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Ding TY, Gagliano L, Jahani A, Toffa DH, Nguyen DK, Bou Assi E. Epileptic seizure forecasting with wearable-based nocturnal sleep features. Epilepsia Open 2024. [PMID: 38980984 DOI: 10.1002/epi4.13008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 06/15/2024] [Accepted: 06/23/2024] [Indexed: 07/11/2024] Open
Abstract
OBJECTIVE Non-invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure-day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt. METHODS Seventy-eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high-frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo-prospective seizure-day forecasting, with forecasting horizons of 16- and 24-h to include both diurnal and nocturnal seizures (24-h) or diurnal seizures only (16-h). The algorithm's performance was assessed using a nested leave-one-patient-out cross-validation approach. RESULTS Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16- and 24-h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16-h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24-h horizon. SIGNIFICANCE Smart shirt-based nocturnal sleep analysis holds promise as a non-invasive approach for seizure-day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long-term recordings, could pave the way for the development of innovative and practical seizure forecasting devices. PLAIN LANGUAGE SUMMARY Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach.
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Affiliation(s)
- Tian Yue Ding
- Centre de Recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
| | - Laura Gagliano
- Centre de Recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
| | - Amirhossein Jahani
- Centre de Recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
| | - Denahin H Toffa
- Centre de Recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
| | - Dang K Nguyen
- Centre de Recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
- Department of Neuroscience, Université de Montréal, Montréal, Québec, Canada
| | - Elie Bou Assi
- Centre de Recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
- Department of Neuroscience, Université de Montréal, Montréal, Québec, Canada
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Sisodiya SM, Gulcebi MI, Fortunato F, Mills JD, Haynes E, Bramon E, Chadwick P, Ciccarelli O, David AS, De Meyer K, Fox NC, Davan Wetton J, Koltzenburg M, Kullmann DM, Kurian MA, Manji H, Maslin MA, Matharu M, Montgomery H, Romanello M, Werring DJ, Zhang L, Friston KJ, Hanna MG. Climate change and disorders of the nervous system. Lancet Neurol 2024; 23:636-648. [PMID: 38760101 DOI: 10.1016/s1474-4422(24)00087-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/12/2024] [Accepted: 02/27/2024] [Indexed: 05/19/2024]
Abstract
Anthropogenic climate change is affecting people's health, including those with neurological and psychiatric diseases. Currently, making inferences about the effect of climate change on neurological and psychiatric diseases is challenging because of an overall sparsity of data, differing study methods, paucity of detail regarding disease subtypes, little consideration of the effect of individual and population genetics, and widely differing geographical locations with the potential for regional influences. However, evidence suggests that the incidence, prevalence, and severity of many nervous system conditions (eg, stroke, neurological infections, and some mental health disorders) can be affected by climate change. The data show broad and complex adverse effects, especially of temperature extremes to which people are unaccustomed and wide diurnal temperature fluctuations. Protective measures might be possible through local forecasting. Few studies project the future effects of climate change on brain health, hindering policy developments. Robust studies on the threats from changing climate for people who have, or are at risk of developing, disorders of the nervous system are urgently needed.
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Affiliation(s)
- Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; Chalfont Centre for Epilepsy, Chalfont-St-Peter, UK.
| | - Medine I Gulcebi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; Chalfont Centre for Epilepsy, Chalfont-St-Peter, UK
| | - Francesco Fortunato
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; Chalfont Centre for Epilepsy, Chalfont-St-Peter, UK
| | - James D Mills
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; Chalfont Centre for Epilepsy, Chalfont-St-Peter, UK
| | - Ethan Haynes
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; Chalfont Centre for Epilepsy, Chalfont-St-Peter, UK
| | - Elvira Bramon
- Division of Psychiatry, University College London, London, UK
| | - Paul Chadwick
- Centre for Behaviour Change, University College London, London, UK
| | - Olga Ciccarelli
- Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK; National Institute of Health Research University College London Hospitals Biomedical Research Centre, London, UK
| | - Anthony S David
- Division of Psychiatry, University College London, London, UK
| | - Kris De Meyer
- UCL Climate Action Unit, University College London, London, UK
| | - Nick C Fox
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK; Department of the UK Dementia Research Institute, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | | - Martin Koltzenburg
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Dimitri M Kullmann
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Manju A Kurian
- Department of Developmental Neurosciences, Zayed Centre for Research into Rare Disease in Children, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Hadi Manji
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Mark A Maslin
- Department of Geography, University College London, London, UK; Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
| | - Manjit Matharu
- Headache and Facial Pain Group, UCL Queen Square Institute of Neurology, UCL and the National Hospital for Neurology and Neurosurgery, London, UK
| | - Hugh Montgomery
- Department of Medicine, University College London, London, UK
| | - Marina Romanello
- Institute for Global Health, University College London, London, UK
| | - David J Werring
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Lisa Zhang
- Centre for Behaviour Change, University College London, London, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Michael G Hanna
- Centre for Neuromuscular Diseases, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, University College London, London, UK; MRC International Centre for Genomic Medicine in Neuromuscular Diseases, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, University College London, London, UK
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Carè M, Chiappalone M, Cota VR. Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function. Front Neurosci 2024; 18:1363128. [PMID: 38516316 PMCID: PMC10954825 DOI: 10.3389/fnins.2024.1363128] [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: 12/29/2023] [Accepted: 02/22/2024] [Indexed: 03/23/2024] Open
Abstract
Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.
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Affiliation(s)
- Marta Carè
- IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Michela Chiappalone
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, Genova, Italy
- Rehab Technologies Lab, Istituto Italiano di Tecnologia, Genova, Italy
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4
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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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Pari E, Sheibani M, Sazegar MR, Mir S, Moazam A, Khalilzadeh M, Motevalian M. Comparison of neuroprotective effects of a topiramate-loaded biocomposite based on mesoporous silica nanoparticles with pure topiramate against methylphenidate-induced neurodegeneration. Mol Biol Rep 2024; 51:65. [PMID: 38170306 DOI: 10.1007/s11033-023-09011-1] [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: 08/16/2023] [Accepted: 11/07/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Methylphenidate (MPH) abuse has been criticized for its role in neurodegeneration. Also, a high risk of seizure was reported in the first month of MPH treatment. Topiramate, a broad-spectrum Antiepileptic Drug (AED), has been used as a neuroprotective agent in both aforementioned complications. Nanotechnology is introduced to increase desirable neurological treatment with minimum side effects. We aimed to investigate the potential neuroprotective activity of topiramate loaded on nanoparticles. METHODS AND RESULTS MTT assay was performed to evaluate the cellular cytotoxicity of Mesoporous Silica Nanoparticles (MSN). Male rats were randomly divided into eight groups. Rats received an intraperitoneal (i.p) MPH (10 mg/kg) injection and a daily oral dose of topiramate (TPM, 30 mg/kg), MSN with Zn core (10 and 30 mg/kg), and MSN with Cu core (10 and 30 mg/kg) for three weeks. On day 21, a seizure was induced by a single injection of pentylenetetrazole (PTZ) to evaluate the protective effects of TPM-loaded nanoparticles on seizure latency and duration following MPH-induced neurotoxicity. Moreover, the hippocampal content of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), malondialdehyde (MDA), and the anti-oxidant enzymes (SOD, GPx, and GR) activities were assessed. Also, BAX and Bcl-2 as two main apoptotic markers were evaluated. RESULTS MPH neurotoxicity was observed as a raised duration and reduced latency in PTZ-induced seizure. However, TPM-loaded MSN with Zn species (NE) treatment reduced the duration and improved the latency time. Also, NE and, somewhat, TPM-loaded MSN with Cu species (NM) administration reduced inflammatory cytokines, MDA, and Bax levels and increased activities in the rat hippocampus. CONCLUSION TPM-loaded nanoparticles could be used as neuroprotective agents against MPH-induced neurodegeneration by improving seizure parameters and reducing inflammatory, oxidant, and apoptotic factors.
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Affiliation(s)
- Erfan Pari
- Faculty of Chemistry, North Tehran Branch, Islamic Azad University, Hakimiyeh, Tehran, Iran
| | - Mohammad Sheibani
- Department of Pharmacology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Razi Drug Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Sazegar
- Faculty of Chemistry, North Tehran Branch, Islamic Azad University, Hakimiyeh, Tehran, Iran.
| | - Saeedeh Mir
- Faculty of Chemistry, North Tehran Branch, Islamic Azad University, Hakimiyeh, Tehran, Iran
| | - Ashrafsadat Moazam
- Department of Pharmacology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mina Khalilzadeh
- Experimental Medicine Research Centre, Tehran University of Medical Sciences, Tehran, Iran
| | - Manijeh Motevalian
- Department of Pharmacology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
- Razi Drug Research Center, Iran University of Medical Sciences, Tehran, Iran.
- Pharmacology Department, Medical School & Razi Drug Research Center, Iran University of Medical Sciences, Tehran, Iran.
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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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Stirling RE, Hidajat CM, Grayden DB, D’Souza WJ, Naim-Feil J, Dell KL, Schneider LD, Nurse E, Freestone D, Cook MJ, Karoly PJ. Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration. Brain 2023; 146:2803-2813. [PMID: 36511881 PMCID: PMC10316760 DOI: 10.1093/brain/awac476] [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: 08/08/2022] [Revised: 10/24/2022] [Accepted: 11/26/2022] [Indexed: 08/21/2023] Open
Abstract
Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P < 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.
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Affiliation(s)
- Rachel E Stirling
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Research Department, Seer Medical, Melbourne 3000, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
| | - Cindy M Hidajat
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Wendyl J D’Souza
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Jodie Naim-Feil
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
| | - Katrina L Dell
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | | | - Ewan Nurse
- Research Department, Seer Medical, Melbourne 3000, Australia
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Dean Freestone
- Research Department, Seer Medical, Melbourne 3000, Australia
| | - Mark J Cook
- Research Department, Seer Medical, Melbourne 3000, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Department of Medicine, St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy 3065, Australia
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
- Research Department, Seer Medical, Melbourne 3000, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville 3010, Australia
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Sheibani M, Shayan M, Khalilzadeh M, Ghasemi M, Dehpour AR. Orexin receptor antagonists in the pathophysiology and treatment of sleep disorders and epilepsy. Neuropeptides 2023; 99:102335. [PMID: 37003137 DOI: 10.1016/j.npep.2023.102335] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 03/20/2023] [Accepted: 03/23/2023] [Indexed: 03/29/2023]
Abstract
The correlation between sleep and epilepsy has been argued over the past decades among scientists. Although the similarities and contrasts between sleep and epilepsy had been considered, their intertwined nature was not revealed until the nineteenth century. Sleep is recognized as a recurring state of mind and body through alternating brain electrical activities. It is documented that sleep disorders are associated with epilepsy. The origin, suppression, and spread of seizures are affected by sleep. As such, in patients with epilepsy, sleep disorders are a frequent comorbidity. Meanwhile, orexin, a wake-promoting neuropeptide, provides a bidirectional effect on both sleep and epilepsy. Orexin and its cognate receptors, orexin receptor type 1 (OX1R) and type 2 (OX2R), orchestrate their effects by activating various downstream signaling pathways. Although orexin was considered a therapeutic target in insomnia shortly after its discovery, its potential usefulness for psychiatric disorders and epileptic seizures has been suggested in the pre-clinical studies. This review aimed to discuss whether the relationship between sleep, epilepsy, and orexin is clearly reciprocal.
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Affiliation(s)
- Mohammad Sheibani
- Department of Pharmacology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Razi Drug Research Center, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Maryam Shayan
- Experimental Medicine Research Centre, Tehran University of Medical Sciences, Tehran, Iran; Department of Pharmacology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mina Khalilzadeh
- Experimental Medicine Research Centre, Tehran University of Medical Sciences, Tehran, Iran; Department of Pharmacology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Ghasemi
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.
| | - Ahmad Reza Dehpour
- Experimental Medicine Research Centre, Tehran University of Medical Sciences, Tehran, Iran; Department of Pharmacology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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Stredny C, Rotenberg A, Leviton A, Loddenkemper T. Systemic inflammation as a biomarker of seizure propensity and a target for treatment to reduce seizure propensity. Epilepsia Open 2023; 8:221-234. [PMID: 36524286 PMCID: PMC9978091 DOI: 10.1002/epi4.12684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
People with diabetes can wear a device that measures blood glucose and delivers just the amount of insulin needed to return the glucose level to within bounds. Currently, people with epilepsy do not have access to an equivalent wearable device that measures a systemic indicator of an impending seizure and delivers a rapidly acting medication or other intervention (e.g., an electrical stimulus) to terminate or prevent a seizure. Given that seizure susceptibility is reliably increased in systemic inflammatory states, we propose a novel closed-loop device where release of a fast-acting therapy is governed by sensors that quantify the magnitude of systemic inflammation. Here, we review the evidence that patients with epilepsy have raised levels of systemic indicators of inflammation than controls, and that some anti-inflammatory drugs have reduced seizure occurrence in animals and humans. We then consider the options of what might be incorporated into a responsive anti-seizure system.
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Affiliation(s)
- Coral Stredny
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Alexander Rotenberg
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Alan Leviton
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
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Reynolds A, Vranic-Peters M, Lai A, Grayden DB, Cook MJ, Peterson A. Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review. Epilepsia 2023; 64:1125-1174. [PMID: 36790369 DOI: 10.1111/epi.17548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 02/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.
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Affiliation(s)
- Ashley Reynolds
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Michaela Vranic-Peters
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Alan Lai
- Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Grayden
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Andre Peterson
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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11
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Gagliano L, Ding TY, Toffa DH, Beauregard L, Robert M, Lesage F, Sawan M, Nguyen DK, Bou Assi E. Decrease in wearable-based nocturnal sleep efficiency precedes epileptic seizures. Front Neurol 2023; 13:1089094. [PMID: 36712456 PMCID: PMC9875007 DOI: 10.3389/fneur.2022.1089094] [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: 11/03/2022] [Accepted: 12/19/2022] [Indexed: 01/12/2023] Open
Abstract
Introduction While it is known that poor sleep is a seizure precipitant, this association remains poorly quantified. This study investigated whether seizures are preceded by significant changes in sleep efficiency as measured by a wearable equipped with an electrocardiogram, respiratory bands, and an accelerometer. Methods Nocturnal recordings from 47 people with epilepsy hospitalized at our epilepsy monitoring unit were analyzed (304 nights). Sleep metrics during nights followed by epileptic seizures (24 h post-awakening) were compared to those of nights which were not. Results Lower sleep efficiency (percentage of sleep during the night) was found in the nights preceding seizure days (p < 0.05). Each standard deviation decrease in sleep efficiency and increase in wake after sleep onset was respectively associated with a 1.25-fold (95 % CI: 1.05 to 1.42, p < 0.05) and 1.49-fold (95 % CI: 1.17 to 1.92, p < 0.01) increased odds of seizure occurrence the following day. Furthermore, nocturnal seizures were associated with significantly lower sleep efficiency and higher wake after sleep onset (p < 0.05), as well as increased odds of seizure occurrence following wake (OR: 5.86, 95 % CI: 2.99 to 11.77, p < 0.001). Discussion Findings indicate lower sleep efficiency during nights preceding seizures, suggesting that wearable sensors could be promising tools for sleep-based seizure-day forecasting in people with epilepsy.
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Affiliation(s)
- Laura Gagliano
- Institute of Biomedical Engineering and the Department of Electrical Engineering, Polytechnique Montréal, Montreal, QC, Canada,Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada,*Correspondence: Laura Gagliano ✉
| | - Tian Yue Ding
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Denahin H. Toffa
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Laurence Beauregard
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Manon Robert
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Frédéric Lesage
- Institute of Biomedical Engineering and the Department of Electrical Engineering, Polytechnique Montréal, Montreal, QC, Canada
| | - Mohamad Sawan
- Institute of Biomedical Engineering and the Department of Electrical Engineering, Polytechnique Montréal, Montreal, QC, Canada,CenBRAIN, Westlake University, Hangzhou, China
| | - Dang K. Nguyen
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada,Department of Neuroscience, Université de Montréal, Montreal, QC, Canada
| | - Elie Bou Assi
- Centre de Recherche du Centre Hospitalier de L'Université de Montréal (CRCHUM), Montreal, QC, Canada,Department of Neuroscience, Université de Montréal, Montreal, QC, Canada
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12
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Xiong W, Kameneva T, Lambert E, Cook MJ, Richardson MP, Nurse ES. Forecasting psychogenic non-epileptic seizure likelihood from ambulatory EEG and ECG. J Neural Eng 2022; 19. [PMID: 36270501 DOI: 10.1088/1741-2552/ac9c97] [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: 04/14/2022] [Accepted: 10/21/2022] [Indexed: 12/24/2022]
Abstract
Objective.Critical slowing features (variance and autocorrelation) of long-term continuous electroencephalography (EEG) and electrocardiography (ECG) data have previously been used to forecast epileptic seizure onset. This study tested the feasibility of forecasting non-epileptic seizures using the same methods. In doing so, we examined if long-term cycles of brain and cardiac activity are present in clinical physiological recordings of psychogenic non-epileptic seizures (PNES).Approach.Retrospectively accessed ambulatory EEG and ECG data from 15 patients with non-epileptic seizures and no background of epilepsy were used for developing the forecasting system. The median period of recordings was 161 h, with a median of 7 non-epileptic seizures per patient. The phases of different cycles (5 min, 1 h, 6 h, 12 h, 24 h) of EEG and RR interval (RRI) critical slowing features were investigated. Forecasters were generated using combinations of the variance and autocorrelation of both EEG and the RRI of the ECG at each of the aforementioned cycle lengths. Optimal forecasters were selected as those with the highest area under the receiver-operator curve (AUC).Main results.It was found that PNES events occurred in the rising phases of EEG feature cycles of 12 and 24 h in duration at a rate significantly above chance. We demonstrated that the proposed forecasters achieved performance significantly better than chance in 8/15 of patients, and the mean AUC of the best forecaster across patients was 0.79.Significance.To our knowledge, this is the first study to retrospectively forecast non-epileptic seizures using both EEG and ECG data. The significance of EEG in the forecasting models suggests that cyclic EEG features of non-epileptic seizures exist. This study opens the potential of seizure forecasting beyond epilepsy, into other disorders of episodic loss of consciousness or dissociation.
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Affiliation(s)
- Wenjuan Xiong
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
| | - Tatiana Kameneva
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia.,Iverson Health Innovation Institute, Swinburne University of Technology, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Elisabeth Lambert
- Iverson Health Innovation Institute, Swinburne University of Technology, Melbourne, Australia.,School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
| | - Mark J Cook
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Ewan S Nurse
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia.,Seer Medical, Melbourne, Australia
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13
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Panagiotopoulou M, Papasavvas CA, Schroeder GM, Thomas RH, Taylor PN, Wang Y. Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions. Hum Brain Mapp 2022; 43:2460-2477. [PMID: 35119173 PMCID: PMC9057101 DOI: 10.1002/hbm.25796] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/30/2021] [Accepted: 01/23/2022] [Indexed: 01/14/2023] Open
Abstract
Epilepsy is recognised as a dynamic disease, where both seizure susceptibility and seizure characteristics themselves change over time. Specifically, we recently quantified the variable electrographic spatio-temporal seizure evolutions that exist within individual patients. This variability appears to follow subject-specific circadian, or longer, timescale modulations. It is therefore important to know whether continuously recorded interictaliEEG features can capture signatures of these modulations over different timescales. In this study, we analyse continuous intracranial electroencephalographic (iEEG) recordings from video-telemetry units and find fluctuations in iEEG band power over timescales ranging from minutes up to 12 days. As expected and in agreement with previous studies, we find that all subjects show a circadian fluctuation in their iEEG band power. We additionally detect other fluctuations of similar magnitude on subject-specific timescales. Importantly, we find that a combination of these fluctuations on different timescales can explain changes in seizure evolutions in most subjects above chance level. These results suggest that subject-specific fluctuations in iEEG band power over timescales of minutes to days may serve as markers of seizure modulating processes. We hope that future study can link these detected fluctuations to their biological driver(s). There is a critical need to better understand seizure modulating processes, as this will enable the development of novel treatment strategies that could minimise the seizure spread, duration or severity and therefore the clinical impact of seizures.
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Affiliation(s)
- Mariella Panagiotopoulou
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Christoforos A. Papasavvas
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Gabrielle M. Schroeder
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Rhys H. Thomas
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
| | - Peter N. Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
- UCL Queen Square Institute of Neurology, Queen SquareLondon
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
- UCL Queen Square Institute of Neurology, Queen SquareLondon
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14
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Abstract
The development of mobile health for epilepsy has grown in the last years, bringing new applications (apps) to the market and improving already existing ones. In this systematic review, we analyse the scope of mobile apps for seizure detection and epilepsy self-management, with two research questions in mind: what are the characteristics of current solutions and do they meet users’ requirements? What should be considered when designing mobile health for epilepsy? We used PRISMA methodology to search within App Store and Google Play Store from February to April of 2021, reaching 55 potential apps. A more thorough analysis regarding particular features was performed on 26 of those apps. The content of these apps was evaluated in five categories, regarding if there was personalisable content; features related to medication management; what aspects of seizure log were present; what type of communication prevailed; and if there was any content related to seizure alarm or seizure action plans. Moreover, the 26 apps were evaluated through using MARS by six raters, including two neurologists. The analysis of MARS categories was performed for the top and bottom apps, to understand the core differences. Overall, the lowest MARS scores were related to engagement and information, which play a big part in long-term use, and previous studies raised the concern of assuring continuous use, especially in younger audiences. With that in mind, we identified conceptual improvement points, which were divided in three main topics: customisation, simplicity and healthcare connection. Moreover, we summarised some ideas to improve m-health apps catered around long-term adherence. We hope this work contributes to a better understanding of the current scope in mobile epilepsy management, endorsing healthcare professionals and developers to provide off-the-shelf solutions that engage patients and allows them to better manage their condition.
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15
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Tomasović S, Sremec J, Košćak Lukač J, Sičaja G, Bačić Baronica K, Ostojić V, Raifi Z, Tomić Sremec N, Plačko-Vršnak D, Srnec L, Mikec K. Weather patterns and occurrence of epileptic seizures. BMC Neurol 2022; 22:33. [PMID: 35062900 PMCID: PMC8780718 DOI: 10.1186/s12883-021-02535-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 12/26/2021] [Indexed: 11/16/2022] Open
Abstract
Background The results of various studies that have so far evaluated the influence of meteorological conditions on seizures are often divergent. No studies have been performed so far that evaluate the possible correlation between the occurrence of seizure-related events, surface and upper level atmospheric conditions and biometeorological forecasts. The aim of this study is to investigate those interactions. Methods Records of “Sveti Duh” University hospital, Zagreb, Croatia between January 2016 and May 2020 were analysed in order to determine number of patients per day who were assessed through Emergency room because of a recent epileptic seizure. The dates were assessed for surface and upper level atmospheric conditions and biometeorological forecasts. Analyses of these factors were performed for two consecutive days preceding that day, the day of seizure, as well as for the following day. Data was analysed using chi-squared test, Mann-Whitney U-test or Kruskal-Wallis test (with Conover’s test for post-hoc analyses), depending on variable type. Additionaly, logistic regression was used to study effects of variables on seizure occurrence. Statistical significance was set to p < 0.05. Results Results showed significant difference between the numbers of patients with seizure depending on upper level type on the following day. We also observed fewer daily patients with seizures when the synoptic situation on the following day was high pressure field then when it was low pressure or non-gradient pressure field (observed just during the colder part of the year), which was supported in our regression analyses. A greater frequency of seizures was observed if the biometerological prognosis was deemed unfavourable on the following day. Furthermore, our results showed significantly larger proportion of days with seizure patients in warmer, than in colder days of the year. Conclusions All of the analyzed weather-related parameters seem to be associated with daily numbers of seizures on the previous day. The reason behind this phenomenon is uncertain; however, the results indicate that weather patterns have a certain influence on patients with epilepsy, but are not yet fully understood or adequately appreciated.
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16
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Grzeskowiak CL, Dumanis SB. Seizure Forecasting: Patient and Caregiver Perspectives. Front Neurol 2021; 12:717428. [PMID: 34616352 PMCID: PMC8488220 DOI: 10.3389/fneur.2021.717428] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 08/09/2021] [Indexed: 11/20/2022] Open
Abstract
Accurate seizure forecasting is emerging as a near-term possibility due to recent advancements in machine learning and EEG technology improvements. Large-scale data curation and new data element collection through consumer wearables and digital health tools such as longitudinal seizure diary data has uncovered new possibilities for personalized algorithm development that may be used to predict the likelihood of future seizures. The Epilepsy Foundation recognized the unmet need for development in seizure forecasting following a 2016 survey where an overwhelming majority of respondents across all seizure types and frequencies reported that unpredictability of seizures had the strongest impact on their life while living with or caring for someone living with epilepsy. In early 2021, the Epilepsy Foundation conducted an updated survey among those living with epilepsies and/or their caregivers to better understand the use-cases that best suit the needs of our community as seizure forecast research advances. These results will provide researchers with insight into user-acceptance of using a forecasting tool and incorporation into their daily life. Ultimately, this input from people living with epilepsy and caregivers will provide timely feedback on what the community needs are and ensure researchers and companies first and foremost consider these needs in seizure forecasting tools/product development.
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Affiliation(s)
- Caitlin L. Grzeskowiak
- Epilepsy Foundation of America, Greater Landover, MD, United States
- *Correspondence: Caitlin L. Grzeskowiak
| | - Sonya B. Dumanis
- Epilepsy Foundation of America, Greater Landover, MD, United States
- Coalition for Aligning Science, Chevy Chase, MD, United States
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17
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Stirling RE, Maturana MI, Karoly PJ, Nurse ES, McCutcheon K, Grayden DB, Ringo SG, Heasman JM, Hoare RJ, Lai A, D'Souza W, Seneviratne U, Seiderer L, McLean KJ, Bulluss KJ, Murphy M, Brinkmann BH, Richardson MP, Freestone DR, Cook MJ. Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System. Front Neurol 2021; 12:713794. [PMID: 34497578 PMCID: PMC8419461 DOI: 10.3389/fneur.2021.713794] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 07/27/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.
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Affiliation(s)
- Rachel E. Stirling
- Seer Medical Pty Ltd, Melbourne, VIC, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Matias I. Maturana
- Seer Medical Pty Ltd, Melbourne, VIC, Australia
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
| | - Philippa J. Karoly
- Seer Medical Pty Ltd, Melbourne, VIC, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Ewan S. Nurse
- Seer Medical Pty Ltd, Melbourne, VIC, Australia
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
| | | | - David B. Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
| | | | - John M. Heasman
- Epi-Minder Pty. Ltd., Melbourne, VIC, Australia
- Cochlear Limited, Sydney, NSW, Australia
| | | | - Alan Lai
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Wendyl D'Souza
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Udaya Seneviratne
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Neuroscience, Monash Medical Centre, Melbourne, VIC, Australia
- Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia
| | - Linda Seiderer
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Karen J. McLean
- Epi-Minder Pty. Ltd., Melbourne, VIC, Australia
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Kristian J. Bulluss
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Michael Murphy
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Mark P. Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Mark J. Cook
- Seer Medical Pty Ltd, Melbourne, VIC, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Epi-Minder Pty. Ltd., Melbourne, VIC, Australia
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18
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Stirling RE, Grayden DB, D'Souza W, Cook MJ, Nurse E, Freestone DR, Payne DE, Brinkmann BH, Pal Attia T, Viana PF, Richardson MP, Karoly PJ. Forecasting Seizure Likelihood With Wearable Technology. Front Neurol 2021; 12:704060. [PMID: 34335457 PMCID: PMC8320020 DOI: 10.3389/fneur.2021.704060] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 06/17/2021] [Indexed: 12/11/2022] Open
Abstract
The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated with seizures but need to be feasible and accessible in the long-term. Wearable devices are perfect candidates to develop non-invasive, accessible forecasts but are yet to be investigated in long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for well-established cycles of seizures and epileptic activity, in addition to other wearable signals, to forecast high and low risk seizure periods. This feasibility study tracked participants' (n = 11) heart rates, sleep, and step counts using wearable smartwatches and seizure occurrence using smartphone seizure diaries for at least 6 months (mean = 14.6 months, SD = 3.8 months). Eligible participants had a diagnosis of refractory epilepsy and reported at least 20 seizures (mean = 135, SD = 123) during the recording period. An ensembled machine learning and neural network model estimated seizure risk either daily or hourly, with retraining occurring on a weekly basis as additional data was collected. Performance was evaluated retrospectively against a rate-matched random forecast using the area under the receiver operating curve. A pseudo-prospective evaluation was also conducted on a held-out dataset. Of the 11 participants, seizures were predicted above chance in all (100%) participants using an hourly forecast and in ten (91%) participants using a daily forecast. The average time spent in high risk (prediction time) before a seizure occurred was 37 min in the hourly forecast and 3 days in the daily forecast. Cyclic features added the most predictive value to the forecasts, particularly circadian and multiday heart rate cycles. Wearable devices can be used to produce patient-specific seizure forecasts, particularly when biomarkers of seizure and epileptic activity cycles are utilized.
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Affiliation(s)
- Rachel E. Stirling
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - David B. Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Wendyl D'Souza
- Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia
| | - Mark J. Cook
- Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Ewan Nurse
- Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia
- Seer Medical, Melbourne, VIC, Australia
| | | | - Daniel E. Payne
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Tal Pal Attia
- Bioelectronics Neurophysiology and Engineering Lab, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Pedro F. Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Mark P. Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Philippa J. Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
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19
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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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