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Michaelis R, Schöller H, Popkirov S, Edelhäuser F, Kolenik T, Trinka E, Schiepek G. Psychological precursors of epileptic seizures. Epilepsia 2024; 65:e35-e40. [PMID: 38100099 DOI: 10.1111/epi.17865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 01/14/2024]
Abstract
Psychological stress is the most commonly self-reported precursor of epileptic seizures. However, retrospective and prospective studies remain inconclusive in this regard. Here, we explored whether seizures would be preceded by significant changes in reported stressors or resource utilization. This study is based on high-frequency time series through daily online completion of personalized questionnaires of 9-24 items in epilepsy outpatients and compared responses 1-14 days before seizures with interictal time series. Fourteen patients (79% women, age = 23-64 years) completed daily questionnaires over a period of 87-898 days (median = 277 days = 9.2 months). A total of 4560 fully completed daily questionnaires were analyzed, 685 of which included reported seizure events. Statistically significant changes in preictal compared to interictal dynamics were found in 11 of 14 patients (79%) across 41 items (22% of all 187 items). In seven of 14 patients (50%), seizures were preceded by a significant mean increase of stressors and/or a significant mean decrease of resource utilization. This exploratory analysis of long-term prospective individual patient data on specific stressors and personal coping strategies generates the hypothesis that medium-term changes in psychological well-being may precede the occurrence of epileptic seizures in some patients.
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Affiliation(s)
- Rosa Michaelis
- Witten/Herdecke University, Herdecke, Germany
- Department of Neurology, University Hospital Knappschaftskrankenhaus Bochum, Ruhr University Bochum, Bochum, Germany
| | - Helmut Schöller
- Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Salzburg, Austria
- University Hospital of Psychiatry, Psychotherapy, and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
| | - Stoyan Popkirov
- Department of Neurology, University Hospital Essen, Essen, Germany
| | - Friedrich Edelhäuser
- Witten/Herdecke University, Herdecke, Germany
- Department of Early Rehabilitation, Gemeinschaftskrankenhaus Herdecke, Herdecke, Germany
| | - Tine Kolenik
- Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Salzburg, Austria
- University Hospital of Psychiatry, Psychotherapy, and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Neurointensive Care, and Neurorehabilitation, member of the European Reference Network EpiCARE, Christian Doppler University Hospital, Paracelsus Medical University, Center for Cognitive Neuroscience, Salzburg, Austria
- Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Center for Cognitive Neuroscience, Salzburg, Austria
| | - Günter Schiepek
- Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Salzburg, Austria
- University Hospital of Psychiatry, Psychotherapy, and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
- Faculty of Psychology and Educational Sciences, Ludwig Maximilian University of Munich, Munich, Germany
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Fu A, Lado FA. Seizure Detection, Prediction, and Forecasting. J Clin Neurophysiol 2024; 41:207-213. [PMID: 38436388 DOI: 10.1097/wnp.0000000000001045] [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: 03/05/2024] Open
Abstract
SUMMARY Among the many fears associated with seizures, patients with epilepsy are greatly frustrated and distressed over seizure's apparent unpredictable occurrence. However, increasing evidence have emerged over the years to support that seizure occurrence is not a random phenomenon as previously presumed; it has a cyclic rhythm that oscillates over multiple timescales. The pattern in rises and falls of seizure rate that varies over 24 hours, weeks, months, and years has become a target for the development of innovative devices that intend to detect, predict, and forecast seizures. This article will review the different tools and devices available or that have been previously studied for seizure detection, prediction, and forecasting, as well as the associated challenges and limitations with the utilization of these devices. Although there is strong evidence for rhythmicity in seizure occurrence, very little is known about the mechanism behind this oscillation. This article concludes with early insights into the regulations that may potentially drive this cyclical variability and future directions.
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Affiliation(s)
- Aradia Fu
- Department of Neurology, Zucker School of Medicine at Hofstra-Northwell, Great Neck, New York, U.S.A
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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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Cui J, Balzekas I, Nurse E, Viana P, Gregg N, Karoly P, Stirling RE, Worrell G, Richardson MP, Freestone DR, Brinkmann BH. Perceived seizure risk in epilepsy: Chronic electronic surveys with and without concurrent electroencephalography. Epilepsia 2023; 64:2421-2433. [PMID: 37303239 PMCID: PMC10526687 DOI: 10.1111/epi.17678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 06/07/2023] [Accepted: 06/07/2023] [Indexed: 06/13/2023]
Abstract
OBJECTIVE Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self-reported and electroencephalographically (EEG)-confirmed seizures in ambulatory patients with epilepsy in their natural home environments. METHODS Long-term e-surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). RESULTS Fifty-four subjects returned 10 269 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12-3.61, AUC = .61, p = .02) was associated with increased relative odds of future self-reported seizures. Multivariate analysis showed that previous self-reported seizures (OR = 5.37, 95% CI = 3.53-8.16, AUC = .76, p < .001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87-5.95, AUC = .69, p < .001) remained significant when prior self-reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e-survey responses and subsequent EEG seizures. SIGNIFICANCE Our results suggest that patients may tend to self-forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.
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Affiliation(s)
- Jie Cui
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- Mayo College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Irena Balzekas
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan Nurse
- Seer Medical, Melbourne, Australia
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Pedro Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
- Faculty of Medicine, University of Lisbon, Portugal
| | - Nicholas Gregg
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Philippa Karoly
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Rachel E Stirling
- Seer Medical, Melbourne, Australia
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Gregory Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
| | | | - Benjamin H Brinkmann
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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Pattnaik AR, Ghosn NJ, Ong IZ, Revell AY, Ojemann WKS, Scheid BH, Georgostathi G, Bernabei JM, Conrad EC, Sinha SR, Davis KA, Sinha N, Litt B. The seizure severity score: a quantitative tool for comparing seizures and their response to therapy. J Neural Eng 2023; 20:10.1088/1741-2552/aceca1. [PMID: 37531949 PMCID: PMC11250994 DOI: 10.1088/1741-2552/aceca1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/01/2023] [Indexed: 08/04/2023]
Abstract
Objective.Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies.Approach.We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features.Main results.The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p= 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical severity was associated with better surgical outcome (p= 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity.Significance.We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool's utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.
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Affiliation(s)
- Akash R Pattnaik
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Nina J Ghosn
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Ian Z Ong
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Andrew Y Revell
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - William K S Ojemann
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Brittany H Scheid
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Georgia Georgostathi
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - John M Bernabei
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Saurabh R Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- These authors contributed equally to this work
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- These authors contributed equally to this work
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Egenasi CK, Moodley AA, Steinberg WJ, Joubert G. A modified Delphi study to determine the contents of a seizure diary for patients living with epilepsy in South Africa. J Public Health Afr 2023; 14:2460. [PMID: 37680868 PMCID: PMC10481901 DOI: 10.4081/jphia.2023.2460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/26/2023] [Indexed: 09/09/2023] Open
Abstract
Background Epilepsy is a debilitating chronic medical condition affecting many patients globally. A seizure diary is used in monitoring and managing patients with epilepsy. In South Africa, no standardized diary is currently being used. Objective This study intended to develop a consensus among experts managing patients with epilepsy on the content of a seizure diary. Methods The modified Delphi method consisted of three survey rounds spanning six months. Using a three-point Likert scale questionnaire, in round one, the panelists were required to choose an option (definitely required, optional, and not required) for 50 items and comment on the contents of the diary. In round two, three items were added based on comments from the panelists. In round three, panelists were allowed to deliberate further on unresolved items and change their responses in view of the group responses. The consensus was determined as an a priori threshold of >70% on items definitely required, optional, or not required. Results Eleven local and two international panelists were enrolled in this study. Twelve completed all three rounds. The consensus was achieved in 21 of 50 items in round 1, three of seven items in round 2, and one of two items in round 3, of which 18 were definitely required as contents of a seizure diary. Conclusions Based on expert opinions, the modified Delphi study determined the essential contents of a seizure diary for use by patients with epilepsy in South Africa.
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Affiliation(s)
- Chika Kennedy Egenasi
- Department of Family Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein
| | | | - Wilhelm Johannes Steinberg
- Department of Family Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein
| | - Gina Joubert
- Department of Biostatistics, School of Biomedical Sciences, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa
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Egenasi CK, Moodley AA, Steinberg WJ, Joubert G. Experience of the new seizure diary in the Free State and Northern Cape. S Afr Fam Pract (2004) 2023; 65:e1-e11. [PMID: 37265139 PMCID: PMC10483308 DOI: 10.4102/safp.v65i1.5736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Epilepsy is a neurological disease affecting adults and children globally. A seizure diary is one of the self-management tools for tracking seizures. This study aims to ascertain the experience of a new seizure diary by persons completing the diary in the Free State and Northern Cape of South Africa. METHODS Adult patients with epilepsy attending Universitas Academic Hospital epilepsy clinic in Bloemfontein, clinics in Kimberley and the casualty department of Kimberley hospital (Robert Mangaliso Sobukwe hospital) received a new seizure diary. After using the diary for 6 months, participants (patients, relatives or caregivers) completed a questionnaire. RESULTS A total of 139 epilepsy patients received a new seizure diary; 67 previously diary-unexposed participants and 33 participants who had previous exposure to a seizure diary. The majority of participants, namely 91% of previously diary-unexposed and 84.9% of participants who had previous exposure to the seizure diary, understood the new seizure diary. Participants who had previous exposure to a seizure diary were predominantly very positive about the new diary because it had more information. However, 21.2% indicated that they preferred the old one because it was easier to complete. CONCLUSION Patients, caregivers or relatives from both groups used the new seizure diary and provided important information about their experience with the new diary. Despite a few complaints about using the new diary, most participants who had previous exposure to a seizure diary preferred the new seizure diary.Contribution: This study explored participants' opinions of the new seizure diary.
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Affiliation(s)
- Chika K Egenasi
- Department of Family Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein.
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Cui J, Balzekas I, Nurse E, Viana P, Gregg N, Karoly P, Worrell G, Richardson MP, Freestone DR, Brinkmann BH. Perceived seizure risk in epilepsy â€" Chronic electronic surveys with and without concurrent EEG. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.23.23287561. [PMID: 37034596 PMCID: PMC10081426 DOI: 10.1101/2023.03.23.23287561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Objective Previous studies suggested that patients with epilepsy might be able to fore-cast their own seizures. We sought to assess the relationships of premonitory symptoms and perceived seizure risk with future and recent self-reported and EEG-confirmed seizures in the subjects living with epilepsy in their natural home environments. Methods We collected long-term e-surveys from ambulatory patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication compliance, sleep quality, mood, stress, perceived seizure risk and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with device seizure forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). Results Sixty-nine subjects returned 12,590 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed increased stress (OR = 2.52, 95% CI = [1.52, 4.14], p < 0.001) and decreased mood (0.32, [0.13, 0.82], 0.02) were associated with increased relative odds of future self-reported seizures. On multivariate analysis, previous self-reported seizures (4.24, [2.69, 6.68], < 0.001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (3.30, [1.97, 5.52], < 0.001) remained significant when prior self-reported seizures were added to the model. No significant association was found between e-survey responses and subsequent EEG seizures. Significance It appears that patients may tend to self-forecast seizures that occur in sequential groupings. Our results suggest that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting. Key points Long-term e-surveys data and concurrent EEG signals were collected across three study sites to assess the ability of the patients to self-forecast their seizures.Patients may tend to self-forecast self-reported seizures that occur in sequential groupings.Factors, such as mood and stress, may not be independent premonitory symptoms but may be the consequence of recent seizures.No ability to self-forecast EEG confirmed seizures was observed in a small cohort with concurrent EEG validation.A mathematic relation between OR and AUC provides a means to compare forecasting performance between survey and device studies.
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Affiliation(s)
- Jie Cui
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- Mayo College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Irena Balzekas
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan Nurse
- Seer Medical, Melbourne, Australia
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Pedro Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
- Faculty of Medicine, University of Lisbon, Portugal
| | - Nicholas Gregg
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Philippa Karoly
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Gregory Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
| | | | - Benjamin H. Brinkmann
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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9
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Egenasi CK, Moodley AA, Steinberg WJ, Joubert G. The perceptions and attitudes of patients with epilepsy to the use of a seizure diary, South Africa. S Afr Fam Pract (2004) 2023; 65:e1-e7. [PMID: 36744483 PMCID: PMC9983296 DOI: 10.4102/safp.v65i1.5503] [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: 02/09/2022] [Revised: 07/08/2022] [Accepted: 07/10/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Epilepsy is responsible for a significant proportion of the world's disease burden, affecting around 50 million people globally. A seizure diary is a self-management tool for epilepsy focusing on self-monitoring, tracking seizures and other symptoms. This study aimed to determine the perceptions and attitudes to the seizure diary in patients with epilepsy in the Free State and Northern Cape of South Africa. METHODS This cross-sectional survey method included adult patients with epilepsy attending Universitas Academic Hospital Specialist Epilepsy Clinic in Bloemfontein and local clinics in Kimberley (City, Beaconsfield and Betty Gatsewe), as well as the casualty department of Kimberley hospital (Robert Mangaliso Sobukwe Hospital). The Kimberley patients were diary-unexposed, while the Bloemfontein patients were patients who had previous exposure to the seizure diary. RESULTS A total of 182 patients with epilepsy were recruited for the study, of whom 65 were patients who had previous exposure to the seizure diary, and 117 were unexposed. In the patients who had previous exposure to the seizure diary, 64 (98.5%) found the diary useful, but 15 (23.1%) reported having various challenges with using the seizure diary. Almost all of the patients who had previous exposure to the seizure diary, 64 (98.5%), were willing to continue to use the diary, while 112 (95.7%) of the diary-unexposed patients were also willing to use the diary. CONCLUSION Information from some patients using the diary confirms various challenges with its use; however, most patients support the continued usage of the diary.
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Affiliation(s)
- Chika K. Egenasi
- Department of Family Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa
| | - Anandan A. Moodley
- Department of Neurology, University of KwaZulu-Natal, Durban, South Africa
| | - Wilhelm J. Steinberg
- Department of Family Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa
| | - Gina Joubert
- Department of Biostatistics, School of Biomedical Sciences, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa
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Egenasi CK, Moodley AA, Steinberg WJ, Adefuye AO. Current norms and practices in using a seizure diary for managing epilepsy: A scoping review. S Afr Fam Pract (2004) 2022; 64:e1-e9. [PMID: 36226950 PMCID: PMC9575367 DOI: 10.4102/safp.v64i1.5540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/22/2022] [Accepted: 07/24/2022] [Indexed: 11/07/2022] Open
Abstract
Background Epilepsy is a chronic and debilitating condition affecting people of all ages in many nations. Healthcare practitioners look for effective ways to track patients’ seizures, and a seizure diary is one of the methods used. This scoping review sought to identify current norms and practices for using seizure diaries to manage epilepsy. Method A scoping review was performed by screening relevant studies and identifying themes, categories and subcategories. Results A total of 1125 articles were identified from the database; 46 full-text articles were assessed for eligibility, of which 23 articles were selected. The majority (48%) of the studies were prospective studies. The majority (65%) of the articles were studies conducted in the United States. The themes identified were types of seizure diaries used in clinical practice, contents and structure of a standardised seizure diary, the use and efficacy of seizure diaries in medicine and challenges relating to using a seizure diary for patient management. Conclusion The study revealed that a seizure diary remains a relevant tool in managing epilepsy. The two forms of diaries in use are electronic and paper-based diaries. The high cost of data and the expensive devices required to access electronic diaries make it unsuitable in a resource-limited setting. Despite its disadvantages, imperfections and inadequacies, the paper-based diary is still relevant for managing patients with epilepsy in resource-limited settings. Contribution This study reviewed the literature to find the current norms and practices in using seizure diaries. The benefits of the different formats were emphasised.
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Affiliation(s)
- Chika K. Egenasi
- Department of Family Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa
| | - Anandan A. Moodley
- Department of Neurology, Faculty of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Wilhelm J. Steinberg
- Department of Family Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa
| | - Anthonio O. Adefuye
- Division of Health Sciences Education, Faculty of Health Science, University of the Free State, Bloemfontein, South Africa
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11
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Moss A, Moss E, Moss R, Moss L, Chiang S, Crino P. A Patient Perspective on Seizure Detection and Forecasting. Front Neurol 2022; 13:779551. [PMID: 35222243 PMCID: PMC8874203 DOI: 10.3389/fneur.2022.779551] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 01/20/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Aria Moss
- Northern Virginia Community College, Alexandria, VA, United States
- *Correspondence: Aria Moss
| | - Evan Moss
- W. T. Woodson High School, Fairfax, VA, United States
| | - Robert Moss
- Seizure Tracker, LLC, Springfield, VA, United States
| | - Lisa Moss
- Seizure Tracker, LLC, Springfield, VA, United States
- TSC Alliance, Silver Spring, MD, United States
| | - Sharon Chiang
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Peter Crino
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, United States
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12
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Cousyn L, Navarro V, Chavez M. Outliers in clinical symptoms as preictal biomarkers. Epilepsy Res 2021; 177:106774. [PMID: 34571459 DOI: 10.1016/j.eplepsyres.2021.106774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/26/2021] [Accepted: 09/20/2021] [Indexed: 11/29/2022]
Abstract
Previous findings have suggested that a preictal state might precede the epileptic seizure onset, which is the basis for seizure prediction attempts. Preictal states can be apprehended as outliers that differ from an interictal baseline and display clinical changes. We collected daily clinical scores from patients with epilepsy who underwent continuous video-EEG and assessed the ability of several outlier detection methods to identify preictal states. Results from 24 patients suggested that outlying clinical features were suggestive of preictal states and can be identified by statistical methods: AUC = 0.71, 95 % CI = [0.63 - 0.79]; PPV = 0.77, 95 % CI = [0.70 - 0.84]; FPR = 0.31, 95 % CI = [0.21 - 0.44]); and F1 score = 0.74, 95 % CI = [0.64 - 0.81]. Such algorithms could be straightforwardly implemented in a mobile device (e.g., tablet or smartphone), which would allow a longer data collection that could improve prediction performances. Additional clinical - and even multimodal - parameters could identify more subtle physiological modifications.
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Affiliation(s)
- Louis Cousyn
- Paris Brain Institute, Inserm, CNRS, Sorbonne Université, Paris, France; AP-HP, Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière Hospital, Paris, France.
| | - Vincent Navarro
- Paris Brain Institute, Inserm, CNRS, Sorbonne Université, Paris, France; AP-HP, Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière Hospital, Paris, France
| | - Mario Chavez
- CNRS UMR-7225, Pitié-Salpêtrière Hospital, Paris, France
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13
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Shum J, Friedman D. Commercially available seizure detection devices: A systematic review. J Neurol Sci 2021; 428:117611. [PMID: 34419933 DOI: 10.1016/j.jns.2021.117611] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 10/20/2022]
Abstract
IMPORTANCE Epilepsy can be associated with significant morbidity and mortality. Seizure detection devices could be invaluable tools for both people with epilepsy, their caregivers, and clinicians as they could alert caretakers about seizures, reduce the risk of sudden unexpected death in epilepsy, and provide objective and more reliable seizure tracking to guide treatment decisions or monitor outcomes in clinical trials. OBJECTIVE To synthesize the characteristics of commercial seizure detection tools/devices currently available. METHODS We performed a systematic search utilizing a diverse set of resources to identify commercially available seizure detection products for consumer use. Performance data was obtained through a systematic review on commercially available products. OBSERVATIONS We identified 23 products marketed for seizure detection/alerting. Devices utilize a variety of mechanisms to detect seizures, including movement detectors, autonomic change detectors, electroencephalogram (EEG) based detectors, and other mechanisms (audio). The optimal device for a person with epilepsy depends on a variety of factors including the main purpose of the device, their age, seizure type and personal preferences. Only 8 devices have published peer-reviewed performance data and the majority for tonic-clonic seizures. An informed conversation between the clinician and the patient can help guide if a seizure detection device is appropriate. CONCLUSIONS AND RELEVANCE Seizure detection devices have a potential to reduce morbidity and mortality for certain people with epilepsy. Clinicians should be familiar with the characteristics of commercially available devices to best counsel their patients on whether a seizure detection device may be beneficial and what the optimal devices may be.
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Affiliation(s)
- Jennifer Shum
- Department of Neurology, Comprehensive Epilepsy Center, New York University Gross School of Medicine, New York, NY, USA.
| | - Daniel Friedman
- Department of Neurology, Comprehensive Epilepsy Center, New York University Gross School of Medicine, New York, NY, USA
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14
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High-frequency monitoring of personalized psychological variables during outpatient psychotherapy in people with seizures: An uncontrolled feasibility study. Epilepsy Behav 2021; 122:108119. [PMID: 34139618 DOI: 10.1016/j.yebeh.2021.108119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/21/2021] [Accepted: 05/28/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND This feasibility study applied the concept of daily systematic monitoring of personalized psychological variables and investigated patients' compliance in order to evaluate if its integration in outpatient psychotherapy is feasible and if patients found the development and daily application of personalized questionnaires user-friendly and useful. METHODS A naturalistic sample of patients with epilepsy (PWE) was enrolled to participate in an outpatient psychotherapy program. A personalized process questionnaire was developed with each patient based on an individual psychological system's model at the outset of therapy. Daily time-stamped self-assessments were collected during outpatient psychotherapy. This process-monitoring was technically realized by an internet-based device for data collection and data analysis, the Synergetic Navigation System (SNS). The reflection of person-specific time series informed by patients' replies to their personalized process questionnaire was integrated in the therapy process. Compliance rates were assessed during a period of six months (i.e., 180 days) after the first entry of the questionnaire [compliance rate = (number of completed questionnaires/180) × 100]. User-friendliness and usefulness of this process monitoring were evaluated quantitatively. RESULTS Twenty patients [15 women/5 men, median age 48 years (range 23-73 years)] were recruited. Compliance rates were high (median: 93%, range 31-100%) among the participants. Participants reported a high overall satisfaction with the application and user-friendliness of SNS. CONCLUSION The results support the feasibility of high-frequency monitoring of personalized psychological processes during outpatient psychotherapy. Repeated daily assessments of a personalized questionnaire yield highly resolved, equidistant time series data, which gives insight into individual psychological processes during outpatient psychotherapy.
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15
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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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16
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Brinkmann BH, Karoly PJ, Nurse ES, Dumanis SB, Nasseri M, Viana PF, Schulze-Bonhage A, Freestone DR, Worrell G, Richardson MP, Cook MJ. Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic. Front Neurol 2021; 12:690404. [PMID: 34326807 PMCID: PMC8315760 DOI: 10.3389/fneur.2021.690404] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/10/2021] [Indexed: 12/14/2022] Open
Abstract
It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic–clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.
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Affiliation(s)
| | - Philippa J Karoly
- Department of Medicine, Graeme Clark Institute and St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Ewan S Nurse
- Department of Medicine, Graeme Clark Institute and St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia.,Seer Medical, Melbourne, VIC, Australia
| | | | - Mona Nasseri
- Department of Neurology, Mayo Foundation, Rochester, MN, United States.,School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Pedro F Viana
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Faculty of Medicine, University of Lisbon, Lisboa, Portugal
| | - Andreas Schulze-Bonhage
- Faculty of Medicine, Epilepsy Center, Medical Center, University of Freiburg, Freiburg, Germany
| | | | - Greg Worrell
- Department of Neurology, Mayo Foundation, Rochester, MN, United States
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Mark J Cook
- Department of Medicine, Graeme Clark Institute and St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
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17
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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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18
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Abstract
PURPOSE OF REVIEW Epilepsy is a dynamical disorder of the brain characterized by sudden, seemingly unpredictable transitions to the ictal state. When and how these transitions occur remain unresolved questions in neurology. RECENT FINDINGS Modelling work based on dynamical systems theory proposed that a slow control parameter is necessary to explain the transition between interictal and ictal states. Recently, converging evidence from chronic EEG datasets unravelled the existence of cycles of epileptic brain activity at multiple timescales - circadian, multidien (over multiple days) and circannual - which could reflect cyclical changes in a slow control parameter. This temporal structure of epilepsy has theoretical implications and argues against the conception of seizures as completely random events. The practical significance of cycles in epilepsy is highlighted by their predictive value in computational models for seizure forecasting. SUMMARY The canonical randomness of seizures is being reconsidered in light of cycles of brain activity discovered through chronic EEG. This paradigm shift motivates development of next-generation devices to track more closely fluctuations in epileptic brain activity that determine time-varying seizure risk.
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19
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Cousyn L, Navarro V, Chavez M. Preictal state detection using prodromal symptoms: A machine learning approach. Epilepsia 2021; 62:e42-e47. [PMID: 33465245 DOI: 10.1111/epi.16804] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 12/13/2020] [Accepted: 12/13/2020] [Indexed: 12/01/2022]
Abstract
A reliable identification of a high-risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML) approach. Twenty-four patients with drug-resistant epilepsy were admitted for continuous video-electroencephalographic monitoring and filled out a daily four-point questionnaire on prodromal symptoms. Data were then classified into (1) a preictal group for questionnaires completed in a 24-h period prior to at least one seizure (n1 = 58) and (2) an interictal group for questionnaires completed in a 24-h period without seizures (n2 = 190). Our prediction model was based on a support vector machine classifier and compared to a Fisher's linear classifier. The combination of all the prodromal symptoms yielded a good prediction performance (area under the curve [AUC] = .72, 95% confidence interval [CI] = .61-.81). This performance was significantly enhanced by selecting a subset of the most relevant symptoms (AUC = .80, 95% CI = .69-.88). In comparison, the linear classifier systematically failed (AUCs < .6). Our findings indicate that the ML analysis of prodromal symptoms is a promising approach to identifying preictal states prior to seizures. This could pave the way for development of clinical strategies in seizure prevention and even a noninvasive alarm system.
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Affiliation(s)
- Louis Cousyn
- Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière Hospital, Public Hospital Network of Paris, Paris, France.,Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France.,Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France.,Sorbonne University, Paris, France
| | - Vincent Navarro
- Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière Hospital, Public Hospital Network of Paris, Paris, France.,Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France.,Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France.,Sorbonne University, Paris, France
| | - Mario Chavez
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
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20
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Rao VR, G Leguia M, Tcheng TK, Baud MO. Cues for seizure timing. Epilepsia 2020; 62 Suppl 1:S15-S31. [PMID: 32738157 DOI: 10.1111/epi.16611] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 01/22/2023]
Abstract
The cyclical organization of seizures in epilepsy has been described since antiquity. However, historical explanations for seizure cycles-based on celestial, hormonal, and environmental factors-have only recently become testable with the advent of chronic electroencephalography (cEEG) and modern statistical techniques. Here, factors purported over millennia to influence seizure timing are viewed through a contemporary lens. We discuss the emerging concept that seizures are organized over multiple timescales, each involving differential influences of external and endogenous rhythm generators. Leveraging large cEEG datasets and circular statistics appropriate for cyclical phenomena, we present new evidence for circadian (day-night), multidien (multi-day), and circannual (about-yearly) variation in seizure activity. Modulation of seizure timing by multiscale temporal variables has implications for diagnosis and therapy in clinical epilepsy. Uncovering the mechanistic basis for seizure cycles, particularly the factors that govern multidien periodicity, will be a major focus of future work.
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Affiliation(s)
- Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California
| | - Marc G Leguia
- Department of Neurology, Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | | | - Maxime O Baud
- Department of Neurology, Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland.,Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
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21
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Stirling RE, Cook MJ, Grayden DB, Karoly PJ. Seizure forecasting and cyclic control of seizures. Epilepsia 2020; 62 Suppl 1:S2-S14. [PMID: 32712968 DOI: 10.1111/epi.16541] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/23/2020] [Accepted: 04/27/2020] [Indexed: 02/02/2023]
Abstract
Epilepsy is a unique neurologic condition characterized by recurrent seizures, where causes, underlying biomarkers, triggers, and patterns differ across individuals. The unpredictability of seizures can heighten fear and anxiety in people with epilepsy, making it difficult to take part in day-to-day activities. Epilepsy researchers have prioritized developing seizure prediction algorithms to combat episodic seizures for decades, but the utility and effectiveness of prediction algorithms has not been investigated thoroughly in clinical settings. In contrast, seizure forecasts, which theoretically provide the probability of a seizure at any time (as opposed to predicting the next seizure occurrence), may be more feasible. Many advances have been made over the past decade in the field of seizure forecasting, including improvements in algorithms as a result of machine learning and exploration of non-EEG-based measures of seizure susceptibility, such as physiological biomarkers, behavioral changes, environmental drivers, and cyclic seizure patterns. For example, recent work investigating periodicities in individual seizure patterns has determined that more than 90% of people have circadian rhythms in their seizures, and many also experience multiday, weekly, or longer cycles. Other potential indicators of seizure susceptibility include stress levels, heart rate, and sleep quality, all of which have the potential to be captured noninvasively over long time scales. There are many possible applications of a seizure-forecasting device, including improving quality of life for people with epilepsy, guiding treatment plans and medication titration, optimizing presurgical monitoring, and focusing scientific research. To realize this potential, it is vital to better understand the user requirements of a seizure-forecasting device, continue to advance forecasting algorithms, and design clear guidelines for prospective clinical trials of seizure forecasting.
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Affiliation(s)
- Rachel E Stirling
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia
| | - Mark J Cook
- Graeme Clark Institute & St Vincent's Hospital, The University of Melbourne, Melbourne, Vic., Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia.,Graeme Clark Institute & St Vincent's Hospital, The University of Melbourne, Melbourne, Vic., Australia
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22
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Karoly PJ, Cook MJ, Maturana M, Nurse ES, Payne D, Brinkmann BH, Grayden DB, Dumanis SB, Richardson MP, Worrell GA, Schulze‐Bonhage A, Kuhlmann L, Freestone DR. Forecasting cycles of seizure likelihood. Epilepsia 2020; 61:776-786. [DOI: 10.1111/epi.16485] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 01/22/2023]
Affiliation(s)
- Philippa J. Karoly
- Graeme Clark Institute and St Vincent’s Hospital University of Melbourne Melbourne Victoria Australia
- Department of Biomedical Engineering University of Melbourne Melbourne Victoria Australia
| | - Mark J. Cook
- Graeme Clark Institute and St Vincent’s Hospital University of Melbourne Melbourne Victoria Australia
| | - Matias Maturana
- Graeme Clark Institute and St Vincent’s Hospital University of Melbourne Melbourne Victoria Australia
- Seer Medical Melbourne Victoria Australia
| | - Ewan S. Nurse
- Graeme Clark Institute and St Vincent’s Hospital University of Melbourne Melbourne Victoria Australia
- Seer Medical Melbourne Victoria Australia
| | - Daniel Payne
- Department of Biomedical Engineering University of Melbourne Melbourne Victoria Australia
| | | | - David B. Grayden
- Department of Biomedical Engineering University of Melbourne Melbourne Victoria Australia
| | | | | | | | - Andreas Schulze‐Bonhage
- Faculty of Medicine Epilepsy Center Medical Center University of Freiburg Freiburg Germany
- European Reference Network EpiCare Freiburg Germany
| | - Levin Kuhlmann
- Department of Data Science and AI Faculty of Information Technology Monash University Clayton Victoria Australia
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Privitera M, Haut SR, Lipton RB, McGinley JS, Cornes S. Seizure self-prediction in a randomized controlled trial of stress management. Neurology 2019; 93:e2021-e2031. [PMID: 31645468 DOI: 10.1212/wnl.0000000000008539] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Accepted: 05/30/2019] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE Using electronic diaries as part of a randomized controlled trial of stress reduction for epilepsy, we evaluated factors associated with successful seizure self-prediction. METHODS Adults with medication-resistant focal epilepsy were recruited from 3 centers and randomized to treatment with progressive muscle relaxation or control focused attention. An 8-week baseline was followed by 12 weeks of double-blind treatment. Twice daily, participants rated the likelihood of a seizure in the next 24 hours on a 5-point scale from very unlikely to almost certain, along with mood, premonitory symptoms, stress ratings, and seizure counts. We analyzed the association of mood, premonitory symptoms, stress, and circadian influences on seizure self-prediction. RESULTS Sixty-four participants completed the trial (3,126 seizures). Diary entry adherence was >82%. Participant self-prediction was associated with seizure occurrence at 6, 12, and 24 hours (p < 0.0001). Odds ratio (OR) of seizure prediction increased systematically with participants' prediction of seizure likelihood (p < 0.0001, all levels of prediction and all time intervals). For the 12-hour prediction window, median specificity for seizure prediction was 0.94 and negative predictive value 0.94; median sensitivity was 0.10 and positive predictive value 0.13. A subset of 13 participants (20% of sample) met criteria for good predictors (median OR for seizure prediction 5.25). Mood, stress, premonitory symptoms, seizure time, and randomized group were not associated with seizure occurrence. CONCLUSION In this prospective study, participants' prediction of a high probability of seizure was significantly associated with subsequent seizure occurrence within 24 hours. Future studies should focus on understanding factors that drive self-prediction. CLINICALTRIALSGOV IDENTIFIER NCT01444183.
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Affiliation(s)
- Michael Privitera
- From the Department of Neurology (M.P.), University of Cincinnati College of Medicine, OH; Montefiore-Einstein Epilepsy Center (S.R.H.) and Departments of Neurology (R.B.L.) and Epidemiology and Population Health (R.B.L.), Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY; Vector Psychometric Group (J.S.M.), LLC, Chapel Hill, NC; and University of California (S.C.), San Francisco.
| | - Sheryl R Haut
- From the Department of Neurology (M.P.), University of Cincinnati College of Medicine, OH; Montefiore-Einstein Epilepsy Center (S.R.H.) and Departments of Neurology (R.B.L.) and Epidemiology and Population Health (R.B.L.), Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY; Vector Psychometric Group (J.S.M.), LLC, Chapel Hill, NC; and University of California (S.C.), San Francisco
| | - Richard B Lipton
- From the Department of Neurology (M.P.), University of Cincinnati College of Medicine, OH; Montefiore-Einstein Epilepsy Center (S.R.H.) and Departments of Neurology (R.B.L.) and Epidemiology and Population Health (R.B.L.), Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY; Vector Psychometric Group (J.S.M.), LLC, Chapel Hill, NC; and University of California (S.C.), San Francisco
| | - James S McGinley
- From the Department of Neurology (M.P.), University of Cincinnati College of Medicine, OH; Montefiore-Einstein Epilepsy Center (S.R.H.) and Departments of Neurology (R.B.L.) and Epidemiology and Population Health (R.B.L.), Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY; Vector Psychometric Group (J.S.M.), LLC, Chapel Hill, NC; and University of California (S.C.), San Francisco
| | - Susannah Cornes
- From the Department of Neurology (M.P.), University of Cincinnati College of Medicine, OH; Montefiore-Einstein Epilepsy Center (S.R.H.) and Departments of Neurology (R.B.L.) and Epidemiology and Population Health (R.B.L.), Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY; Vector Psychometric Group (J.S.M.), LLC, Chapel Hill, NC; and University of California (S.C.), San Francisco
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Correlation of EEG spectra, connectivity, and information theoretical biomarkers with psychological states in the epilepsy monitoring unit - A pilot study. Epilepsy Behav 2019; 99:106485. [PMID: 31493735 DOI: 10.1016/j.yebeh.2019.106485] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 08/05/2019] [Accepted: 08/06/2019] [Indexed: 11/24/2022]
Abstract
At the level of individual experience, the relation between electroencephalographic (EEG) phenomena and subjective ratings of psychological states is poorly examined. This study investigated the correlation of quantitative EEG markers with systematic high-frequency monitoring of psychological states in patients admitted to the epilepsy monitoring unit (EMU). We used a digital questionnaire, including eight standardized items about stress, energy level, mood, ward atmosphere, seizure likelihood, hopefulness/frustration, boredom, and self-efficacy. Self-assessments were collected four times per day, in total 15 times during the stay in the EMU. We extracted brainrate, Hjorth parameters, Hurst exponent, Wackermann parameters, and power spectral density from the EEG. We performed correlation between these quantitative EEG measures and responses to the 8 items and evaluated their significance on single subject and on group level. Twenty-one consecutive patients (12 women/9 men, median age: 29 years, range: 18-74 years) were recruited. On group level, no significant correlations were found whereas on single-subject level, we found significant correlations for 6 out of 21 patients. Most significant correlations were found between Hjorth parameters and items that reflect changes in mood or stress. This study supports the feasibility of correlating quantitative EEG measures with psychological states in routine EMU settings and emphasizes the need for single-subject statistics when assessing aspects with high interindividual variance. Future studies should select samples with high within-subject variability of psychological states and examine a subsample with patients encountering a critical number of seizures needed in order to relate the psychological states to the ultimate question: Are psychological states potential indicators for seizure likelihood?
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Simblett SK, Bruno E, Siddi S, Matcham F, Giuliano L, López JH, Biondi A, Curtis H, Ferrão J, Polhemus A, Zappia M, Callen A, Gamble P, Wykes T. Patient perspectives on the acceptability of mHealth technology for remote measurement and management of epilepsy: A qualitative analysis. Epilepsy Behav 2019; 97:123-129. [PMID: 31247523 DOI: 10.1016/j.yebeh.2019.05.035] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 05/22/2019] [Accepted: 05/23/2019] [Indexed: 11/27/2022]
Abstract
BACKGROUND Innovative uses of mobile health (mHealth) technology for real-time measurement and management of epilepsy may improve the care provided to patients. For instance, seizure detection and quantifying related problems will have an impact on quality of life and improve clinical management for people experiencing frequent and uncontrolled seizures. Engaging patients with mHealth technology is essential, but little is known about patient perspectives on their acceptability. The aim of this study was to conduct an in-depth qualitative analysis of what people with uncontrolled epilepsy think could be the potential uses of mHealth technology and to identify early potential barriers and facilitators to engagement in three European countries. METHOD Twenty people currently experiencing epileptic seizures took part in five focus groups held across the UK, Italy, and Spain. Participants all completed written consent and a demographic questionnaire prior to the focus group commencing, and each group discussion lasted 60-120 min. A coding frame, developed from a systematic review of the previous literature, was used to structure a thematic analysis. We extracted themes and subthemes from the discussions, focusing first on possible uses of mHealth and then the barriers and facilitators to engagement. RESULTS Participants were interested in mHealth technology as a clinical detection tool, e.g., to aid communication about seizure occurrence with their doctors. Other suggested uses included being able to predict or prevent seizures, and to improve self-management. Key facilitators to engagement were the ability to raise awareness, plan activities better, and improve safety. Key barriers were the potential for increased stigma and anxiety. Using familiar and customizable products could be important moderators of engagement. CONCLUSION People with uncontrolled epilepsy think that there is a scope for mHealth technology to be useful in healthcare as a detection or prediction tool. The costs will be compared with the benefits when it comes to engagement, and ongoing work with patients and other stakeholders is needed to design practical resources.
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Affiliation(s)
- Sara K Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Elisa Bruno
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Centro de Investigació Biomedica en Red CIBERSAM, Spain; University of Barcelona, Barcelona, Spain
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Loretta Giuliano
- Department of Medical and Surgical Sciences and Advanced Technologies "G.F. Ingrassia", Section of Neurosciences, University of Catania, Catania, Italy
| | | | - Andrea Biondi
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Hannah Curtis
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | | | | | - Mario Zappia
- Department of Medical and Surgical Sciences and Advanced Technologies "G.F. Ingrassia", Section of Neurosciences, University of Catania, Catania, Italy
| | - Antonio Callen
- Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Centro de Investigació Biomedica en Red CIBERSAM, Spain; University of Barcelona, Barcelona, Spain
| | | | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust, King's College London, UK
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Patient and caregiver preferences for the potential benefits and risks of a seizure forecasting device: A best-worst scaling. Epilepsy Behav 2019; 96:183-191. [PMID: 31150998 DOI: 10.1016/j.yebeh.2019.04.018] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 04/12/2019] [Accepted: 04/13/2019] [Indexed: 01/17/2023]
Abstract
BACKGROUND Epilepsy is the 4th most common neurological disorder and is characterized by recurrent, unpredictable seizures. The ability to forecast seizures is a significant unmet need and would have a transformative effect on the lives of people living with epilepsy. In an effort to address this need, the Epilepsy Foundation has committed effort and resources to promote the development of seizure forecasting devices (SFD). OBJECTIVE To promote user-centered design of future SFD, we sought to quantify patient and caregiver preferences for the potential benefits and risks of SFD. METHODS A community-centered approach was used to develop a survey incorporating a novel best-worst scaling (BWS) to assess preferences for SFD. A main-effect orthogonal array was used to design and generate 18 "prototypes" that systematically varied across six attributes: seizure forecasting probability, seizure forecasting range, inaccuracy of forecasting, amount of time required to use the device, how the device is worn, and cost. The dependent variable was the attributes that respondents selected the best and worst in each profile, and a choice model was estimated using conditional logistic regression, which was also stratified and compared across patients and caregivers. Respondents also indicated that they would accept each of the prototype SFDs if it were real. These acceptance data and net monetary benefits (relative to the least preferred SFD) were explored. RESULTS There were 633 eligible respondents; 493 (78%) completed at least one task. Responses indicated that 346 (68%) had epilepsy, and 147 (29%) were primary caregivers or family members of someone with epilepsy. The data show that short forecasting range is the most favored among experimental attributes, followed by mid forecasting range and notification of high chance of seizure. Having the device implanted is the least favorable attribute. Stated preferences differed between patients and caregivers (p < 0.001) for range of forecasting and inaccuracy of device. Caregivers preferred any range of forecasting, regardless of length, more than patients. Patients cared less about inaccuracy of the device compared to caregivers. The groups also differ in impact of fear of having seizures (versus actually having seizures) (p = 0.034) and on device acceptance. The acceptance of devices ranged from 42.3% to 95%, with caregivers being more likely to use a device (p < 0.05) for the majority of device profiles. Acceptance of devices varied with net monetary benefit of the best device being $717.44 more per month relative to the least preferred device. CONCLUSION Our finding extends previous calls for seizure forecasting devices by demonstrating the value that they might provide to patients and caregivers affected by epilepsy and the feature that might be most and least desirable. In addition to guiding device development, the data can help inform regulatory decisions makers.
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Seizure cluster: Definition, prevalence, consequences, and management. Seizure 2019; 68:9-15. [DOI: 10.1016/j.seizure.2018.05.013] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 05/18/2018] [Accepted: 05/19/2018] [Indexed: 12/22/2022] Open
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Michaelis R, Schöller H, Höller Y, Kalss G, Kirschner M, Schmid E, Trinka E, Schiepek G. Integrating the systematic assessment of psychological states in the epilepsy monitoring unit: Concept and compliance. Epilepsy Behav 2018; 88:5-14. [PMID: 30212726 DOI: 10.1016/j.yebeh.2018.08.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 08/16/2018] [Accepted: 08/22/2018] [Indexed: 11/17/2022]
Abstract
BACKGROUND Admission to the epilepsy monitoring unit (EMU) for long-term video-electroencephalography (EEG) monitoring (VEEG) constitutes the gold standard for seizure diagnosis and presurgical evaluation. This study applied the concept of a high-frequency systematic monitoring of psychological states and tested patients' compliance in order to evaluate if its integration in the EMU is feasible and if patients benefit from the graphically underpinned discussion of their EMU stay-related cognitions and emotions. METHODS The process-monitoring is technically realized by an internet-based device for data collection and data analysis, the Synergetic Navigation System (SNS). A convenient sample was enrolled: All eligible patients who were admitted to the EMU of the Department of Neurology, Christian Doppler Medical Center, Salzburg, Austria, between November 6th 2017 and January 26th 2018 were approached and recruited upon consent. After a short resource-oriented interview, each enrolled patient was provided with a tablet. The daily questionnaire included eight standardized and up to three personalized items. Self-assessments were collected every 5 h prior to meal times (6:30 am, 11:30 am, and 4:30 pm) and at 9:30 pm. The detailed visualizations of the patients' replies were discussed with the participants during a feedback session at the end of the EMU stay. RESULTS Twenty-one patients (12 women/9 men, median age 29 years [range 18-74 years]) were consecutively recruited (72% of all eligible patients). Compliance rates were high (median: 82%, range 60%-100%) among the respondents. Mood correlated strongly with hopefulness (r = 0.71) and moderately with energy (r = 0.63) in all patients. When correlating the intraindividual medians of the process questionnaire time series with the pretest total scores, energy correlated moderately and negatively with the Perceived Stress Scale (PSS) (r = -0.45), while self-efficacy correlated moderately and negatively with the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) total scores in all patients (r = -0.5). Nine patients (43%) reported that they learned something meaningful about themselves after the feedback discussion of their individual time series. CONCLUSION The results support the feasibility of high-frequency monitoring of psychological states and processes in routine EMU settings. Repeated daily collections four times per day of psychological surveys allow for the assessment of highly resolved, equidistant time series data, which gives insight into psychological states and processes during EMU admission.
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Affiliation(s)
- Rosa Michaelis
- Department of Neurology, Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria; Department of Neurology, Gemeinschaftskrankenhaus Herdecke, Herdecke, Germany; Integrated Curriculum for Anthroposophical Medicine (ICURAM), Witten/Herdecke University, Herdecke, Germany.
| | - Helmut Schöller
- Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Salzburg, Austria; Department of Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
| | - Yvonne Höller
- Department of Neurology, Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria
| | - Gudrun Kalss
- Department of Neurology, Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria
| | - Margarita Kirschner
- Department of Neurology, Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria
| | - Elisabeth Schmid
- Department of Neurology, Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria
| | - Günter Schiepek
- Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Salzburg, Austria; Department of Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
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Abstract
The current paradigm for treatment of epilepsy begins with trials of antiepileptic drugs, followed by evaluation for resective brain surgery in drug-resistant patients. If surgery is not possible or fails to control seizures, some patients benefit from implanted neurostimulation devices. In addition to their therapeutic benefit, some of these devices have diagnostic capability enabling recordings of brain activity with unprecedented chronicity. Two recent studies using different devices for chronic EEG (i.e., over months to years) yielded convergent findings of daily and multiday cycles of brain activity that help explain seizure timing. Knowledge of these patient-specific cycles can be leveraged to gauge and forecast seizure risk, empowering patients to adopt risk-stratified treatment strategies and behavioral modifications. We review evidence that epilepsy is a cyclical disorder, and we argue that implanted monitoring devices should be offered earlier in the treatment paradigm. Chronic EEG would allow pharmacologic treatments tailored to days of high seizure risk-here termed chronotherapy-and would help characterize long timescale seizure dynamics to improve subsequent surgical planning. Coupled with neuromodulation, the proposed approach could improve quality of life for patients and decrease the number ultimately requiring resective surgery. We outline challenges for chronic monitoring and seizure forecasting that demand close collaboration among engineers, neurosurgeons, and neurologists.
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Affiliation(s)
- Maxime O Baud
- From the Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology (M.O.B.), Inselspital, Bern University Hospital, University of Bern; Wyss Center for Bio- and Neuro-engineering (M.O.B.), Geneva, Switzerland; and Department of Neurology and Weill Institute for Neurosciences (V.R.R.), University of California, San Francisco.
| | - Vikram R Rao
- From the Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology (M.O.B.), Inselspital, Bern University Hospital, University of Bern; Wyss Center for Bio- and Neuro-engineering (M.O.B.), Geneva, Switzerland; and Department of Neurology and Weill Institute for Neurosciences (V.R.R.), University of California, San Francisco
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Besag FMC, Vasey MJ. Prodrome in epilepsy. Epilepsy Behav 2018; 83:219-233. [PMID: 29650466 DOI: 10.1016/j.yebeh.2018.03.019] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 03/04/2018] [Accepted: 03/16/2018] [Indexed: 11/29/2022]
Abstract
BACKGROUND Prodromal symptoms (PS) of epileptic seizures are clinically well-recognized but relatively little researched. The purpose of this review was to examine the evidence in the literature for the existence of prodrome and the reported frequency and nature of prodromal characteristics. METHODS We performed a PubMed review of the clinical characteristics, frequency, and duration of PS in papers published between 2007 and 2017. We also reviewed findings from prospective studies into the predictive performance of prodrome. In a second analysis, we reviewed studies reporting a single symptom/sign of prodrome. RESULTS In 8 studies reporting on the prevalence of prodrome, we found a mean frequency of 21.9%. The most frequent symptoms were "funny feeling" (10.4%), confusion (9.0%), anxiety (8.6%), and irritability (7.7%), but other features were also reported. The duration of prodrome was typically between 10min and 3days, with most prodromes lasting for between 30min and 24h. In studies that reported a single prodromal symptom/sign, headache was the most frequent: 8% with a range of between 1.2 and 30%. CONCLUSIONS Prodromes are characterized by a broad spectrum of preictal symptoms that may be experienced for a duration of between 10min and several days, which usually persist until seizure onset. Opinion is divided on their precise nature and value as predictors of seizures. A greater understanding of prodromes might offer insights into the preictal period and hold promise for new seizure management therapies.
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Affiliation(s)
- Frank M C Besag
- East London Foundation NHS Trust, 5-7 Rush Court, Bedford MK40 3JT, UK; University College, London, UK; King's College, London, UK.
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Seizure Forecasting from Idea to Reality. Outcomes of the My Seizure Gauge Epilepsy Innovation Institute Workshop. eNeuro 2017; 4:eN-OPN-0349-17. [PMID: 29291239 PMCID: PMC5744646 DOI: 10.1523/eneuro.0349-17.2017] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 11/20/2017] [Accepted: 11/21/2017] [Indexed: 01/09/2023] Open
Abstract
The Epilepsy Innovation Institute (Ei2) is a new research program of the Epilepsy Foundation designed to be an innovation incubator for epilepsy. Ei2 research areas are selected based on community surveys that ask people impacted by epilepsy what they would like researchers to focus on. In their 2016 survey, unpredictability was selected as a top issue regardless of seizure frequency or severity. In response to this need, Ei2 launched the My Seizure Gauge challenge, with the end goal of creating a personalized seizure advisory system device. Prior to moving forward, Ei2 convened a diverse group of stakeholders from people impacted by epilepsy and clinicians, to device developers and data scientists, to basic science researchers and regulators, for a state of the science assessment on seizure forecasting. From the discussions, it was clear that we are at an exciting crossroads. With the advances in bioengineering, we can utilize digital markers, wearables, and biosensors as parameters for a seizure-forecasting algorithm. There are also over a thousand individuals who have been implanted with ambulatory intracranial EEG recording devices. Pairing up peripheral measurements to brain states could identify new relationships and insights. Another key component is the heterogeneity of the relationships indicating that pooling findings across groups is suboptimal, and that data collection will need to be done on longer time scales to allow for individualization of potential seizure-forecasting algorithms.
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Shegog R, Begley CE. Clinic-Based Mobile Health Decision Support to Enhance Adult Epilepsy Self-Management: An Intervention Mapping Approach. Front Public Health 2017; 5:256. [PMID: 29043247 PMCID: PMC5632356 DOI: 10.3389/fpubh.2017.00256] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 09/08/2017] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Epilepsy is a neurological disorder involving recurrent seizures. It affects approximately 5 million people in the U.S. To optimize their quality of life people with epilepsy are encouraged to engage in self-management (S-M) behaviors. These include managing their treatment (e.g., adhering to anti-seizure medication and clinical visit schedules), managing their seizures (e.g., responding to seizure episodes), managing their safety (e.g., monitoring and avoiding environmental seizure triggers), and managing their co-morbid conditions (e.g., anxiety, depression). The clinic-based Management Information Decision Support Epilepsy Tool (MINDSET) is a decision-support system founded on theory and empirical evidence. It is designed to increase awareness by adult patients (≥18 years) and their health-care provider regarding the patient's epilepsy S-M behaviors, facilitate communication during the clinic visit to prioritize S-M goals and strategies commensurate with the patient's needs, and increase the patient's self-efficacy to achieve those goals. METHODS The purpose of this paper is to describe the application of intervention mapping (IM) to develop, implement, and formatively evaluate the clinic-based MINDSET prototype and in developing implementation and evaluation plans. Deliverables comprised a logic model of the problem (IM Step 1); matrices of program objectives (IM Step 2); a program planning document comprising scope, sequence, theory-based methods, and practical strategies (IM Step 3); a functional MINDSET program prototype (IM Step 4); plans for implementation (IM Step 5); and evaluation (IM Step 6). IM provided a logical and systematic approach to developing and evaluating clinic-based decision support toward epilepsy S-M.
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Affiliation(s)
- Ross Shegog
- School of Public Health, University of Texas Health Science Center, Houston, TX, United States
| | - Charles E. Begley
- School of Public Health, University of Texas Health Science Center, Houston, TX, United States
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Seizure self-prediction: Myth or missed opportunity? Seizure 2017; 51:180-185. [PMID: 28892758 DOI: 10.1016/j.seizure.2017.08.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 08/22/2017] [Accepted: 08/25/2017] [Indexed: 11/24/2022] Open
Abstract
PURPOSE Many patients report being able to predict their own seizures, and yet most seizures appear to strike out of the blue. This inherent contradiction makes the topic of seizure self-prediction controversial as well as difficult to study. Here we review the evidence for whether this ability exists, how many patients are capable of self-prediction and the nature of this capability, and whether this could provide a target for intervention. METHODS Systematic searches of bibliographic databases including MEDLINE, EMBASE and PsycINFO through OVID were performed to identify relevant papers which were then screened by the study authors for inclusion in the study. 18 papers were selected for inclusion as the focus of this review. RESULTS On the basis of two studies, between 17% and 41% of patients demonstrate a significantly greater than chance ability to predict an upcoming seizure in the following 12-h time window. This risk is correlated with self-reported anxiety, stress, sleep deprivation, mood and certain prodromal symptoms. However, there is no evidence for any subjective experience which directly heralds an imminent seizure. Thus, while patients may be aware of seizure risk, and have some ability to predict seizure occurrence over a wide time window, they are unable to subjectively recognise seizure onset in advance. CONCLUSION Utilising subjectively acquired knowledge of seizure risk may provide a widely implementable tool for targeted intervention. The risk fluctuates over a time course appropriate for pharmacotherapy which may improve seizure control and the side-effect profile of anti-epileptic medication.
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Abstract
The aim of this study is to develop a treatment diary for patients receiving spasticity treatment including botulinum toxin injection and physiotherapy and/or occupational therapy. The diary focuses on problems triggered by skeletal muscle overactivity; agreed goals for treatment and the patient’s self-evaluation of achievement on the Goal Attainment Scale; which skeletal muscles were injected; physiotherapists’ and occupational therapists’ evaluation of the patients’ achievement of objectives on the Goal Attainment Scale; and proposals for optimization of treatment and changing goals. The evaluation included a satisfaction questionnaire and the WHO-QoL BREF and WHO-5 well-being score. Overall, 10 patients were enrolled in the pilot study. The patients were generally satisfied with the diary, found that it involved them more in their treatment and made it easier to set personal goals, and found it worth the time spent using it. However, no clear advantage in relation to their quality of life (WHO-QoL BREF and WHO-5 well-being score) was reported.
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Galtrey CM, Mula M, Cock HR. Stress and epilepsy: fact or fiction, and what can we do about it? Pract Neurol 2016; 16:270-8. [PMID: 26933239 DOI: 10.1136/practneurol-2015-001337] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/08/2016] [Indexed: 11/04/2022]
Abstract
People with epilepsy report that stress is their most common trigger for seizures and some believe it caused their epilepsy in the first place. The extensive preclinical, epidemiological and clinical studies examining the link between stress and epilepsy have given confusing results; the clinical studies in particular are fraught with confounders. However stress is clearly bad for health, and we now have substantial preclinical evidence suggesting that chronic stress worsens epilepsy; in selected cases it may even be a causal factor for epilepsy. Healthcare professionals working with people with epilepsy should pay more attention to stress in clinical practice. This review includes some practical advice and guidance for stress screening and management.
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Affiliation(s)
- Clare M Galtrey
- Epilepsy Group, Atkinson Morley Regional Neuroscience Centre, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Marco Mula
- Epilepsy Group, Atkinson Morley Regional Neuroscience Centre, St George's University Hospitals NHS Foundation Trust, London, UK Institute of Biomedical & Medical Education, St George's University of London, London, UK
| | - Hannah R Cock
- Epilepsy Group, Atkinson Morley Regional Neuroscience Centre, St George's University Hospitals NHS Foundation Trust, London, UK Institute of Biomedical & Medical Education, St George's University of London, London, UK
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Kotwas I, McGonigal A, Trebuchon A, Bastien-Toniazzo M, Nagai Y, Bartolomei F, Micoulaud-Franchi JA. Self-control of epileptic seizures by nonpharmacological strategies. Epilepsy Behav 2016; 55:157-64. [PMID: 26780213 DOI: 10.1016/j.yebeh.2015.12.023] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Revised: 12/12/2015] [Accepted: 12/14/2015] [Indexed: 02/04/2023]
Abstract
Despite the unpredictability of epileptic seizures, many patients report that they can anticipate seizure occurrence. Using certain alert symptoms (i.e., auras, prodromes, precipitant factors), patients can adopt behaviors to avoid injury during and after the seizure or may implement spontaneous cognitive and emotional strategies to try to control the seizure itself. From the patient's view point, potential means of enhancing seizure prediction and developing seizure control supports are seen as very important issues, especially when the epilepsy is drug-resistant. In this review, we first describe how some patients anticipate their seizures and whether this is effective in terms of seizure prediction. Secondly, we examine how these anticipatory elements might help patients to prevent or control their seizures and how the patient's neuropsychological profile, specifically parameters of perceived self-control (PSC) and locus of control (LOC), might impact these strategies and quality of life (QOL). Thirdly, we review the external supports that can help patients to better predict seizures. Finally, we look at nonpharmacological means of increasing perceived self-control and achieving potential reduction of seizure frequency (i.e., stress-based and arousal-based strategies). In the past few years, various approaches for detection and control of seizures have gained greater interest, but more research is needed to confirm a positive effect on seizure frequency as well as on QOL.
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Affiliation(s)
- Iliana Kotwas
- Laboratoire Parole et Langage UMR 7309, Aix-Marseille Université, Marseille, France
| | - Aileen McGonigal
- Service de Neurophysiologie Clinique, Centre Hospitalo Universitaire de la Timone, 264, Rue Saint-Pierre, 13005 Marseille, France; Unité Mixte INSERM Epilepsie et Cognition UMR 751, 27 Bd Jean Moulin, 13385 Marseille Cedex 05, France
| | - Agnès Trebuchon
- Service de Neurophysiologie Clinique, Centre Hospitalo Universitaire de la Timone, 264, Rue Saint-Pierre, 13005 Marseille, France; Unité Mixte INSERM Epilepsie et Cognition UMR 751, 27 Bd Jean Moulin, 13385 Marseille Cedex 05, France
| | | | - Yoko Nagai
- Psychiatry, Brighton and Sussex Medical School, University of Sussex, UK; Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, UK
| | - Fabrice Bartolomei
- Service de Neurophysiologie Clinique, Centre Hospitalo Universitaire de la Timone, 264, Rue Saint-Pierre, 13005 Marseille, France; Unité Mixte INSERM Epilepsie et Cognition UMR 751, 27 Bd Jean Moulin, 13385 Marseille Cedex 05, France
| | - Jean-Arthur Micoulaud-Franchi
- Service d'Explorations Fonctionnelles du Système Nerveux, Clinique du Sommeil, CHU de Bordeaux, Place Amélie Raba-Léon, 33076 Bordeaux, France; USR CNRS 3413 SANPSY, CHU Pellegrin, Université de Bordeaux, France
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Galtrey CM, Cock HR. Stress and Epilepsy. NEUROPSYCHIATRIC SYMPTOMS OF NEUROLOGICAL DISEASE 2016. [DOI: 10.1007/978-3-319-22159-5_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Illingworth JL, Watson P, Xu S, Manford M, Ring H. A method for identifying associations between seizures and possible trigger events in adults with intellectual disability. Epilepsia 2015; 56:1812-8. [PMID: 26385590 DOI: 10.1111/epi.13137] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/05/2015] [Indexed: 11/27/2022]
Abstract
OBJECTIVES Precipitants of seizures are often reported by patients and carers, but the accuracy of these claims remains unknown. Focusing on epilepsy in people with intellectual disability (ID), the aims of this work were to (1) identify a set of methods for assessing the validity of reported seizure triggers in individual patients; and (2) undertake an initial assessment of the ease of implementation and acceptability of the method by applying it to a series of cases. METHODS Data collection materials (developed with carer involvement) consisted primarily of carer diaries of seizure and trigger occurrences. Statistical analysis of diary data was using the self-controlled case series method. Unlike previously used methods, the analysis method included a means of choosing the time window, following trigger exposure, during which changes in seizure likelihood are to be assessed. RESULTS The method developed was trialed in five adults with ID and epilepsy, who had a range of ID severities and living circumstances. Examples of the application of the method in two of the five cases are presented for illustrative purposes. The method was acceptable to participants and most aspects successfully implemented. SIGNIFICANCE This method may be useful for clinicians and researchers wishing to investigate possible triggers in individual patients with epilepsy and ID. It also supports the identification of a statistically defined time window following exposure to a precipitant, during which the risk of developing a seizure is increased. The identification of such a window has value not just in contributing to clinical management, but also in guiding future work into the mechanisms of seizure precipitation.
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Affiliation(s)
- Josephine L Illingworth
- Department of Psychiatry, University of Cambridge, Douglas House, Cambridge, United Kingdom.,NIHR Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East of England, Cambridge, United Kingdom
| | - Peter Watson
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, United Kingdom
| | - Stanley Xu
- Institute for Health Research, Kaiser Permanente Colorado, Denver, Colorado, U.S.A.,School of Public Health, University of Colorado, Aurora, Colorado, U.S.A
| | - Mark Manford
- Cambridge University Hospitals NHS Trust, Cambridge, United Kingdom.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Howard Ring
- Department of Psychiatry, University of Cambridge, Douglas House, Cambridge, United Kingdom.,NIHR Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East of England, Cambridge, United Kingdom.,Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, United Kingdom
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Hixson JD, Barnes D, Parko K, Durgin T, Van Bebber S, Graham A, Wicks P. Patients optimizing epilepsy management via an online community: the POEM Study. Neurology 2015; 85:129-36. [PMID: 26085605 PMCID: PMC4515038 DOI: 10.1212/wnl.0000000000001728] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Accepted: 03/18/2015] [Indexed: 01/17/2023] Open
Abstract
Objective: The study objective was to test whether engaging in an online patient community improves self-management and self-efficacy in veterans with epilepsy. Methods: The study primary outcomes were validated questionnaires for self-management (Epilepsy Self-Management Scale [ESMS]) and self-efficacy (Epilepsy Self-Efficacy Scale [ESES]). Results were based on within-subject comparisons of pre- and postintervention survey responses of veterans with epilepsy engaging with the PatientsLikeMe platform for a period of at least 6 weeks. Analyses were based on both completer and intention-to-treat scenarios. Results: Of 249 eligible participants enrolled, 92 individuals completed both surveys. Over 6 weeks, completers improved their epilepsy self-management (ESMS total score from 139.7 to 142.7, p = 0.02) and epilepsy self-efficacy (ESES total score from 244.2 to 254.4, p = 0.02) scores, with greatest impact on an information management subscale (ESMS–information management total score from 20.3 to 22.4, p < 0.001). Results were similar in intention-to-treat analyses. Median number of logins, postings to forums, leaving profile comments, and sending private messages were more common in completers than noncompleters. Conclusions: An internet-based psychosocial intervention was feasible to implement in the US veteran population and increased epilepsy self-management and self-efficacy scores. The greatest improvement was noted for information management behaviors. Patients with chronic conditions are increasingly encouraged to self-manage their condition, and digital communities have potential advantages, such as convenience, scalability to large populations, and building a community support network. Classification of evidence: This study provides Class IV evidence that for patients with epilepsy, engaging in an online patient community improves self-management and self-efficacy.
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Affiliation(s)
- John D Hixson
- From the Departments of Neurology (J.D.H., K.P.), Psychiatry (D.B.), and Epidemiology & Biostatistics (D.B.), University of California San Francisco and the SF VA Medical Center; US Medical Affairs (T.D.), UCB, Inc.; Northern California Institute for Research and Education and the SF VA Medical Center (S.V.B.); and PatientsLikeMe (A.G., P.W.).
| | - Deborah Barnes
- From the Departments of Neurology (J.D.H., K.P.), Psychiatry (D.B.), and Epidemiology & Biostatistics (D.B.), University of California San Francisco and the SF VA Medical Center; US Medical Affairs (T.D.), UCB, Inc.; Northern California Institute for Research and Education and the SF VA Medical Center (S.V.B.); and PatientsLikeMe (A.G., P.W.)
| | - Karen Parko
- From the Departments of Neurology (J.D.H., K.P.), Psychiatry (D.B.), and Epidemiology & Biostatistics (D.B.), University of California San Francisco and the SF VA Medical Center; US Medical Affairs (T.D.), UCB, Inc.; Northern California Institute for Research and Education and the SF VA Medical Center (S.V.B.); and PatientsLikeMe (A.G., P.W.)
| | - Tracy Durgin
- From the Departments of Neurology (J.D.H., K.P.), Psychiatry (D.B.), and Epidemiology & Biostatistics (D.B.), University of California San Francisco and the SF VA Medical Center; US Medical Affairs (T.D.), UCB, Inc.; Northern California Institute for Research and Education and the SF VA Medical Center (S.V.B.); and PatientsLikeMe (A.G., P.W.)
| | - Stephanie Van Bebber
- From the Departments of Neurology (J.D.H., K.P.), Psychiatry (D.B.), and Epidemiology & Biostatistics (D.B.), University of California San Francisco and the SF VA Medical Center; US Medical Affairs (T.D.), UCB, Inc.; Northern California Institute for Research and Education and the SF VA Medical Center (S.V.B.); and PatientsLikeMe (A.G., P.W.)
| | - Arianne Graham
- From the Departments of Neurology (J.D.H., K.P.), Psychiatry (D.B.), and Epidemiology & Biostatistics (D.B.), University of California San Francisco and the SF VA Medical Center; US Medical Affairs (T.D.), UCB, Inc.; Northern California Institute for Research and Education and the SF VA Medical Center (S.V.B.); and PatientsLikeMe (A.G., P.W.)
| | - Paul Wicks
- From the Departments of Neurology (J.D.H., K.P.), Psychiatry (D.B.), and Epidemiology & Biostatistics (D.B.), University of California San Francisco and the SF VA Medical Center; US Medical Affairs (T.D.), UCB, Inc.; Northern California Institute for Research and Education and the SF VA Medical Center (S.V.B.); and PatientsLikeMe (A.G., P.W.)
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An integrative review of the benefits of self-management interventions for adults with epilepsy. Epilepsy Behav 2015; 45:195-204. [PMID: 25843342 DOI: 10.1016/j.yebeh.2015.01.026] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Revised: 01/15/2015] [Accepted: 01/21/2015] [Indexed: 11/24/2022]
Abstract
The life-limiting effects of epilepsy are well documented in the literature, where the management of epilepsy and seizure control relies heavily on the self-management abilities of the individual. The psychosocial impact of epilepsy on the person and their family is profound and has been studied extensively. Interventions such as educational programs and lifestyle management education to improve self-mastery and quality of life in people with epilepsy are not necessarily integrated in standard care practices. The aim of this integrative review was to systematically identify and appraise research that reported findings related to self-management interventions for adults with epilepsy. A search of bibliographic databases was conducted, and a total of n=14 articles were included in this review. The main finding was that self-management education for adults with epilepsy shows promise to improving knowledge and self-confidence in managing one's own condition including the management of the psychosocial stressors, improvement in seizure control, and enhancement of quality of life. Self-management interventions were delivered in diverse formats, and the inclusion of this type of intervention should be part of the comprehensive care for people living with epilepsy.
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