicity tests based on the ISO 10993-5:2009 protocol.
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Affiliation(s)
- Ramona B Damalerio
- Institute of Microelectronics, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Ruiqi Lim
- Institute of Microelectronics, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Yuan Gao
- Institute of Microelectronics, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Tan-Tan Zhang
- Institute of Microelectronics, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Ming-Yuan Cheng
- Institute of Microelectronics, Agency for Science, Technology and Research, Singapore 138634, Singapore
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9
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Reynolds A, Vranic-Peters M, Lai A, Grayden DB, Cook MJ, Peterson A. Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review. Epilepsia 2023; 64:1125-1174. [PMID: 36790369 DOI: 10.1111/epi.17548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 02/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.
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Affiliation(s)
- Ashley Reynolds
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Michaela Vranic-Peters
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Alan Lai
- Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Grayden
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Andre Peterson
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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10
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Boddeti U, McAfee D, Khan A, Bachani M, Ksendzovsky A. Responsive Neurostimulation for Seizure Control: Current Status and Future Directions. Biomedicines 2022; 10:2677. [PMID: 36359197 PMCID: PMC9687706 DOI: 10.3390/biomedicines10112677] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/13/2022] [Accepted: 10/15/2022] [Indexed: 10/29/2023] Open
Abstract
Electrocorticography (ECoG) data are commonly obtained during drug-resistant epilepsy (DRE) workup, in which subdural grids and stereotaxic depth electrodes are placed on the cortex for weeks at a time, with the goal of elucidating seizure origination. ECoG data can also be recorded from neuromodulatory devices, such as responsive neurostimulation (RNS), which involves the placement of electrodes deep in the brain. Of the neuromodulatory devices, RNS is the first to use recorded ECoG data to direct the delivery of electrical stimulation in order to control seizures. In this review, we first introduced the clinical management for epilepsy, and discussed the steps from seizure onset to surgical intervention. We then reviewed studies discussing the emergence and therapeutic mechanism behind RNS, and discussed why RNS may be underperforming despite an improved seizure detection mechanism. We discussed the potential utility of incorporating machine learning techniques to improve seizure detection in RNS, and the necessity to change RNS targets for stimulation, in order to account for the network theory of epilepsy. We concluded by commenting on the current and future status of neuromodulation in managing epilepsy, and the role of predictive algorithms to improve outcomes.
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Affiliation(s)
- Ujwal Boddeti
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Darrian McAfee
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Anas Khan
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Muzna Bachani
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Alexander Ksendzovsky
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
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11
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Al-Bakri AF, Martinek R, Pelc M, Zygarlicki J, Kawala-Sterniuk A. Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples. SENSORS (BASEL, SWITZERLAND) 2022; 22:7522. [PMID: 36236621 PMCID: PMC9571066 DOI: 10.3390/s22197522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/20/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.
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Affiliation(s)
- Amir F. Al-Bakri
- Department of Biomedical Engineering, College of Engineering, University of Babylon, Hillah 51001, Iraq
| | - Radek Martinek
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava–Poruba, Czech Republic
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
- School of Computing and Mathematical Sciences, University of Greenwich, Park Row, London SE10 9LS, UK
| | - Jarosław Zygarlicki
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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12
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Kreitlow BL, Li W, Buchanan GF. Chronobiology of epilepsy and sudden unexpected death in epilepsy. Front Neurosci 2022; 16:936104. [PMID: 36161152 PMCID: PMC9490261 DOI: 10.3389/fnins.2022.936104] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 06/27/2022] [Indexed: 11/24/2022] Open
Abstract
Epilepsy is a neurological disease characterized by spontaneous, unprovoked seizures. Various insults render the brain hyperexcitable and susceptible to seizure. Despite there being dozens of preventative anti-seizure medications available, these drugs fail to control seizures in nearly 1 in 3 patients with epilepsy. Over the last century, a large body of evidence has demonstrated that internal and external rhythms can modify seizure phenotypes. Physiologically relevant rhythms with shorter periodic rhythms, such as endogenous circadian rhythms and sleep-state, as well as rhythms with longer periodicity, including multidien rhythms and menses, influence the timing of seizures through poorly understood mechanisms. The purpose of this review is to discuss the findings from both human and animal studies that consider the effect of such biologically relevant rhythms on epilepsy and seizure-associated death. Patients with medically refractory epilepsy are at increased risk of sudden unexpected death in epilepsy (SUDEP). The role that some of these rhythms play in the nocturnal susceptibility to SUDEP will also be discussed. While the involvement of some of these rhythms in epilepsy has been known for over a century, applying the rhythmic nature of such phenomenon to epilepsy management, particularly in mitigating the risk of SUDEP, has been underutilized. As our understanding of the physiological influence on such rhythmic phenomenon improves, and as technology for chronic intracranial epileptiform monitoring becomes more widespread, smaller and less invasive, novel seizure-prediction technologies and time-dependent chronotherapeutic seizure management strategies can be realized.
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Affiliation(s)
- Benjamin L. Kreitlow
- Medical Scientist Training Program, University of Iowa, Iowa City, IA, United States
- Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, IA, United States
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, United States
- Department of Neurology, University of Iowa, Iowa City, IA, United States
- Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - William Li
- Department of Neurology, University of Iowa, Iowa City, IA, United States
- Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - Gordon F. Buchanan
- Medical Scientist Training Program, University of Iowa, Iowa City, IA, United States
- Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, IA, United States
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, United States
- Department of Neurology, University of Iowa, Iowa City, IA, United States
- Carver College of Medicine, University of Iowa, Iowa City, IA, United States
- *Correspondence: Gordon F. Buchanan, ; orcid.org/0000-0003-2371-4455
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13
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Zhang S, Liu G, Xiao R, Cui W, Cai J, Hu X, Sun Y, Qiu J, Qi Y. A combination of statistical parameters for epileptic seizure detection and classification using VMD and NLTWSVM. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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14
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Chiang S, Fan JM, Rao VR. Bilateral temporal lobe epilepsy: How many seizures are required in chronic ambulatory electrocorticography to estimate the laterality ratio? Epilepsia 2022; 63:199-208. [PMID: 34723396 PMCID: PMC9056258 DOI: 10.1111/epi.17113] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/17/2021] [Accepted: 10/18/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE This study was undertaken to measure the duration of chronic electrocorticography (ECoG) needed to attain stable estimates of the seizure laterality ratio in patients with drug-resistant bilateral temporal lobe epilepsy (BTLE). METHODS We studied 13 patients with drug-resistant BTLE who were implanted for at least 1 year with a responsive neurostimulation device (RNS System) that provides chronic ambulatory ECoG. Bootstrap analysis and nonlinear regression were applied to model the relationship between chronic ECoG duration and the probability of capturing at least one seizure. Laterality of electrographic seizures in chronic ECoG was compared with the seizure laterality ratio from Phase 1 scalp video-electroencephalographic (vEEG) monitoring. The Kaplan-Meier estimator was used to evaluate time to seizure laterality ratio convergence. RESULTS Seizure laterality ratios from Phase 1 scalp vEEG monitoring correlated poorly with those from RNS chronic ECoG (r = .31, p = .30). Across the 13 patients, average electrographic seizure frequencies ranged from 1.4 seizures/month to 5.1 seizures/day. A 50% probability of recording at least one electrographic seizure required 9.1 days of chronic ECoG, and 90% probability required 44.3 days of chronic ECoG. A median recording duration of 150.9 days (5 months), corresponding to a median of 16 seizures, was needed before confidence intervals for the seizure laterality ratio reliably contained the long-term value. The median recording duration before the point estimate of the seizure laterality ratio converged to a stationary value was 236.8 days (7.9 months). SIGNIFICANCE RNS chronic ECoG overcomes temporal sampling limitations intrinsic to inpatient Phase 1 vEEG evaluations. In patients with drug-resistant BTLE, approximately 8 months of chronic RNS ECoG are needed to precisely estimate the seizure laterality ratio, with 75% of people with BTLE achieving convergence after 1 year of RNS recording. For individuals who are candidates for unilateral resection based on seizure laterality, optimized recording duration may help avert morbidity associated with delay to definitive treatment.
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Affiliation(s)
- Sharon Chiang
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Joline M Fan
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
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15
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Peedicail J, Mehdiratta N, Zhu S, Nedjadrasul P, Ng MC. Quantitative burst suppression on serial intermittent EEG in refractory status epilepticus. Clin Neurophysiol Pract 2021; 6:275-280. [PMID: 34825115 PMCID: PMC8604990 DOI: 10.1016/j.cnp.2021.10.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 10/03/2021] [Accepted: 10/23/2021] [Indexed: 11/24/2022] Open
Abstract
Quantitative burst suppression ratios (QBSR) represent depth of EEG suppression. Deeper QBSR on serial intermittent EEG did not affect survival in RSE. Non-suppressive continuous EEG effects on RSE mortality merits further research.
Objectives In refractory status epilepticus (RSE), the optimal degree of suppression (EEG burst suppression or merely suppressing seizures) remains unknown. Many centers lacking continuous EEG must default to serial intermittent recordings where uncertainty from lack of data may prompt more aggressive suppression. In this study, we sought to determine whether the quantitative burst suppression ratio (QBSR) from serial intermittent EEG recording is associated with RSE patient outcome. Methods We screened the EEG database to identify non-anoxic adult RSE patients for EEG and chart review. QBSR was calculated per 10-second EEG epoch as the percentage of time during which EEG amplitude was <3 µV. Patients who survived 1–3 months after discharge from ICU and hospital comprised the favorable group. Further to initial unadjusted univariate analysis of all pooled QBSR, we conducted multivariate analyses to account for individual patient confounders (“per-capita analysis”), uneven number of EEG recordings (“per-session analysis”), and uneven number of epochs (“per-epoch analysis”). We analyzed gender, anesthetic number, and adjusted status epilepticus severity score (aSTESS) as confounders. Results In 135,765 QBSR values over 160 EEG recordings (median 2.17 h every ≥24 h) from 17 patients on Propofol, Midazolam, and/or Ketamine, QBSR was deeper in the favorable group (p < 0.001) on initial unadjusted analysis. However, on adjusted multivariate analysis, there was consistently no association between QBSR and outcome. Higher aSTESS consistently associated with unfavorable outcome on per-capita (p = 0.033), per-session (p = 0.048) and per-epoch (p < 0.001) analyses. Greater maximal number of non-barbiturate anesthetic associated with favorable outcome on per-epoch analysis (p < 0.001). Conclusions There was no association between depth of EEG suppression using non-barbiturate anesthetic and RSE patient outcome based on QBSR from serial intermittent EEG. A per-epoch association between non-barbiturate anesthetic and favorable outcome suggests an effect from non-suppressive time-varying EEG content. Significance Targeting and following deeper burst suppression through non-barbiturate anesthetics on serial intermittent EEG monitoring of RSE is of limited utility.
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Affiliation(s)
- Joseph Peedicail
- Section of Neurology, University of Manitoba, Winnipeg, MB, Canada
| | - Neil Mehdiratta
- Section of Neurology, University of Manitoba, Winnipeg, MB, Canada
| | - Shenghua Zhu
- Department of Radiology, University of Ottawa, Ottawa, ON, Canada
| | | | - Marcus C Ng
- Section of Neurology, University of Manitoba, Winnipeg, MB, Canada
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Rao VR. Chronic electroencephalography in epilepsy with a responsive neurostimulation device: current status and future prospects. Expert Rev Med Devices 2021; 18:1093-1105. [PMID: 34696676 DOI: 10.1080/17434440.2021.1994388] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Implanted neurostimulation devices are gaining traction as therapeutic options for people with certain forms of drug-resistant focal epilepsy. Some of these devices enable chronic electroencephalography (cEEG), which offers views of the dynamics of brain activity in epilepsy over unprecedented time horizons. AREAS COVERED This review focuses on clinical insights and basic neuroscience discoveries enabled by analyses of cEEG from an exemplar device, the NeuroPace RNS® System. Applications of RNS cEEG covered here include counting and lateralizing seizures, quantifying medication response, characterizing spells, forecasting seizures, and exploring mechanisms of cognition. Limitations of the RNS System are discussed in the context of next-generation devices in development. EXPERT OPINION The wide temporal lens of cEEG helps capture the dynamism of epilepsy, revealing phenomena that cannot be appreciated with short duration recordings. The RNS System is a vanguard device whose diagnostic utility rivals its therapeutic benefits, but emerging minimally invasive devices, including those with subscalp recording electrodes, promise to be more applicable within a broad population of people with epilepsy. Epileptology is on the precipice of a paradigm shift in which cEEG is a standard part of diagnostic evaluations and clinical management is predicated on quantitative observations integrated over long timescales.
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Affiliation(s)
- Vikram R Rao
- Associate Professor of Clinical Neurology, Chief, Epilepsy Division, Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
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17
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Schindler KA, Rahimi A. A Primer on Hyperdimensional Computing for iEEG Seizure Detection. Front Neurol 2021; 12:701791. [PMID: 34354666 PMCID: PMC8329339 DOI: 10.3389/fneur.2021.701791] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 06/18/2021] [Indexed: 11/13/2022] Open
Abstract
A central challenge in today's care of epilepsy patients is that the disease dynamics are severely under-sampled in the currently typical setting with appointment-based clinical and electroencephalographic examinations. Implantable devices to monitor electrical brain signals and to detect epileptic seizures may significantly improve this situation and may inform personalized treatment on an unprecedented scale. These implantable devices should be optimized for energy efficiency and compact design. Energy efficiency will ease their maintenance by reducing the time of recharging, or by increasing the lifetime of their batteries. Biological nervous systems use an extremely small amount of energy for information processing. In recent years, a number of methods, often collectively referred to as brain-inspired computing, have also been developed to improve computation in non-biological hardware. Here, we give an overview of one of these methods, which has in particular been inspired by the very size of brains' circuits and termed hyperdimensional computing. Using a tutorial style, we set out to explain the key concepts of hyperdimensional computing including very high-dimensional binary vectors, the operations used to combine and manipulate these vectors, and the crucial characteristics of the mathematical space they inhabit. We then demonstrate step-by-step how hyperdimensional computing can be used to detect epileptic seizures from intracranial electroencephalogram (EEG) recordings with high energy efficiency, high specificity, and high sensitivity. We conclude by describing potential future clinical applications of hyperdimensional computing for the analysis of EEG and non-EEG digital biomarkers.
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Affiliation(s)
- Kaspar A Schindler
- Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, NeuroTec, Bern University Hospital, University Bern, Bern, Switzerland
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18
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von Wrede R, Surges R. Transcutaneous vagus nerve stimulation in the treatment of drug-resistant epilepsy. Auton Neurosci 2021; 235:102840. [PMID: 34246121 DOI: 10.1016/j.autneu.2021.102840] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/22/2021] [Accepted: 06/24/2021] [Indexed: 01/16/2023]
Abstract
Epilepsy is a common chronic neurological disease with a high burden of illness. Invasive vagus nerve stimulation (iVNS) is a well-established treatment option in patients with epilepsy (PWE). More recently, transcutaneous vagus nerve stimulation (tVNS) was introduced, an alternative option which is particularly interesting because it does not require surgery and is instantaneously removable. Here, we thoroughly reviewed clinical data on efficacy and safety of tVNS in epilepsies. Five prospective trials in 118 patients with drug-resistant epilepsies and 3 randomized controlled trials in 280 patients with drug-resistant epilepsies were carried out. Study protocols were heterogeneous in terms of patients' characteristics, used device, stimulation parameters, study duration and endpoints. Seizure reduction amounted up to 64%, with responder rates (seizure reduction ≥50%) up to 65%. Seizure freedom was reached in up to 24%, and even to 31% in a small pediatric study group. Seizure severity scores were provided in 4 studies, showing significant improvement in two of them. Adverse side effects were mostly headache, ear pain and skin alteration and rated as mild to moderate. Drowsiness might be depend on stimulation intensity. Quality of life scores reflecting burden of illness showed significant improvement in two studies. Efficacy and safety of tVNS in PWE has to be interpreted as promising. Multicenter randomized double-blind clinical trials with standardized stimulation protocols and long-term follow-up studies are necessary to finally assess tVNS treatment outcome in drug-resistant epilepsies.
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Affiliation(s)
- Randi von Wrede
- Department of Epileptology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany.
| | - Rainer Surges
- Department of Epileptology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
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19
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Karoly PJ, Rao VR, Gregg NM, Worrell GA, Bernard C, Cook MJ, Baud MO. Cycles in epilepsy. Nat Rev Neurol 2021; 17:267-284. [PMID: 33723459 DOI: 10.1038/s41582-021-00464-1] [Citation(s) in RCA: 115] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/19/2021] [Indexed: 01/31/2023]
Abstract
Epilepsy is among the most dynamic disorders in neurology. A canonical view holds that seizures, the characteristic sign of epilepsy, occur at random, but, for centuries, humans have looked for patterns of temporal organization in seizure occurrence. Observations that seizures are cyclical date back to antiquity, but recent technological advances have, for the first time, enabled cycles of seizure occurrence to be quantitatively characterized with direct brain recordings. Chronic recordings of brain activity in humans and in animals have yielded converging evidence for the existence of cycles of epileptic brain activity that operate over diverse timescales: daily (circadian), multi-day (multidien) and yearly (circannual). Here, we review this evidence, synthesizing data from historical observational studies, modern implanted devices, electronic seizure diaries and laboratory-based animal neurophysiology. We discuss advances in our understanding of the mechanistic underpinnings of these cycles and highlight the knowledge gaps that remain. The potential clinical applications of a knowledge of cycles in epilepsy, including seizure forecasting and chronotherapy, are discussed in the context of the emerging concept of seizure risk. In essence, this Review addresses the broad question of why seizures occur when they occur.
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Affiliation(s)
- Philippa J Karoly
- Graeme Clark Institute, The University of Melbourne, Melbourne, Victoria, Australia.
| | - Vikram R Rao
- Department of Neurology, University of California, San Francisco, CA, USA.,Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Nicholas M Gregg
- Bioelectronics, Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Gregory A Worrell
- Bioelectronics, Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Christophe Bernard
- Aix Marseille University, Inserm, Institut de Neurosciences des Systèmes, Marseille, France
| | - Mark J Cook
- Graeme Clark Institute, The University of Melbourne, Melbourne, Victoria, Australia
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland. .,Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland.
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Lehnertz K, Rings T, Bröhl T. Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks. FRONTIERS IN NETWORK PHYSIOLOGY 2021; 1:755016. [PMID: 36925573 PMCID: PMC10013076 DOI: 10.3389/fnetp.2021.755016] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022]
Abstract
Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.
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Affiliation(s)
- Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany.,Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
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