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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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Wang B, Xu Y, Peng S, Wang H, Li F. Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks. SENSORS (BASEL, SWITZERLAND) 2024; 24:3360. [PMID: 38894151 PMCID: PMC11174829 DOI: 10.3390/s24113360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 06/21/2024]
Abstract
Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of electroencephalogram (EEG) signals. However, the raw EEG signals contain limited recognizable features, and in order to increase the recognizable features in the input of the network, the differential features of the signals, the amplitude spectrum and the phase spectrum in the frequency domain are extracted to form a two-dimensional feature vector. In order to solve the problem of recognizing multimodal features, a neural network model based on a multimodal dual-stream network is proposed, which uses a mixture of one-dimensional convolution, two-dimensional convolution and LSTM neural networks to extract the spatial features of the EEG two-dimensional vectors and the temporal features of the signals, respectively, and combines the advantages of the two networks, using the hybrid neural network to extract both the temporal and spatial features of the signals at the same time. In addition, a channel attention module was used to focus the model on features related to seizures. Finally, multiple sets of experiments were conducted on the Bonn and New Delhi data sets, and the highest accuracy rates of 99.69% and 97.5% were obtained on the test set, respectively, verifying the superiority of the proposed model in the task of epileptic seizure detection.
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Affiliation(s)
- Baiyang Wang
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China; (B.W.)
| | - Yidong Xu
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China; (B.W.)
| | - Siyu Peng
- School of Information Engineering, Changji University, Changji Hui Autonomous Prefecture, Changji 831100, China
| | - Hongjun Wang
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China; (B.W.)
| | - Fang Li
- School of Information Engineering, Changji University, Changji Hui Autonomous Prefecture, Changji 831100, China
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Baud MO, Proix T, Gregg NM, Brinkmann BH, Nurse ES, Cook MJ, Karoly PJ. Seizure forecasting: Bifurcations in the long and winding road. Epilepsia 2023; 64 Suppl 4:S78-S98. [PMID: 35604546 PMCID: PMC9681938 DOI: 10.1111/epi.17311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/28/2022]
Abstract
To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.
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Affiliation(s)
- Maxime O Baud
- Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio- and Neuro-Engineering, Geneva, Switzerland
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan S Nurse
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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Viana PF, Attia TP, Nasseri M, Duun-Henriksen J, Biondi A, Winston JS, Martins IP, Nurse ES, Dümpelmann M, Schulze-Bonhage A, Freestone DR, Kjaer TW, Richardson MP, Brinkmann BH. Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models. Epilepsia 2023; 64 Suppl 4:S124-S133. [PMID: 35395101 PMCID: PMC9547037 DOI: 10.1111/epi.17252] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/05/2022] [Accepted: 04/05/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. METHODS We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. RESULTS Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. SIGNIFICANCE This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.
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Affiliation(s)
- Pedro F. Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Tal Pal Attia
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Mona Nasseri
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- School of Engineering, University of North Florida, Jacksonville, Florida, USA
| | | | - Andrea Biondi
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
| | - Joel S. Winston
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
| | | | - Ewan S. Nurse
- Seer Medical, Melbourne, Victoria, Australia
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Matthias Dümpelmann
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Dean R. Freestone
- Seer Medical, Melbourne, Victoria, Australia
- Department of Medicine, 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
| | - Mark P. Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
- National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust, London, UK
| | - Benjamin H. Brinkmann
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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Djurhuus BD, Viana PF, Ahrens E, Nielsen SS, Srinivasan HL, Richardson MP, Homøe P, Hasegawa H, Zarei AA, Gauger PLK, Duun-Henriksen J. Minimally invasive surgery for placement of a subcutaneous EEG implant. Front Surg 2023; 10:1304343. [PMID: 38026479 PMCID: PMC10665563 DOI: 10.3389/fsurg.2023.1304343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023] Open
Abstract
Background A new class of subcutaneous electroencephalography has enabled ultra long-term monitoring of people with epilepsy. The objective of this paper is to describe surgeons' experiences in an early series of implantations as well as discomfort or complications experienced by the participants. Methods We included 38 implantation procedures from two trials on people with epilepsy and healthy adults. Questionnaires to assess surgeons' and participants' experience were analyzed as well as all recorded adverse events occurring up to 21 days post-surgery. Results With training, the implantation could be performed in approximately 15 min. Overall, the implantation procedure was considered easy to perform with only 2 episodes where the implant got fixated in the introducing needle and a new implant had to be used. The explantation procedure was considered effortless. In 2 cases the silicone sheath covering the lead was damaged during the explantation, but it was possible to remove the entire implant without leaving any foreign body under the skin. Especially in the trial on healthy participants, a proportion experienced adverse events in the form of headache or implant-pain up to 21 days post-operatively. In 6 cases, adverse events contributed to the decision to explant and discontinue the study: Four of these cases involved implant pain or headache; One case involved a post-operative local infection; and in one case superficial lead placement resulted in skin perforation a few weeks after implantation. Conclusion The implantation and explantation procedures are considered swift and easy to perform by both neurosurgeons and ENT surgeons. The implant is well tolerated by most participants. However, headache or pain around the implant can occur for up to 21 days post-operatively as anticipated with any such surgery. The expected benefits from the implant should always outweigh the potential disadvantages.
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Affiliation(s)
- Bjarki D. Djurhuus
- Department of Otorhinolaryngology and Maxillofacial Surgery, Zealand University Hospital, University of Copenhagen, Koge, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Pedro F. Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Esben Ahrens
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- T&W Engineering A/S, Lillerød, Denmark
| | | | | | - Mark P. Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Preben Homøe
- Department of Otorhinolaryngology and Maxillofacial Surgery, Zealand University Hospital, University of Copenhagen, Koge, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Harutomo Hasegawa
- Neurosurgery Department, King’s College Hospital NHS Foundation Trust, London, United Kingdom
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Viana PF, Duun-Henriksen J, Winston JS, Kjaer TW, Skaarup C, Richardson MP. Atlas of ambulatory subcutaneous electroencephalography. Epileptic Disord 2023; 25:584-585. [PMID: 37149497 DOI: 10.1002/epd2.20066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/25/2023] [Accepted: 04/29/2023] [Indexed: 05/08/2023]
Affiliation(s)
- 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
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | | | - 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
| | | | | | - 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
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7
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Gelbard-Sagiv H, Pardo S, Getter N, Guendelman M, Benninger F, Kraus D, Shriki O, Ben-Sasson S. Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:5805. [PMID: 37447653 DOI: 10.3390/s23135805] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/15/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
Abstract
Epilepsy, a prevalent neurological disorder, profoundly affects patients' quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection.
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Affiliation(s)
| | - Snir Pardo
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
| | - Nir Getter
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Miriam Guendelman
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Felix Benninger
- Department of Neurology, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Dror Kraus
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Pediatric Neurology, Schneider Children's Medical Center of Israel, Petach Tikva 4920235, Israel
| | - Oren Shriki
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
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8
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Automatic sleep scoring using patient-specific ensemble models and knowledge distillation for ear-EEG data. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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9
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Arslan RS, Ulutas H, Köksal AS, Bakir M, Çiftçi B. Sensitive deep learning application on sleep stage scoring by using all PSG data. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08037-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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10
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Kjaer TW, Rank ML, Hemmsen MC, Kidmose P, Mikkelsen K. Repeated automatic sleep scoring based on ear-EEG is a valuable alternative to manually scored polysomnography. PLOS DIGITAL HEALTH 2022; 1:e0000134. [PMID: 36812563 PMCID: PMC9931275 DOI: 10.1371/journal.pdig.0000134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/25/2022] [Indexed: 11/07/2022]
Abstract
While polysomnography (PSG) is the gold standard to quantify sleep, modern technology allows for new alternatives. PSG is obtrusive, affects the sleep it is set out to measure and requires technical assistance for mounting. A number of less obtrusive solutions based on alternative methods have been introduced, but few have been clinically validated. Here we validate one of these solutions, the ear-EEG method, against concurrently recorded PSG in twenty healthy subjects each measured for four nights. Two trained technicians scored the 80 nights of PSG independently, while an automatic algorithm scored the ear-EEG. The sleep stages and eight sleep metrics (Total Sleep Time (TST), Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset, REM latency, REM fraction of TST, N2 fraction of TST, and N3 fraction of TST) were used in the further analysis. We found the sleep metrics: Total Sleep Time, Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset were estimated with high accuracy and precision between automatic sleep scoring and manual sleep scoring. However, the REM latency and REM fraction of sleep showed high accuracy but low precision. Further, the automatic sleep scoring systematically overestimated the N2 fraction of sleep and slightly underestimated the N3 fraction of sleep. We demonstrate that sleep metrics estimated from automatic sleep scoring based on repeated ear-EEG in some cases are more reliably estimated with repeated nights of automatically scored ear-EEG than with a single night of manually scored PSG. Thus, given the obtrusiveness and cost of PSG, ear-EEG seems to be a useful alternative for sleep staging for the single night recording and an advantageous choice for several nights of sleep monitoring.
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Affiliation(s)
| | | | | | - Preben Kidmose
- Department of Electrical and Computer Engineering, University of Aarhus, Denmark
| | - Kaare Mikkelsen
- Department of Electrical and Computer Engineering, University of Aarhus, Denmark,* E-mail:
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11
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Haneef Z, Yang K, Sheth SA, Aloor FZ, Aazhang B, Krishnan V, Karakas C. Sub-scalp electroencephalography: A next-generation technique to study human neurophysiology. Clin Neurophysiol 2022; 141:77-87. [PMID: 35907381 DOI: 10.1016/j.clinph.2022.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/20/2022] [Accepted: 07/03/2022] [Indexed: 11/29/2022]
Abstract
Sub-scalp electroencephalography (ssEEG) is emerging as a promising technology in ultra-long-term electroencephalography (EEG) recordings. Given the diversity of devices available in this nascent field, uncertainty persists about its utility in epilepsy evaluation. This review critically dissects the many proposed utilities of ssEEG devices including (1) seizure quantification, (2) seizure characterization, (3) seizure lateralization, (4) seizure localization, (5) seizure alarms, (6) seizure forecasting, (7) biomarker discovery, (8) sleep medicine, and (9) responsive stimulation. The different ssEEG devices in development have individual design philosophies with unique strengths and limitations. There are devices offering primarily unilateral recordings (24/7 EEGTM SubQ, NeuroviewTM, Soenia® UltimateEEG™), bilateral recordings (Minder™, Epios™), and even those with responsive stimulation capability (EASEE®). We synthesize the current knowledge of these ssEEG systems. We review the (1) ssEEG devices, (2) use case scenarios, (3) challenges and (4) suggest a roadmap for ideal ssEEG designs.
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Affiliation(s)
- Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Kaiyuan Yang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Fuad Z Aloor
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Vaishnav Krishnan
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Cemal Karakas
- Division of Pediatric Neurology, Department of Neurology, University of Louisville, Louisville, KY 40202, USA; Norton Children's Neuroscience Institute, Louisville, KY 40241, USA
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12
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Kazemi A, McKeown MJ, Mirian MS. Sleep staging using semi-unsupervised clustering of EEG: Application to REM sleep behavior disorder. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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13
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von Ellenrieder N, Peter-Derex L, Gotman J, Frauscher B. SleepSEEG: Automatic sleep scoring using intracranial EEG recordings only. J Neural Eng 2022; 19. [PMID: 35439736 DOI: 10.1088/1741-2552/ac6829] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/18/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To perform automatic sleep scoring based only on intracranial EEG, without the need for scalp electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), in order to study sleep, epilepsy, and their interaction. APPROACH Data from 33 adult patients was used for development and training of the automatic scoring algorithm using both oscillatory and non-oscillatory spectral features. The first step consisted in unsupervised clustering of channels based on feature variability. For each cluster the classification was done in two steps, a multiclass tree followed by binary classification trees to distinguish the more challenging stage N1. The test data consisted in 11 patients, in whom the classification was done independently for each channel and then combined to get a single stage per epoch. MAIN RESULTS An overall agreement of 78% was observed in the test set between the sleep scoring of the algorithm and two human experts scoring based on scalp EEG, EOG and EMG. Balanced sensitivity and specificity were obtained for the different sleep stages. The performance was excellent for stages W, N2, and N3, and good for stage R, but with high variability across patients. The performance for the challenging stage N1 was poor, but at a similar level as for published algorithms based on scalp EEG. High confidence epochs in different stages (other than N1) can be identified with median per patient specificity >80%. SIGNIFICANCE The automatic algorithm can perform sleep scoring of long term recordings of patients with intracranial electrodes undergoing presurgical evaluation in the absence of scalp EEG, EOG and EMG, which are normally required to define sleep stages but are difficult to use in the context of intracerebral studies. It also constitutes a valuable tool to generate hypotheses regarding local aspects of sleep, and will be significant for sleep evaluation in clinical epileptology and neuroscience research.
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Affiliation(s)
- Nicolás von Ellenrieder
- Montreal Neurological Institute and Hospital, McGill University, 3801 University streeet, Montreal, Quebec, H3A 2B4, CANADA
| | - Laure Peter-Derex
- PAM Team, Centre de Recherche en Neurosciences de Lyon, 95 Boulevard Pinel, Lyon, Rhône-Alpes , 69675 BRON, FRANCE
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, 3801 University St, Montreal, Quebec, H3A 2B4, CANADA
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, CANADA
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14
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Phan H, Mikkelsen K. Automatic sleep staging of EEG signals: recent development, challenges, and future directions. Physiol Meas 2022; 43. [PMID: 35320788 DOI: 10.1088/1361-6579/ac6049] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/23/2022] [Indexed: 11/11/2022]
Abstract
Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.
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Affiliation(s)
- Huy Phan
- School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Rd, London, E1 4NS, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Kaare Mikkelsen
- Department of Electrical and Computer Engineering, Aarhus Universitet, Finlandsgade 22, Aarhus, 8000, DENMARK
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15
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Mivalt F, Kremen V, Sladky V, Balzekas I, Nejedly P, Gregg N, Lundstrom B, Lepkova K, Pridalova T, Brinkmann BH, Jurak P, Van Gompel JJ, Miller K, Denison T, Louis ES, Worrell GA. Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans. J Neural Eng 2022; 19:10.1088/1741-2552/ac4bfd. [PMID: 35038687 PMCID: PMC9070680 DOI: 10.1088/1741-2552/ac4bfd] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/17/2022] [Indexed: 11/11/2022]
Abstract
Objective.Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT).Approach.The iEEG recorded from HPC is used to classify sleep during concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz).Main results.We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC iEEG sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard PSG annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment.Significance.The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities.
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Affiliation(s)
- Filip Mivalt
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Vaclav Kremen
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Vladimir Sladky
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Irena Balzekas
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Mayo Clinic School of Medicine and the Mayo Clinic Medical Scientist Training Program, Rochester, MN, USA
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, USA
| | - Petr Nejedly
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic
| | - Nick Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Brian Lundstrom
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Kamila Lepkova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Tereza Pridalova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Pavel Jurak
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic
| | | | - Kai Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA
| | - Timothy Denison
- Department of Biomedical Engineering, Oxford University, Oxford, UK
| | - Erik St Louis
- Center for Sleep Medicine, Departments of Neurology and Medicine, Divisions of Sleep Neurology & Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
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16
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Pathmanathan J, Kjaer TW, Cole AJ, Delanty N, Surges R, Duun-Henriksen J. Expert Perspective: Who May Benefit Most From the New Ultra Long-Term Subcutaneous EEG Monitoring? Front Neurol 2022; 12:817733. [PMID: 35126304 PMCID: PMC8810530 DOI: 10.3389/fneur.2021.817733] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 12/23/2021] [Indexed: 12/21/2022] Open
Abstract
Today's modalities for short-term monitoring of EEG are primarily meant for supporting clinical diagnosis of epilepsy or classifying seizures and interictal epileptiform discharges while long-term EEG adds the value of differential diagnosis investigation or pre-surgical evaluation. However, longitudinal epilepsy care relies on patient diaries, which is known to be unreliable for most patients and especially those with focal impaired awareness or nocturnal seizures. The subcutaneous ultra long-term EEG (ULT-EEG) systems alleviate those issue by enabling objective, continuous EEG monitoring for days, weeks, months, or years. Albeit a great advance in continuous EEG over extended periods, it comes with the caveat of limited spatial resolution of two channels. Therefore, the new subcutaneous EEG modality may be especially suited for a selected group of patients. We convened a panel of experienced epileptologists to consider the utility of a subcutaneous, two-channel ULT-EEG device with the goal of developing a consensus-based expert recommendation on selecting the optimal patient types for this investigative technique. The ideal patients to select for this type of monitoring would have focal impaired awareness seizures without predominant motor features and seizures with medium to high voltage patterns. As this technology matures and we learn more about its limitations and benefits we might find a wider array of use case scenarios as it is believed that the benefits for many patients are most likely to outweigh the risks and cost.
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Affiliation(s)
- Jay Pathmanathan
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Troels W. Kjaer
- Department of Neurology, Center of Neurophysiology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Andrew J. Cole
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Norman Delanty
- Department of Neurology, Beaumont Hospital, Dublin, Ireland
- FutureNeuro Research Centre, Dublin, Ireland
- School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Rainer Surges
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
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17
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Balzekas I, Sladky V, Nejedly P, Brinkmann BH, Crepeau D, Mivalt F, Gregg NM, Pal Attia T, Marks VS, Wheeler L, Riccelli TE, Staab JP, Lundstrom BN, Miller KJ, Van Gompel J, Kremen V, Croarkin PE, Worrell GA. Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies. Front Hum Neurosci 2021; 15:702605. [PMID: 34381344 PMCID: PMC8349989 DOI: 10.3389/fnhum.2021.702605] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 06/29/2021] [Indexed: 01/10/2023] Open
Abstract
Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid psychiatric disorders are very common in drug-resistant epilepsy and their added complexity warrants careful consideration. In this review, we first discuss psychiatric comorbidities and symptoms in patients with epilepsy. We describe how epilepsy can potentially impact patient presentation and how these factors can be addressed in the experimental designs of studies focused on the electrophysiologic correlates of mood. Second, we review emerging technologies to integrate long-term iEEG recording with dense behavioral tracking in naturalistic environments. Third, we explore questions on how best to address the intersection between epilepsy and psychiatric comorbidities. Advances in ambulatory iEEG and long-term behavioral monitoring technologies will be instrumental in studying the intersection of seizures, epilepsy, psychiatric comorbidities, and their underlying circuitry.
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Affiliation(s)
- Irena Balzekas
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
- Mayo Clinic Medical Scientist Training Program, Rochester, MN, United States
| | - Vladimir Sladky
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czechia
| | - Petr Nejedly
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czechia
| | - Benjamin H. Brinkmann
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Daniel Crepeau
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Filip Mivalt
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
| | - Nicholas M. Gregg
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Tal Pal Attia
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Victoria S. Marks
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
| | - Lydia Wheeler
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
| | - Tori E. Riccelli
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
| | - Jeffrey P. Staab
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
- Department of Otorhinolaryngology, Mayo Clinic, Rochester, MN, United States
| | - Brian Nils Lundstrom
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Kai J. Miller
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Jamie Van Gompel
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Vaclav Kremen
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czechia
| | - Paul E. Croarkin
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Gregory A. Worrell
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
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18
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Viana PF, Remvig LS, Duun-Henriksen J, Glasstetter M, Dümpelmann M, Nurse ES, Martins IP, Schulze-Bonhage A, Freestone DR, Brinkmann BH, Kjaer TW, Richardson MP. Signal quality and power spectrum analysis of remote ultra long-term subcutaneous EEG. Epilepsia 2021; 62:1820-1828. [PMID: 34250608 DOI: 10.1111/epi.16969] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 06/02/2021] [Accepted: 06/03/2021] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Ultra long-term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long-term quality and consistency of the sqEEG signal, which is the objective of this study. METHODS The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG™ SubQ), and recorded from 23 to 230 days (median 42 days), with a median data capture rate of 75% (17.9 hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long-term temporal trends in signal impedance and power spectral features were investigated with subject-specific linear regression models and group-level linear mixed-effects models. RESULTS sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8-13Hz) and nocturnal peaks in the sigma range (12-16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods. SIGNIFICANCE The spectral characteristics of minimally invasive, ultra long-term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain-computer interfaces.
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Affiliation(s)
- Pedro F Viana
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | | | | | - Martin Glasstetter
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Ewan S Nurse
- Seer Medical Inc, Melbourne, Vic, Australia.,Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Vic, Australia
| | | | - Andreas Schulze-Bonhage
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Dean R Freestone
- Seer Medical Inc, Melbourne, Vic, Australia.,Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Vic, Australia
| | - Benjamin H Brinkmann
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Troels W Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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19
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Tabar YR, Mikkelsen KB, Rank ML, Hemmsen MC, Kidmose P. Investigation of low dimensional feature spaces for automatic sleep staging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 205:106091. [PMID: 33882415 DOI: 10.1016/j.cmpb.2021.106091] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 04/03/2021] [Indexed: 05/22/2023]
Abstract
BACKGROUND Automatic sleep stage classification depends crucially on the selection of a good set of descriptive features. However, the selection of a feature set with an appropriate low computational cost without compromising classification performance is still a challenge. This study attempts to represent sleep EEG patterns using a minimum number of features, without significant performance loss. METHODS Three feature selection algorithms were applied to a high dimensional feature space comprising 84 features. These methods were based on a bootstrapping approach guided by Gini ranking and mutual information between the features. The algorithms were tested on three scalp electroencephalography (EEG) and one ear-EEG datasets. The relationship between the information carried by different features was investigated using mutual information and illustrated by a graphical clustering tool. RESULTS The minimum number of features that can represent the whole feature set without performance loss was found to range between 5 and 11 for different datasets. In ear-EEG, 7 features based on Continuous Wavelet Transform (CWT) resulted in similar performance as the whole set whereas in two scalp EEG datasets, the difference between minimal CWT set and the whole set was statistically significant (0.008 and 0.017 difference in average kappa). Features were divided into groups according to the type of information they carry. The group containing relative power features was identified as the most informative feature group in sleep stage classification, whereas the group containing non-linear features was found to be the least informative. CONCLUSIONS This study showed that EEG sleep staging can be performed based on a low dimensional feature space without significant decrease in sleep staging performance. This is especially important in the case of wearable devices like ear-EEG where low computational complexity is needed. The division of the feature space into groups of features, and the analysis of the distribution of feature groups for different feature set sizes, is helpful in the selection of an appropriate feature set.
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Affiliation(s)
- Yousef Rezaei Tabar
- Department of Engineering, Aarhus University, Tabar, Finlandsgade 22, Building 5125, 8200 Aarhus N, Denmark.
| | - Kaare B Mikkelsen
- Department of Engineering, Aarhus University, Tabar, Finlandsgade 22, Building 5125, 8200 Aarhus N, Denmark
| | | | | | - Preben Kidmose
- Department of Engineering, Aarhus University, Tabar, Finlandsgade 22, Building 5125, 8200 Aarhus N, Denmark
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20
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Biondi A, Laiou P, Bruno E, Viana PF, Schreuder M, Hart W, Nurse E, Pal DK, Richardson MP. Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study. JMIR Res Protoc 2021; 10:e25309. [PMID: 33739290 PMCID: PMC8088854 DOI: 10.2196/25309] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/22/2020] [Accepted: 12/23/2020] [Indexed: 01/06/2023] Open
Abstract
Background Epileptic seizures are spontaneous events that severely affect the lives of patients due to their recurrence and unpredictability. The integration of new wearable and mobile technologies to collect electroencephalographic (EEG) and extracerebral signals in a portable system might be the solution to prospectively identify times of seizure occurrence or propensity. The performances of several seizure detection devices have been assessed by validated studies, and patient perspectives on wearables have been explored to better match their needs. Despite this, there is a major gap in the literature on long-term, real-life acceptability and performance of mobile technology essential to managing chronic disorders such as epilepsy. Objective EEG@HOME is an observational, nonrandomized, noninterventional study that aims to develop a new feasible procedure that allows people with epilepsy to independently, continuously, and safely acquire noninvasive variables at home. The data collected will be analyzed to develop a general model to predict periods of increased seizure risk. Methods A total of 12 adults with a diagnosis of pharmaco-resistant epilepsy and at least 20 seizures per year will be recruited at King’s College Hospital, London. Participants will be asked to self-apply an easy and portable EEG recording system (ANT Neuro) to record scalp EEG at home twice daily. From each serial EEG recording, brain network ictogenicity (BNI), a new biomarker of the propensity of the brain to develop seizures, will be extracted. A noninvasive wrist-worn device (Fitbit Charge 3; Fitbit Inc) will be used to collect non-EEG biosignals (heart rate, sleep quality index, and steps), and a smartphone app (Seer app; Seer Medical) will be used to collect data related to seizure occurrence, medication taken, sleep quality, stress, and mood. All data will be collected continuously for 6 months. Standardized questionnaires (the Post-Study System Usability Questionnaire and System Usability Scale) will be completed to assess the acceptability and feasibility of the procedure. BNI, continuous wrist-worn sensor biosignals, and electronic survey data will be correlated with seizure occurrence as reported in the diary to investigate their potential values as biomarkers of seizure risk. Results The EEG@HOME project received funding from Epilepsy Research UK in 2018 and was approved by the Bromley Research Ethics Committee in March 2020. The first participants were enrolled in October 2020, and we expect to publish the first results by the end of 2022. Conclusions With the EEG@HOME study, we aim to take advantage of new advances in remote monitoring technology, including self-applied EEG, to investigate the feasibility of long-term disease self-monitoring. Further, we hope our study will bring new insights into noninvasively collected personalized risk factors of seizure occurrence and seizure propensity that may help to mitigate one of the most difficult aspects of refractory epilepsy: the unpredictability of seizure occurrence. International Registered Report Identifier (IRRID) PRR1-10.2196/25309
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Affiliation(s)
- Andrea Biondi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Petroula Laiou
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Elisa Bruno
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Pedro F Viana
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.,Faculty of Medicine, University of Lisbon, Hospital de Santa Maria, Lisbon, Portugal
| | | | | | - Ewan Nurse
- Seer Medical Inc, Melbourne, Australia.,Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia
| | - Deb K Pal
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Mark P Richardson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
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21
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Duun-Henriksen J, Baud M, Richardson MP, Cook M, Kouvas G, Heasman JM, Friedman D, Peltola J, Zibrandtsen IC, Kjaer TW. A new era in electroencephalographic monitoring? Subscalp devices for ultra-long-term recordings. Epilepsia 2020; 61:1805-1817. [PMID: 32852091 DOI: 10.1111/epi.16630] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/16/2020] [Accepted: 07/05/2020] [Indexed: 12/21/2022]
Abstract
Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital- and home-based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud-based software. This new generation of ultra-long-term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra-long-term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.
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Affiliation(s)
- Jonas Duun-Henriksen
- Department of Basic & Clinical Neuroscience, King's College London, London, UK.,UNEEG medical, Lynge, Denmark
| | - Maxime Baud
- Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland.,Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
| | - Mark P Richardson
- Department of Basic & Clinical Neuroscience, King's College London, London, UK
| | - Mark Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia.,Epi-Minder, Melbourne, Victoria, Australia
| | - George Kouvas
- Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
| | | | - Daniel Friedman
- NYU Langone Comprehensive Epilepsy Center, New York, New York, USA
| | - Jukka Peltola
- Department of Neurology, Tampere University and Tampere University Hospital, Tampere, Finland
| | - Ivan C Zibrandtsen
- Center of Neurophysiology, Department of Neurology, Zealand University Hospital, Roskilde, Denmark
| | - Troels W Kjaer
- Center of Neurophysiology, Department of Neurology, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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