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Amin U, Nascimento FA, Karakis I, Schomer D, Benbadis SR. Normal variants and artifacts: Importance in EEG interpretation. Epileptic Disord 2023; 25:591-648. [PMID: 36938895 DOI: 10.1002/epd2.20040] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 02/15/2023] [Accepted: 02/15/2023] [Indexed: 03/21/2023]
Abstract
Overinterpretation of EEG is an important contributor to the misdiagnosis of epilepsy. For the EEG to have a high diagnostic value and high specificity, it is critical to recognize waveforms that can be mistaken for abnormal patterns. This article describes artifacts, normal rhythms, and normal patterns that are prone to being misinterpreted as abnormal. Artifacts are potentials generated outside the brain. They are divided into physiologic and extraphysiologic. Physiologic artifacts arise from the body and include EMG, eyes, various movements, EKG, pulse, and sweat. Some physiologic artifacts can be useful for interpretation such as EMG and eye movements. Extraphysiologic artifacts arise from outside the body, and in turn can be divided into the environments (electrodes, equipment, and cellphones) and devices within the body (pacemakers and neurostimulators). Normal rhythms can be divided into awake patterns (alpha rhythm and its variants, mu rhythm, lambda waves, posterior slow waves of youth, HV-induced slowing, photic driving, and photomyogenic response) and sleep patterns (POSTS, vertex waves, spindles, K complexes, sleep-related hypersynchrony, and frontal arousal rhythm). Breach can affect both awake and sleep rhythms. Normal variants or variants of uncertain clinical significance include variants that may have been considered abnormal in the early days of EEG but are now considered normal. These include wicket spikes and wicket rhythms (the most common normal pattern overread as epileptiform), small sharp spikes (aka benign epileptiform transients of sleep), rhythmic midtemporal theta of drowsiness (aka psychomotor variant), Cigánek rhythm (aka midline theta), 6 Hz phantom spike-wave, 14 and 6 Hz positive spikes, subclinical rhythmic epileptiform discharges of adults (SREDA), slow-fused transients, occipital spikes of blindness, and temporal slowing of the elderly. Correctly identifying artifacts and normal patterns can help avoid overinterpretation and misdiagnosis. This is an educational review paper addressing a learning objective of the International League Against Epilepsy (ILAE) curriculum.
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Affiliation(s)
- Ushtar Amin
- University of South Florida, Department of Neurology, Tampa, Florida, USA
| | - Fábio A Nascimento
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Ioannis Karakis
- Emory University School of Medicine - Neurology, Atlanta, Georgia, USA
| | - Donald Schomer
- Beth Israel Deaconess Medical Center, Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Selim R Benbadis
- University of South Florida, Department of Neurology, Tampa, Florida, USA
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2
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Djemal A, Bouchaala D, Fakhfakh A, Kanoun O. Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study. Bioengineering (Basel) 2023; 10:703. [PMID: 37370634 DOI: 10.3390/bioengineering10060703] [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: 05/04/2023] [Revised: 05/29/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles. For the experimental studies, first, compact wireless sensor nodes were developed for real-time measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps muscles on the right side and the left side. For the classification of the seizure, a machine learning model has been elaborated. The 16 common sEMG time-domain features were first extracted and examined with respect to discrimination and redundancy. This allowed the features to be classified into irrelevant features, important features, and redundant features. Redundant features were examined with the Big-O notation method and with the average execution time method to select the feature that leads to lower complexity and reduced processing time. The finally selected six features were explored using different machine learning classifiers to compare the resulting classification accuracy. The results show that the artificial neural network (ANN) model with the six features: IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.
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Affiliation(s)
- Achraf Djemal
- Measurement and Sensor Technology, Chemnitz University of Technology, Reichenhainer Straße 70, 09126 Chemnitz, Germany
- Laboratory of Signals, Systems, Artificial Intelligence and Networks, Digital Research Centre of Sfax, National School of Electronics and Telecommunications of Sfax, Technopole of Sfax, Ons City 3021, Tunisia
| | - Dhouha Bouchaala
- National Engineering School of Sfax, University of Sfax, Route de la Soukra km 4, Sfax 3038, Tunisia
| | - Ahmed Fakhfakh
- Laboratory of Signals, Systems, Artificial Intelligence and Networks, Digital Research Centre of Sfax, National School of Electronics and Telecommunications of Sfax, Technopole of Sfax, Ons City 3021, Tunisia
| | - Olfa Kanoun
- Measurement and Sensor Technology, Chemnitz University of Technology, Reichenhainer Straße 70, 09126 Chemnitz, Germany
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3
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Peltola ME, Leitinger M, Halford JJ, Vinayan KP, Kobayashi K, Pressler RM, Mindruta I, Mayor LC, Lauronen L, Beniczky S. Routine and sleep EEG: Minimum recording standards of the International Federation of Clinical Neurophysiology and the International League Against Epilepsy. Epilepsia 2023; 64:602-618. [PMID: 36762397 PMCID: PMC10006292 DOI: 10.1111/epi.17448] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/18/2022] [Accepted: 10/25/2022] [Indexed: 02/11/2023]
Abstract
This article provides recommendations on the minimum standards for recording routine ("standard") and sleep electroencephalography (EEG). The joint working group of the International Federation of Clinical Neurophysiology (IFCN) and the International League Against Epilepsy (ILAE) developed the standards according to the methodology suggested for epilepsy-related clinical practice guidelines by the Epilepsy Guidelines Working Group. We reviewed the published evidence using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. The quality of evidence for sleep induction methods was assessed by the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) method. A tool for Quality Assessment of Diagnostic Studies (QUADAS-2) was used to assess the risk of bias in technical and methodological studies. Where high-quality published evidence was lacking, we used modified Delphi technique to reach expert consensus. The GRADE system was used to formulate the recommendations. The quality of evidence was low or moderate. We formulated 16 consensus-based recommendations for minimum standards for recording routine and sleep EEG. The recommendations comprise the following aspects: indications, technical standards, recording duration, sleep induction, and provocative methods.
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Affiliation(s)
- Maria E Peltola
- HUS Diagnostic Center, Clinical Neurophysiology, Clinical Neurosciences, Epilepsia Helsinki, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Markus Leitinger
- Department of Neurology, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Jonathan J Halford
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | | | - Katsuhiro Kobayashi
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Ronit M Pressler
- Clinical Neuroscience, UCL-Great Ormond Street Institute of Child Health and Department of Clinical Neurophysiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Ioana Mindruta
- Department of Neurology, University Emergency Hospital of Bucharest and University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania
| | - Luis Carlos Mayor
- Department of Neurology, Hospital Universitario Fundacion Santa Fe de Bogota, Bogota, Colombia
| | - Leena Lauronen
- HUS Diagnostic Center, Clinical Neurophysiology, Clinical Neurosciences, Epilepsia Helsinki, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, and Danish Epilepsy Centre, Dianalund, Denmark
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4
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Peltola ME, Leitinger M, Halford JJ, Vinayan KP, Kobayashi K, Pressler RM, Mindruta I, Mayor LC, Lauronen L, Beniczky S. Routine and sleep EEG: Minimum recording standards of the International Federation of Clinical Neurophysiology and the International League Against Epilepsy. Clin Neurophysiol 2023; 147:108-120. [PMID: 36775678 DOI: 10.1016/j.clinph.2023.01.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
This article provides recommendations on the minimum standards for recording routine ("standard") and sleep electroencephalography (EEG). The joint working group of the International Federation of Clinical Neurophysiology (IFCN) and the International League Against Epilepsy (ILAE) developed the standards according to the methodology suggested for epilepsy-related clinical practice guidelines by the Epilepsy Guidelines Working Group. We reviewed the published evidence using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. The quality of evidence for sleep induction methods was assessed by the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) method. A tool for Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the risk of bias in technical and methodological studies. Where high-quality published evidence was lacking, we used modified Delphi technique to reach expert consensus. The GRADE system was used to formulate the recommendations. The quality of evidence was low or moderate. We formulated 16 consensus-based recommendations for minimum standards for recording routine and sleep EEG. The recommendations comprise the following aspects: indications, technical standards, recording duration, sleep induction, and provocative methods.
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Affiliation(s)
- Maria E Peltola
- HUS Diagnostic Center, Clinical Neurophysiology, Clinical Neurosciences, Epilepsia Helsinki, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
| | - Markus Leitinger
- Department of Neurology, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Jonathan J Halford
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
| | | | - Katsuhiro Kobayashi
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Ronit M Pressler
- Clinical Neuroscience, UCL-Great Ormond Street Institute of Child Health and Department of Clinical Neurophysiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Ioana Mindruta
- Department of Neurology, University Emergency Hospital of Bucharest and University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania
| | - Luis Carlos Mayor
- Department of Neurology, Hospital Universitario Fundacion Santa Fe de Bogota, Bogota, Colombia
| | - Leena Lauronen
- HUS Diagnostic Center, Clinical Neurophysiology, Clinical Neurosciences, Epilepsia Helsinki, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, and Danish Epilepsy Centre, Dianalund, Denmark
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Vlachou M, Ryvlin P, Arbune AA, Armand Larsen S, Skraep Sidaros A, Cacic Hribljan M, Fabricius M, Beniczky S. Progressive slowing of clonic phase predicts postictal generalized EEG suppression. Epilepsia 2022; 63:3204-3211. [PMID: 36208032 PMCID: PMC10092045 DOI: 10.1111/epi.17434] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/06/2022] [Accepted: 10/06/2022] [Indexed: 01/11/2023]
Abstract
OBJECTIVE Postictal generalized electroencephalography (EEG) suppression (PGES) is a surrogate marker of sudden unexpected death in epilepsy (SUDEP). It is still unclear which ictal phenomena lead to prolonged PGES and increased risk of SUDEP. Semiology features of generalized convulsive seizure (GCS type 1) have been reported as a predictor of prolonged PGES. Progressive slowing of clonic phase (PSCP) has been observed in GCSs, with gradually increasing inhibitory periods interrupting the tonic contractions. We hypothesized that PSCP is associated with prolonged PGES. METHODS We analyzed 90 bilateral convulsive seizures in 50 consecutive patients (21 female; age: 11-62 years, median: 31 years) recruited to video-EEG monitoring. Five raters, blinded to all other data, independently assessed the presence of PSCP. PGES and seizure semiology were evaluated independently. We determined inter-rater agreement (IRA) for the presence of PSCP, and we evaluated its association, as well as that of other ictal features, with the occurrence of PGES, prolonged PGES (≥20 s) and very prolonged PGES (≥50 s) using multivariate logistic regression analysis. RESULTS We found substantial IRA for the presence of PSCP (percent agreement: 80%; beyond-chance agreement coefficient: .655). PSCP was an independent predictor of the occurrence of PGES and prolonged PGES (p < .001). All seizures with very prolonged PGES had PSCP. GCS type 1 was an independent predictor of occurrence of PGES (p = .02) and prolonged PGES (p = .03) but not of very prolonged PGES. Only half of the seizures with very prolonged PGES were GCS type 1. SIGNIFICANCE PSCP predicts prolonged PGES, emphasizing the importance of gradually increasing inhibitory phenomena at the end of the seizures. Our findings shed more light on the ictal phenomena leading to increased risk of SUDEP. These phenomena may provide basis for algorithms implemented into wearable devices for identifying GCS with increased risk of SUDEP.
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Affiliation(s)
- Maria Vlachou
- Department of Clinical Neurophysiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
| | - Anca Adriana Arbune
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.,Neurology Clinic, Fundeni Clinical Institute, Bucharest, Romania
| | - Sidsel Armand Larsen
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
| | - Annette Skraep Sidaros
- Department of Clinical Neurophysiology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Melita Cacic Hribljan
- Department of Clinical Neurophysiology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Martin Fabricius
- Department of Clinical Neurophysiology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
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Duncan AJ, Peric I, Boston R, Seneviratne U. Predictive semiology of psychogenic non-epileptic seizures in an epilepsy monitoring unit. J Neurol 2021; 269:2172-2178. [PMID: 34550469 PMCID: PMC8456070 DOI: 10.1007/s00415-021-10805-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/12/2021] [Accepted: 09/13/2021] [Indexed: 11/25/2022]
Abstract
Introduction The diagnosis of psychogenic nonepileptic seizures (PNES) is a common clinical dilemma. We sought to assess the diagnostic value of four ictal signs commonly used in differentiating PNES from epileptic seizures (ES). Methods We retrospectively reviewed consecutive adult video-electroencephalogram (VEM) studies conducted at a single tertiary epilepsy center between May 2009 and August 2016. Each event was assessed by a blinded rater for the presence of four signs: fluctuating course, head shaking, hip thrusting, and back arching. The final diagnosis of PNES or ES was established for each event based on clinical and VEM characteristics. All ES were pooled regardless of focal or generalized onset. We analyzed the odds ratio of each sign in PNES in comparison to ES with adjustment for repeated measures using logistic regression. Additionally, we calculated the sensitivity, specificity, predictive values, and likelihood ratios of each sign to diagnose PNES. Results A total of 742 events from 140 VEM studies were assessed. Fluctuating course (odds ratio (OR) 37.37, 95% confidence interval (CI) 13.56–102.96, P < 0.0001), head shaking (OR 2.95, 95% CI 1.26–6.79, P = 0.012), and hip thrusting (OR 4.28, 95% CI 1.21–15.18, P = 0.02) were each significantly predictive of PNES. Fluctuating course had the highest sensitivity (76.16%). Back arching (OR 1.06, 95% CI 0.35–3.20, P = 0.92) was not significantly associated with PNES. Conclusion Fluctuating course, head shaking, and hip thrusting are semiological features significantly more common in PNES than ES. Fluctuating course is the most reliable sign. Back arching does not appear to differentiate PNES from ES.
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Affiliation(s)
- Andrew J Duncan
- Department of Clinical Neurosciences, St. Vincent's Hospital Melbourne, Melbourne, VIC, Australia.
| | - Ivana Peric
- Department of Neurology, Monash Medical Centre, Melbourne, VIC, Australia
| | - Ray Boston
- Department of Clinical Neurosciences, St. Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- Department of Clinical Studies, New Bolton Center, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, USA
| | - Udaya Seneviratne
- Department of Clinical Neurosciences, St. Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- Department of Neurology, Monash Medical Centre, Melbourne, VIC, Australia
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7
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Husain AM, Towne AR, Chen DK, Whitmire LE, Voyles SR, Cardenas DP. Differentiation of Epileptic and Psychogenic Nonepileptic Seizures Using Single-Channel Surface Electromyography. J Clin Neurophysiol 2021; 38:432-438. [PMID: 32501944 DOI: 10.1097/wnp.0000000000000703] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) are difficult to differentiate when based on a patient's self-reported symptoms. This study proposes review of objective data captured by a surface electromyography (sEMG) wearable device for classification of events as ES or PNES. This may help clinicians accurately identify ES and PNES. METHODS Seventy-one subjects were prospectively enrolled across epilepsy monitoring units at VA Epilepsy Centers of Excellence. Subjects were concomitantly monitored using video EEG and a wearable sEMG epilepsy monitor, the Sensing Portable sEmg Analysis Characterization (SPEAC) System. Three epileptologists independently classified ES and PNES that contained upper extremity motor activity based on video EEG. The sEMG data from those events were automatically processed to provide a seizure score for event classification. After brief training (60 minutes), the sEMG data were reviewed by a separate group of four epileptologists to independently classify events as ES or PNES. RESULTS According to video EEG review, 17 subjects experienced 34 events (15 ES and 19 PNES with upper extremity motor activity). The automated process correctly classified 87% of ES (positive predictive value = 88%, negative predictive value = 76%) and 79% of PNES, and the expert reviewers correctly classified 77% of ES (positive predictive value = 94%, negative predictive value = 84%) and 96% of PNES. The automated process and the expert reviewers correctly classified 100% of tonic-clonic seizures as ES, and 71 and 50%, respectively, of non-tonic-clonic ES. CONCLUSIONS Automated and expert review, particularly in combination, of sEMG captured by a wearable seizure monitor (SPEAC System) may be able to differentiate ES (especially tonic-clonic) and PNES with upper extremity motor activity.
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Affiliation(s)
- Aatif M Husain
- Department of Neurology, Duke University Medical Center, Durham, North Carolina, U.S.A
- Neurosciences Medicine, Duke Clinical Research Institute, Durham, North Carolina, U.S.A
- Neurodiagnostic Center, Veterans Affairs Medical Center Neuroscience Medicine, Durham, North Carolina, U.S.A
| | - Alan R Towne
- Virginia Commonwealth University, Richmond, Virginia, U.S.A
- Department of Veterans Affairs, Northeast Epilepsy Center of Excellence, Hunter Holmes McGuire Veterans Affairs Medical Center, Richmond, Virginia, U.S.A
| | - David K Chen
- Department of Neurology, Baylor College of Medicine, Southwest Epilepsy Center of Excellence, Michael E. DeBakey VA Medical Center, Houston, Texas, U.S.A.; and
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Baumgartner C, Whitmire LE, Voyles SR, Cardenas DP. Using sEMG to identify seizure semiology of motor seizures. Seizure 2021; 86:52-59. [PMID: 33550134 DOI: 10.1016/j.seizure.2020.11.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/20/2020] [Accepted: 11/19/2020] [Indexed: 11/18/2022] Open
Abstract
PURPOSE Accurate characterization and quantification of seizure types are critical for optimal pharmacotherapy in epilepsy patients. Technological advances have made it possible to continuously monitor physiological signals within or outside the hospital setting. This study tested the utility of single-channel surface-electromyography (sEMG) for characterization of motor epileptic seizure semiology. METHODS Seventy-one subjects were prospectively enrolled where vEEG and sEMG were simultaneously recorded. Three epileptologists independently identified and classified seizure events with upper-extremity (UE) motor activity by reviewing vEEG, serving as a clinical standard. Surface EMG recorded during the events identified by the clinical standard were evaluated using automated classification methods and expert review by a second group of three independent epileptologists (blinded to the vEEG data). Surface EMG classification categories included: tonic-clonic (TC), tonic only, clonic only, or other motor seizures. Both automated and expert review of sEMG was compared to the clinical standard. RESULTS Twenty subjects experienced 47 motor seizures. Automated sEMG event classification methods accurately classified 72 % (95 % CI [0.57, 0.84]) of events (15/18 TC seizures, 5/9 tonic seizures, 1/3 clonic seizures, and 13/17 other seizures). Three independent reviewers' majority-rule analysis of sEMG correctly classified 81 % (95 % CI [0.67, 0.91]) of events (16/18 TC seizures, 8/9 tonic seizures, 1/3 clonic seizures, and 13/17 other manifestations). CONCLUSIONS Continuous monitoring of sEMG data provides an objective measure to evaluate motor seizure activity. Using sEMG from a wearable monitor recorded from the biceps, automated and expert review may be used to characterize the semiology of events with UE motor activity, particularly TC and tonic seizures.
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Affiliation(s)
- Christoph Baumgartner
- Department of Neurology, General Hospital Hietzing With Neurological Center Rosenhügel, Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Medical Faculty, Sigmund Freud University, Vienna, Austria
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Lhatoo SD, Bernasconi N, Blumcke I, Braun K, Buchhalter J, Denaxas S, Galanopoulou A, Josephson C, Kobow K, Lowenstein D, Ryvlin P, Schulze-Bonhage A, Sahoo SS, Thom M, Thurman D, Worrell G, Zhang GQ, Wiebe S. Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy. Epilepsia 2020; 61:1869-1883. [PMID: 32767763 DOI: 10.1111/epi.16633] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 07/07/2020] [Accepted: 07/08/2020] [Indexed: 12/25/2022]
Abstract
Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.
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Affiliation(s)
- Samden D Lhatoo
- University of Texas Health Sciences Center at Houston, Houston, Texas
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Ingmar Blumcke
- Friedrich-Alexander University Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Kees Braun
- Department of Child Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jeffrey Buchhalter
- Department of Neurology, St Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | - Aristea Galanopoulou
- Saul Korey Department of Neurology, Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York
| | - Colin Josephson
- Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
| | - Katja Kobow
- Friedrich-Alexander University Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Daniel Lowenstein
- Department of Neurology, University of California, San Francisco, San Francisco, California
| | - Philippe Ryvlin
- Department of Neurosciences, University of Lausanne, Lausanne, Switzerland
| | | | - Satya S Sahoo
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Maria Thom
- Institute of Neurology, University College London, London, UK
| | | | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Guo-Qiang Zhang
- University of Texas Health Sciences Center at Houston, Houston, Texas
| | - Samuel Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
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10
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Beniczky S, Karoly P, Nurse E, Ryvlin P, Cook M. Machine learning and wearable devices of the future. Epilepsia 2020; 62 Suppl 2:S116-S124. [PMID: 32712958 DOI: 10.1111/epi.16555] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/05/2020] [Accepted: 05/08/2020] [Indexed: 01/06/2023]
Abstract
Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Philippa Karoly
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
| | - Ewan Nurse
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, CHUV, Lausanne, Switzerland
| | - Mark Cook
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
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11
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Beniczky S, Arbune AA, Jeppesen J, Ryvlin P. Biomarkers of seizure severity derived from wearable devices. Epilepsia 2020; 61 Suppl 1:S61-S66. [PMID: 32519759 DOI: 10.1111/epi.16492] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 03/05/2020] [Accepted: 03/05/2020] [Indexed: 11/28/2022]
Abstract
Besides triggering alarms, wearable seizure detection devices record a variety of biosignals that represent biomarkers of seizure severity. There is a need for automated seizure characterization, to identify high-risk seizures. Wearable devices can automatically identify seizure types with the highest associated morbidity and mortality (generalized tonic-clonic seizures), quantify their duration and frequency, and provide data on postictal position and immobility, autonomic changes derived from electrocardiography/heart rate variability, electrodermal activity, respiration, and oxygen saturation. In this review, we summarize how these biosignals reflect seizure severity, and how they can be monitored in the ambulatory outpatient setting using wearable devices. Multimodal recording of these biosignals will provide valuable information for individual risk assessment, as well as insights into the mechanisms and prevention of sudden unexpected death in epilepsy.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Anca A Arbune
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Neurosciences, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Jesper Jeppesen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Vaud University Hospital Center, Lausanne, Switzerland
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12
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Arbune AA, Conradsen I, Cardenas DP, Whitmire LE, Voyles SR, Wolf P, Lhatoo S, Ryvlin P, Beniczky S. Ictal quantitative surface electromyography correlates with postictal EEG suppression. Neurology 2020; 94:e2567-e2576. [PMID: 32398358 PMCID: PMC7455333 DOI: 10.1212/wnl.0000000000009492] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 12/05/2019] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment. METHODS Quantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES. RESULTS We found significant correlations between quantitative surface EMG parameters and the duration of PGES (p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%. CONCLUSIONS Ictal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression.
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Affiliation(s)
- Anca A Arbune
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Isa Conradsen
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Damon P Cardenas
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Luke E Whitmire
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Shannon R Voyles
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Peter Wolf
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Samden Lhatoo
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Philippe Ryvlin
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Sándor Beniczky
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark.
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Abstract
Although the EEG is designed to record cerebral activity, it also frequently records activity from extracerebral sources, leading to artifact. Differentiating rhythmical artifact from true electrographic ictal activity remains a substantial challenge to even experienced electroencephalographers because the sources of artifact able to mimic ictal activity on EEG have continued to increase with the advent of technology. Knowledge of the characteristics of the polarity and physiologic electrical fields of the brain, as opposed to those generated by the eyes, heart, and muscles, allows the electroencephalographer to intuitively recognize noncerebrally generated waveforms. In this review, we provide practical guidelines for the EEG interpreter to correctly identify physiologic and nonphysiologic artifacts capable of mimicking electrographic seizures. In addition, we further elucidate the common pitfalls in artifact interpretation and the costly impact of epilepsy misdiagnosis due to artifact.
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Naganur VD, Kusmakar S, Chen Z, Palaniswami MS, Kwan P, O'Brien TJ. The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non-epileptic seizures. Epilepsia Open 2019; 4:309-317. [PMID: 31168498 PMCID: PMC6546070 DOI: 10.1002/epi4.12327] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 04/14/2019] [Accepted: 04/22/2019] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE Accurate differentiation between epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) can be challenging based on history alone. Inpatient video EEG monitoring (VEM) is often needed for a definitive diagnosis. However, VEM is highly resource intensive, is of limited availability, and cannot be undertaken over long periods. Previous research has shown that time-frequency analysis of accelerometer data could be utilized to differentiate between ES and PNES. Using a seizure detection and classification algorithm, we sought to examine the diagnostic utility of an automated analysis with an ambulatory accelerometer. METHODS A wrist-worn device was used to collect accelerometer data from patients during VEM admission, for diagnostic evaluation of convulsive seizures. An automated process, that involved the use of K-means clustering and support vector machines, was used to detect and classify each seizure as ES or PNES. The results were compared with VEM diagnoses determined by epileptologists blinded to the accelerometer data. RESULTS Twenty-four convulsive seizures, consisting of at least 20 seconds of sustained continuous activity, recorded from 11 patients during inpatient VEM (13 PNES from five patients and 11 ES from six patients) were included for analysis. The automated system detected all convulsive seizures (ES, PNES) from >661 hours of recording with 67 false alarms (2.4 per 24 hours). The sensitivity and specificity for classifying ES from PNES were 72.7% and 100%, respectively. The positive and negative predictive values for classifying PNES were 81.3% and 100%, respectively. There was no significant difference between the classification results obtained from the automation process and the VEM diagnoses. SIGNIFICANCE This automated system can potentially provide a wearable out-of-hospital seizure diagnostic monitoring system.
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Affiliation(s)
- Vaidehi D. Naganur
- Departments of Neurology and MedicineThe Melbourne Brain Centre, The Royal Melbourne HospitalParkvilleVictoriaAustralia
| | - Shitanshu Kusmakar
- Department of Electrical EngineeringThe University of MelbourneParkvilleVictoriaAustralia
| | - Zhibin Chen
- Department of Electrical EngineeringThe University of MelbourneParkvilleVictoriaAustralia
| | | | - Patrick Kwan
- Departments of Neurology and MedicineThe Melbourne Brain Centre, The Royal Melbourne HospitalParkvilleVictoriaAustralia
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
| | - Terence J. O'Brien
- Departments of Neurology and MedicineThe Melbourne Brain Centre, The Royal Melbourne HospitalParkvilleVictoriaAustralia
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
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15
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Li Y, Matzka L, Flahive J, Weber D. Potential use of leukocytosis and anion gap elevation in differentiating psychogenic nonepileptic seizures from epileptic seizures. Epilepsia Open 2019; 4:210-215. [PMID: 30868134 PMCID: PMC6398111 DOI: 10.1002/epi4.12301] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 12/28/2018] [Indexed: 12/28/2022] Open
Abstract
Epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) can be difficult to differentiate from each other in the emergency department (ED) setting. We have previously shown that the anion gap (AG) can help differentiate between ES and PNES in the ED. In this study, we explored whether additionally considering leukocytosis can help better differentiate between ES and PNES. We screened a total of 1354 subjects seen in the ED of a tertiary care medical center; 27 PNES and 27 ES patients were identified based on clinical description and subsequent electroencephalography (EEG). Multivariable logistic regression analysis was used to model the association between ES, leukocytosis, and AG. Our results indicated that within 9 hours after the index event, serum AG (adjusted odds ratio [aOR] 2.07) and white blood cell (WBC) count (aOR 1.61) were both independently associated with ES. We derived an equation to help differentiate between ES and PNES: 1.5*AG+WBC. A score >24.8 indicated a >90% likelihood of ES. A score <15.5 indicated a <10% likelihood of ES (ie, the alternate diagnosis of PNES should be considered). This study for the first time provides evidence to help differentiate PNES and ES utilizing acidosis and leukocytosis.
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Affiliation(s)
- Yi Li
- Department of NeurologyUniversity of Massachusetts Medical SchoolWorcesterMassachusetts
- Department of NeurologyStanford UniversityStanfordCalifornia
| | - Liesl Matzka
- Department of NeurologyUniversity of Massachusetts Medical SchoolWorcesterMassachusetts
| | - Julie Flahive
- Quantitative Health SciencesUniversity of Massachusetts Medical SchoolWorcesterMassachusetts
| | - Daniel Weber
- Department of NeurologyUniversity of Massachusetts Medical SchoolWorcesterMassachusetts
- Department of NeurologySaint Louis University School of MedicineSt. LouisMissouri
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16
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Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device. IEEE Trans Biomed Eng 2019; 66:421-432. [DOI: 10.1109/tbme.2018.2845865] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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17
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Beniczky S, Conradsen I, Wolf P. Detection of convulsive seizures using surface electromyography. Epilepsia 2018; 59 Suppl 1:23-29. [PMID: 29873829 DOI: 10.1111/epi.14048] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/22/2017] [Indexed: 02/04/2023]
Abstract
Bilateral (generalized) tonic-clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low- and high-frequency signal components. Algorithms targeting increase in high-frequency EMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large-scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%-100%). The rate of false alarms (0.7-2.5/24 h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG-based seizure detection.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Peter Wolf
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Medicine, Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil
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18
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Kusmakar S, Karmakar C, Yan B, Muthuganapathy R, Kwan P, O'Brien TJ, Palaniswami MS. Novel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures. Epilepsia 2018; 60:165-174. [PMID: 30536390 DOI: 10.1111/epi.14619] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 11/12/2018] [Accepted: 11/13/2018] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To investigate the characteristics of motor manifestation during convulsive epileptic and psychogenic nonepileptic seizures (PNES), captured using a wrist-worn accelerometer (ACM) device. The main goal was to find quantitative ACM features that can differentiate between convulsive epileptic and convulsive PNES. METHODS In this study, motor data were recorded using wrist-worn ACM-based devices. A total of 83 clinical events were recorded: 39 generalized tonic-clonic seizures (GTCS) from 12 patients with epilepsy, and 44 convulsive PNES from 7 patients (one patient had both GTCS and PNES). The temporal variations in the ACM traces corresponding to 39 GTCS and 44 convulsive PNES events were extracted using Poincaré maps. Two new indices-tonic index (TI) and dispersion decay index (DDI)-were used to quantify the Poincaré-derived temporal variations for every GTCS and convulsive PNES event. RESULTS The TI and DDI of Poincaré-derived temporal variations for GTCS events were higher in comparison to convulsive PNES events (P < 0.001). The onset and the subsiding patterns captured by TI and DDI differentiated between epileptic and convulsive nonepileptic seizures. An automated classifier built using TI and DDI of Poincaré-derived temporal variations could correctly differentiate 42 (sensitivity: 95.45%) of 44 convulsive PNES events and 37 (specificity: 94.87%) of 39 GTCS events. A blinded review of the Poincaré-derived temporal variations in GTCS and convulsive PNES by epileptologists differentiated 26 (sensitivity: 70.27%) of 44 PNES events and 33 (specificity: 86.84%) of 39 GTCS events correctly. SIGNIFICANCE In addition to quantifying the motor manifestation mechanism of GTCS and convulsive PNES, the proposed approach also has diagnostic significance. The new ACM features incorporate clinical characteristics of GTCS and PNES, thus providing an accurate, low-cost, and practical alternative to differential diagnosis of PNES.
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Affiliation(s)
- Shitanshu Kusmakar
- Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Chandan Karmakar
- Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,School of Information Technology, Deakin University, Geelong, Victoria, Australia
| | - Bernard Yan
- Melbourne Brain Centre, Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | | | - Patrick Kwan
- Melbourne Brain Centre, Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences and Neurology, The Central Clinical School, Alfred Hospital, Monash University, Melbourne, Victoria, Australia.,Department of Medicine and Neurology, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Terence J O'Brien
- Melbourne Brain Centre, Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences and Neurology, The Central Clinical School, Alfred Hospital, Monash University, Melbourne, Victoria, Australia.,Department of Medicine and Neurology, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Marimuthu Swami Palaniswami
- Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Victoria, Australia
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Zhao X, Lhatoo SD. Seizure detection: do current devices work? And when can they be useful? Curr Neurol Neurosci Rep 2018; 18:40. [PMID: 29796939 DOI: 10.1007/s11910-018-0849-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW The unpredictability and apparent randomness of epileptic seizures is one of the most vexing aspects of epilepsy. Methods or devices capable of detecting seizures may help prevent injury or even death and significantly improve quality of life. Here, we summarize and evaluate currently available, unimodal, or polymodal detection systems for epileptic seizures, mainly in the ambulatory setting. RECENT FINDINGS There are two broad categories of detection devices: EEG-based and non-EEG-based systems. Wireless wearable EEG devices are now available both in research and commercial arenas. Neuro-stimulation devices are currently evolving and initial experiences of these show potential promise. As for non-EEG devices, different detecting systems show different sensitivity according to the different patient and seizure types. Regardless, when used in combination, these modalities may complement each other to increase positive predictive value. Although some devices with high sensitivity are promising, practical widespread use of such detection systems is still some way away. More research and experience are needed to evaluate the most efficient and integrated systems, to allow for better approaches to detection and prediction of seizures. The concept of closed-loop systems and prompt intervention may substantially improve quality of life for patients and carers.
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Affiliation(s)
- Xiuhe Zhao
- Epilepsy Center, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA.,Neurology Department, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
| | - Samden D Lhatoo
- Epilepsy Center, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA. .,NIH/NINDS Center for SUDEP Research, Boston, MA, USA.
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20
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Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol 2018; 17:279-288. [DOI: 10.1016/s1474-4422(18)30038-3] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 12/05/2017] [Accepted: 12/06/2017] [Indexed: 12/24/2022]
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Beniczky S, Conradsen I, Henning O, Fabricius M, Wolf P. Automated real-time detection of tonic-clonic seizures using a wearable EMG device. Neurology 2018; 90:e428-e434. [PMID: 29305441 PMCID: PMC5791791 DOI: 10.1212/wnl.0000000000004893] [Citation(s) in RCA: 98] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 10/09/2017] [Indexed: 11/15/2022] Open
Abstract
Objective To determine the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) using a wearable surface EMG device. Methods We prospectively tested the technical performance and diagnostic accuracy of real-time seizure detection using a wearable surface EMG device. The seizure detection algorithm and the cutoff values were prespecified. A total of 71 patients, referred to long-term video-EEG monitoring, on suspicion of GTCS, were recruited in 3 centers. Seizure detection was real-time and fully automated. The reference standard was the evaluation of video-EEG recordings by trained experts, who were blinded to data from the device. Reading the seizure logs from the device was done blinded to all other data. Results The mean recording time per patient was 53.18 hours. Total recording time was 3735.5 hours, and device deficiency time was 193 hours (4.9% of the total time the device was turned on). No adverse events occurred. The sensitivity of the wearable device was 93.8% (30 out of 32 GTCS were detected). Median seizure detection latency was 9 seconds (range −4 to 48 seconds). False alarm rate was 0.67/d. Conclusions The performance of the wearable EMG device fulfilled the requirements of patients: it detected GTCS with a sensitivity exceeding 90% and detection latency within 30 seconds. Classification of evidence This study provides Class II evidence that for people with a history of GTCS, a wearable EMG device accurately detects GTCS (sensitivity 93.8%, false alarm rate 0.67/d).
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Affiliation(s)
- Sándor Beniczky
- From the Department of Clinical Neurophysiology (S.B.), Danish Epilepsy Centre Dianalund and Aarhus University Hospital; FORCE Technology (I.C.), Hørsholm, Denmark; Department of Clinical Neurophysiology (O.H.), National Centre for Epilepsy, Oslo University Hospital, Norway; Department of Clinical Neurophysiology (M.F.), Copenhagen University Hospital Rigshospitalet; Department of Neurology (P.W.), Danish Epilepsy Centre, Dianalund, Denmark; and Postgraduate Programme in Clinical Medicine (P.W.), Federal University of Santa Catarina, Florianópolis, Brazil.
| | - Isa Conradsen
- From the Department of Clinical Neurophysiology (S.B.), Danish Epilepsy Centre Dianalund and Aarhus University Hospital; FORCE Technology (I.C.), Hørsholm, Denmark; Department of Clinical Neurophysiology (O.H.), National Centre for Epilepsy, Oslo University Hospital, Norway; Department of Clinical Neurophysiology (M.F.), Copenhagen University Hospital Rigshospitalet; Department of Neurology (P.W.), Danish Epilepsy Centre, Dianalund, Denmark; and Postgraduate Programme in Clinical Medicine (P.W.), Federal University of Santa Catarina, Florianópolis, Brazil
| | - Oliver Henning
- From the Department of Clinical Neurophysiology (S.B.), Danish Epilepsy Centre Dianalund and Aarhus University Hospital; FORCE Technology (I.C.), Hørsholm, Denmark; Department of Clinical Neurophysiology (O.H.), National Centre for Epilepsy, Oslo University Hospital, Norway; Department of Clinical Neurophysiology (M.F.), Copenhagen University Hospital Rigshospitalet; Department of Neurology (P.W.), Danish Epilepsy Centre, Dianalund, Denmark; and Postgraduate Programme in Clinical Medicine (P.W.), Federal University of Santa Catarina, Florianópolis, Brazil
| | - Martin Fabricius
- From the Department of Clinical Neurophysiology (S.B.), Danish Epilepsy Centre Dianalund and Aarhus University Hospital; FORCE Technology (I.C.), Hørsholm, Denmark; Department of Clinical Neurophysiology (O.H.), National Centre for Epilepsy, Oslo University Hospital, Norway; Department of Clinical Neurophysiology (M.F.), Copenhagen University Hospital Rigshospitalet; Department of Neurology (P.W.), Danish Epilepsy Centre, Dianalund, Denmark; and Postgraduate Programme in Clinical Medicine (P.W.), Federal University of Santa Catarina, Florianópolis, Brazil
| | - Peter Wolf
- From the Department of Clinical Neurophysiology (S.B.), Danish Epilepsy Centre Dianalund and Aarhus University Hospital; FORCE Technology (I.C.), Hørsholm, Denmark; Department of Clinical Neurophysiology (O.H.), National Centre for Epilepsy, Oslo University Hospital, Norway; Department of Clinical Neurophysiology (M.F.), Copenhagen University Hospital Rigshospitalet; Department of Neurology (P.W.), Danish Epilepsy Centre, Dianalund, Denmark; and Postgraduate Programme in Clinical Medicine (P.W.), Federal University of Santa Catarina, Florianópolis, Brazil
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22
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Kusmakar S, Muthuganapathy R, Yan B, O'Brien TJ, Palaniswami M. Gaussian mixture model for the identification of psychogenic non-epileptic seizures using a wearable accelerometer sensor. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:1006-1009. [PMID: 28268494 DOI: 10.1109/embc.2016.7590872] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Any abnormal hypersynchronus activity of neurons can be characterized as an epileptic seizure (ES). A broad class of non-epileptic seizures is comprised of Psychogenic non-epileptic seizures (PNES). PNES are paroxysmal events, which mimics epileptic seizures and pose a diagnostic challenge with epileptic seizures due to their clinical similarities. The diagnosis of PNES is done using video-electroencephalography (VEM) monitoring. VEM being a resource intensive process calls for alternative methods for detection of PNES. There is now an emerging interest in the use of accelerometer based devices for the detection of seizures. In this work, we present an algorithm based on Gaussian mixture model (GMM's) for the identification of PNES, ES and normal movements using a wrist-worn accelerometer device. Features in time, frequency and wavelet domain are extracted from the norm of accelerometry signal. All events are then classified into three classes i.e normal, PNES and ES using a parametric estimate of the multivariate normal probability density function. An algorithm based on GMM's allows us to accurately model the non-epileptic and epileptic movements, thus enhancing the overall predictive accuracy of the system. The new algorithm was tested on data collected from 16 patients and showed an overall detection accuracy of 91% with 25 false alarms.
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23
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Whitehead K, Kane N, Wardrope A, Kandler R, Reuber M. Proposal for best practice in the use of video-EEG when psychogenic non-epileptic seizures are a possible diagnosis. Clin Neurophysiol Pract 2017; 2:130-139. [PMID: 30214985 PMCID: PMC6123876 DOI: 10.1016/j.cnp.2017.06.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 05/31/2017] [Accepted: 06/02/2017] [Indexed: 11/24/2022] Open
Abstract
The gold-standard for the diagnosis of psychogenic non-epileptic seizures (PNES) is capturing an attack with typical semiology and lack of epileptic ictal discharges on video-EEG. Despite the importance of this diagnostic test, lack of standardisation has resulted in a wide variety of protocols and reporting practices. The goal of this review is to provide an overview of research findings on the diagnostic video-EEG procedure, in both the adult and paediatric literature. We discuss how uncertainties about the ethical use of suggestion can be resolved, and consider what constitutes best clinical practice. We stress the importance of ictal observation and assessment and consider how diagnostically useful information is best obtained. We also discuss the optimal format of video-EEG reports; and of highlighting features with high sensitivity and specificity to reduce the risk of miscommunication. We suggest that over-interpretation of the interictal EEG, and the failure to recognise differences between typical epileptic and nonepileptic seizure manifestations are the greatest pitfalls in neurophysiological assessment of patients with PNES. Meanwhile, under-recognition of semiological pointers towards frontal lobe seizures and of the absence of epileptiform ictal EEG patterns during some epileptic seizure types (especially some seizures not associated with loss of awareness), may lead to erroneous PNES diagnoses. We propose that a standardised approach to the video-EEG examination and the subsequent written report will facilitate a clear communication of its import, improving diagnostic certainty and thereby promoting appropriate patient management.
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Affiliation(s)
- Kimberley Whitehead
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Nick Kane
- Grey Walter Department of Clinical Neurophysiology, North Bristol NHS Trust, Bristol, UK
| | | | - Ros Kandler
- Department of Clinical Neurophysiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Markus Reuber
- Academic Neurology Unit, University of Sheffield, Sheffield, UK
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24
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Bauer PR, Thijs RD, Lamberts RJ, Velis DN, Visser GH, Tolner EA, Sander JW, Lopes da Silva FH, Kalitzin SN. Dynamics of convulsive seizure termination and postictal generalized EEG suppression. Brain 2017; 140:655-668. [PMID: 28073789 DOI: 10.1093/brain/aww322] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 10/31/2016] [Indexed: 12/21/2022] Open
Abstract
It is not fully understood how seizures terminate and why some seizures are followed by a period of complete brain activity suppression, postictal generalized EEG suppression. This is clinically relevant as there is a potential association between postictal generalized EEG suppression, cardiorespiratory arrest and sudden death following a seizure. We combined human encephalographic seizure data with data of a computational model of seizures to elucidate the neuronal network dynamics underlying seizure termination and the postictal generalized EEG suppression state. A multi-unit computational neural mass model of epileptic seizure termination and postictal recovery was developed. The model provided three predictions that were validated in EEG recordings of 48 convulsive seizures from 48 subjects with refractory focal epilepsy (20 females, age range 15-61 years). The duration of ictal and postictal generalized EEG suppression periods in human EEG followed a gamma probability distribution indicative of a deterministic process (shape parameter 2.6 and 1.5, respectively) as predicted by the model. In the model and in humans, the time between two clonic bursts increased exponentially from the start of the clonic phase of the seizure. The terminal interclonic interval, calculated using the projected terminal value of the log-linear fit of the clonic frequency decrease was correlated with the presence and duration of postictal suppression. The projected terminal interclonic interval explained 41% of the variation in postictal generalized EEG suppression duration (P < 0.02). Conversely, postictal generalized EEG suppression duration explained 34% of the variation in the last interclonic interval duration. Our findings suggest that postictal generalized EEG suppression is a separate brain state and that seizure termination is a plastic and autonomous process, reflected in increased duration of interclonic intervals that determine the duration of postictal generalized EEG suppression.
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Affiliation(s)
- Prisca R Bauer
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Roland D Thijs
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.,Department of Neurology, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Robert J Lamberts
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Demetrios N Velis
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Gerhard H Visser
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Else A Tolner
- Department of Neurology, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Josemir W Sander
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.,Epilepsy Society, Chalfont St Peter SL9 0RJ, UK
| | - Fernando H Lopes da Silva
- Center of Neurosciences, Swammerdam Institute of Life Sciences, University of Amsterdam, P.O. Box 94215 1090 GE, The Netherlands.,Instituto Superior Técnico, University of Lisbon, 1049-001, Lisbon, Portugal
| | - Stiliyan N Kalitzin
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,Image Sciences Institute, University Medical Center Utrecht, P.O. Box 85500, 3508 GA Utrecht, The Netherlands
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25
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Şerban CA, Barborică A, Roceanu AM, Mîndruță IR, Ciurea J, Zăgrean AM, Zăgrean L, Moldovan M. EEG Assessment of Consciousness Rebooting from Coma. THE PHYSICS OF THE MIND AND BRAIN DISORDERS 2017. [DOI: 10.1007/978-3-319-29674-6_16] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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26
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Kalitzin SN, Bauer PR, Lamberts RJ, Velis DN, Thijs RD, Lopes Da Silva FH. Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse. Int J Neural Syst 2016; 26:1650027. [DOI: 10.1142/s0129065716500271] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automated monitoring and alerting for adverse events in people with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently, we found a relation between clonic slowing at the end of a convulsive seizure (CS) and the occurrence and duration of a subsequent period of postictal generalized EEG suppression (PGES). Prolonged periods of PGES can be predicted by the amount of progressive increase of interclonic intervals (ICIs) during the seizure. The purpose of the present study is to develop an automated, remote video sensing-based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES. This may help preventing sudden unexpected death in epilepsy (SUDEP). The technique is based on our previously published optical flow video sequence processing paradigm that was applied for automated detection of major motor seizures. Here, we introduce an integral Radon-like transformation on the time–frequency wavelet spectrum to detect log–linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed electroencephalography (EEG) traces as “gold standard”. We studied 48 cases of convulsive seizures for which synchronized EEG-video recordings were available. In most cases, the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during the seizure. The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection. If this effect is validated as a reliable precursor of PGES periods that lead to or increase the probability of SUDEP, the proposed method would provide an efficient alerting device.
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Affiliation(s)
- Stiliyan N. Kalitzin
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
- Image Sciences Institute, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
| | - Prisca R. Bauer
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Robert J. Lamberts
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Demetrios N. Velis
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
- Department of Neurosurgery, Free University Medical Center Amsterdam, 1007 MB Amsterdam, The Netherlands
| | - Roland D. Thijs
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Fernando H. Lopes Da Silva
- Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 SM Amsterdam, The Netherlands
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal
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27
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Quantitative analysis of surface electromyography: Biomarkers for convulsive seizures. Clin Neurophysiol 2016; 127:2900-2907. [DOI: 10.1016/j.clinph.2016.04.017] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2016] [Revised: 04/14/2016] [Accepted: 04/18/2016] [Indexed: 11/21/2022]
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28
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Kusmakar S, Gubbi J, Rao AS, Yan B, O'Brien TJ, Palaniswami M. Classification of convulsive psychogenic non-epileptic seizures using histogram of oriented motion of accelerometry signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:586-589. [PMID: 26736330 DOI: 10.1109/embc.2015.7318430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
A seizure is caused due to sudden surge of electrical activity within the brain. There is another class of seizures called psychogenic non-epileptic seizure (PNES) that mimics epilepsy, but is caused due to underlying psychology. The diagnosis of PNES is done using video-electroencephalography monitoring (VEM), which is a resource intensive process. Recently, accelerometers have been shown to be effective in classification of epileptic and non-epileptic seizures. In this work, we propose a novel feature called histogram of oriented motion (HOOM) extracted from accelerometer signals for classification of convulsive PNES. An automated algorithm based on HOOM is proposed. The algorithm showed a high sensitivity of (93.33%) and an overall accuracy of (80%) in classifying convulsive PNES.
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29
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Kusmakar S, Gubbi J, Yan B, O'Brien TJ, Palaniswami M. Classification of convulsive psychogenic non-epileptic seizures using muscle transforms obtained from accelerometry signal. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:582-585. [PMID: 26736329 DOI: 10.1109/embc.2015.7318429] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Convulsive psychogenic non-epileptic seizure (PNES) can be characterized as events which mimics epileptic seizures but do not show any characteristic changes on electroencephalogram (EEG). Correct diagnosis requires video-electroencephalography monitoring (VEM) as the diagnosis of PNES is extremely difficult in primary health care. Recent work has demonstrated the usefulness of accelerometry signal taken during a seizure in classification of PNES. In this work, a new direction has been explored to understand the role of different muscles in PNES. This is achieved by modeling the muscle activity of ten different upper limb muscles as a resultant function of accelerometer signal. Using these models, the accelerometer signals recorded from convulsive epileptic patients were transformed into individual muscle components. Based on this, an automated algorithm for classification of convulsive PNES is proposed. The algorithm calculates four wavelet domain features based on signal power, approximate entropy, kurtosis and signal skewness. These features were then used to build a classification model using support vector machines (SVM) classifier. It was found that the transforms corresponding to anterior deltoid and brachioradialis results in good PNES classification accuracy. The algorithm showed a high sensitivity of 93.33% and an overall PNES classification accuracy of 89% with the transform corresponding to anterior deltoid.
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30
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Gubbi J, Kusmakar S, Rao AS, Yan B, OBrien T, Palaniswami M. Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device. IEEE J Biomed Health Inform 2015; 20:1061-72. [PMID: 26087511 DOI: 10.1109/jbhi.2015.2446539] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.
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31
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Wiseman H, Reuber M. New insights into psychogenic nonepileptic seizures 2011-2014. Seizure 2015; 29:69-80. [PMID: 26076846 DOI: 10.1016/j.seizure.2015.03.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Revised: 02/24/2015] [Accepted: 03/11/2015] [Indexed: 10/23/2022] Open
Abstract
PURPOSE There has been a rapid increase in the rate of publications about psychogenic nonepileptic seizures (PNES). This review summarises insights from the 50 most important original articles about PNES published since 2011 and describes the advances made in the understanding of PNES over the last 3 years. METHOD We carried out a systematic literature search of all English language publications about PNES published between October 2011 and October 2014 on Scopus, Ovid Medline and Web of Knowledge, and inspected all abstracts. Having excluded all review articles, case reports, conference abstracts, articles exploring PNES in children, and articles not actually focussing on PNES, we considered 150 papers for inclusion in this review. We assessed the quality of the identified studies and used expert judgement to identify the 50 most important publications from the review period and composed a narrative review based on these original papers. RESULTS Almost one half of the studies initially identified only provided Class 4 evidence. Recent work has provided more support for a biopsychosocial account of PNES. It has illustrated the heterogeneity of PNES, identifying varying and distinct psychological profiles of individuals with this disorder. These findings suggest that intervention needs to be flexible or adaptive if it is appropriately to target the different mechanisms which may give rise to PNES. Several educational and psychotherapeutic interventions for PNES have been described, but sufficiently powered randomised controlled trials are yet to be undertaken. Recent research using social, economic and quality of life indicators has provided further evidence of the societal and individual burden of PNES. CONCLUSION The research into PNES published over the last 3 years has deepened our understanding of the condition as a biopsychosocial disorder which is neither a "physical" nor a "psychological" condition. A number of small studies have demonstrated the potential of educational and psychotherapeutic treatments, but rigorous and sufficiently large trials still need to be conducted to determine the effectiveness of these interventions.
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Affiliation(s)
- Hannah Wiseman
- Academic Neurology Unit, University of Sheffield, United Kingdom.
| | - Markus Reuber
- Academic Neurology Unit, University of Sheffield, United Kingdom
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32
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Beniczky S, Conradsen I, Moldovan M, Jennum P, Fabricius M, Benedek K, Andersen N, Hjalgrim H, Wolf P. Automated differentiation between epileptic and nonepileptic convulsive seizures. Ann Neurol 2015; 77:348-51. [PMID: 25545895 DOI: 10.1002/ana.24338] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2014] [Revised: 11/27/2014] [Accepted: 11/21/2014] [Indexed: 11/11/2022]
Abstract
Our objective was the clinical validation of an automated algorithm based on surface electromyography (EMG) for differentiation between convulsive epileptic and psychogenic nonepileptic seizures (PNESs). Forty-four consecutive episodes with convulsive events were automatically analyzed with the algorithm: 25 generalized tonic-clonic seizures (GTCSs) from 11 patients, and 19 episodes of convulsive PNES from 13 patients. The gold standard was the interpretation of the video-electroencephalographic recordings by experts blinded to the EMG results. The algorithm correctly classified 24 GTCSs (96%) and 18 PNESs (95%). The overall diagnostic accuracy was 95%. This algorithm is useful for distinguishing between epileptic and psychogenic convulsive seizures.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
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