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Lopes F, Pinto MF, Dourado A, Schulze-Bonhage A, Dümpelmann M, Teixeira C. Addressing data limitations in seizure prediction through transfer learning. Sci Rep 2024; 14:14169. [PMID: 38898066 PMCID: PMC11187122 DOI: 10.1038/s41598-024-64802-1] [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: 02/10/2024] [Accepted: 06/13/2024] [Indexed: 06/21/2024] Open
Abstract
According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning the number of events that may be used to optimise seizure prediction approaches is limited. To overcome such constraint, we analysed the possibility of using data from patients from an external database to improve patient-specific seizure prediction models. We present seizure prediction models trained using a transfer learning procedure. We trained a deep convolutional autoencoder using electroencephalogram data from 41 patients collected from the EPILEPSIAE database. Then, a bidirectional long short-term memory and a classifier layers were added on the top of the encoder part and were optimised for 24 patients from the Universitätsklinikum Freiburg individually. The encoder was used as a feature extraction module. Therefore, its weights were not changed during the patient-specific training. Experimental results showed that seizure prediction models optimised using pretrained weights present about four times fewer false alarms while maintaining the same ability to predict seizures and achieved more 13% validated patients. Therefore, results evidenced that the optimisation using transfer learning was more stable and faster, saving computational resources. In summary, adopting transfer learning for seizure prediction models represents a significant advancement. It addresses the data limitation seen in the seizure prediction field and offers more efficient and stable training, conserving computational resources. Additionally, despite the compact size, transfer learning allows to easily share data knowledge due to fewer ethical restrictions and lower storage requirements. The convolutional autoencoder developed in this study will be shared with the scientific community, promoting further research.
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Affiliation(s)
- Fábio Lopes
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal.
- Department Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Mauro F Pinto
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal
| | - António Dourado
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal
| | - Andreas Schulze-Bonhage
- Department Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Department Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
| | - César Teixeira
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal
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Ahrens E, Jennum P, Duun-Henriksen J, Borregaard HWS, Nielsen SS, Taptiklis N, Cormack F, Djurhuus BD, Homøe P, Kjær TW, Hemmsen MC. The Ultra-Long-Term Sleep study: Design, rationale, data stability and user perspective. J Sleep Res 2024:e14197. [PMID: 38572813 DOI: 10.1111/jsr.14197] [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: 08/21/2023] [Revised: 02/26/2024] [Accepted: 03/04/2024] [Indexed: 04/05/2024]
Abstract
Sleep deprivation and poor sleep quality are significant societal challenges that negatively impact individuals' health. The interaction between subjective sleep quality, objective sleep measures, physical and cognitive performance, and their day-to-day variations remains poorly understood. Our year-long study of 20 healthy individuals, using subcutaneous electroencephalography, aimed to elucidate these interactions, assessing data stability and participant satisfaction, usability, well-being and adherence. In the study, 25 participants were fitted with a minimally invasive subcutaneous electroencephalography lead, with 20 completing the year of subcutaneous electroencephalography recording. Signal stability was measured using covariance of variation. Participant satisfaction, usability and well-being were measured with questionnaires: Perceived Ease of Use questionnaire, System Usability Scale, Headache questionnaire, Major Depression Inventory, World Health Organization 5-item Well-Being Index, and interviews. The subcutaneous electroencephalography signals remained stable for the entire year, with an average participant adherence rate of 91%. Participants rated their satisfaction with the subcutaneous electroencephalography device as easy to use with minimal or no discomfort. The System Usability Scale score was high at 86.3 ± 10.1, and interviews highlighted that participants understood how to use the subcutaneous electroencephalography device and described a period of acclimatization to sleeping with the device. This study provides compelling evidence for the feasibility of longitudinal sleep monitoring during everyday life utilizing subcutaneous electroencephalography in healthy subjects, showcasing excellent signal stability, adherence and user experience. The amassed subcutaneous electroencephalography data constitutes the largest dataset of its kind, and is poised to significantly advance our understanding of day-to-day variations in normal sleep and provide key insights into subjective and objective sleep quality.
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Affiliation(s)
- Esben Ahrens
- T&W Engineering A/S, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Poul Jennum
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Glostrup, Denmark
| | | | | | | | | | - Francesca Cormack
- Cambridge Cognition Ltd, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Bjarki Ditlev Djurhuus
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Otorhinolaryngology and Maxillofacial Surgery, Zealand University Hospital, Køge, Denmark
| | - Preben Homøe
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Otorhinolaryngology and Maxillofacial Surgery, Zealand University Hospital, Køge, Denmark
| | - Troels W Kjær
- T&W Engineering A/S, Denmark
- UNEEG medical A/S, Lillerød, Lillerød, Denmark
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Lehnen J, Venkatesh P, Yao Z, Aziz A, Nguyen PVP, Harvey J, Alick-Lindstrom S, Doyle A, Podkorytova I, Perven G, Hays R, Zepeda R, Das RR, Ding K. Real-Time Seizure Detection Using Behind-the-Ear Wearable System. J Clin Neurophysiol 2024:00004691-990000000-00128. [PMID: 38376923 DOI: 10.1097/wnp.0000000000001076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024] Open
Abstract
INTRODUCTION This study examines the usability and comfort of a behind-the-ear seizure detection device called brain seizure detection (BrainSD) that captures ictal electroencephalogram (EEG) data using four scalp electrodes. METHODS This is a feasibility study. Thirty-two patients admitted to a level 4 Epilepsy Monitoring Unit were enrolled. The subjects wore BrainSD and the standard 21-channel video-EEG simultaneously. Epileptologists analyzed the EEG signals collected by BrainSD and validated it using video-EEG data to confirm its accuracy. A poststudy survey was completed by each participant to evaluate the comfort and usability of the device. In addition, a focus group of UT Southwestern epileptologists was held to discuss the features they would like to see in a home EEG-based seizure detection device such as BrainSD. RESULTS In total, BrainSD captured 11 of the 14 seizures that occurred while the device was being worn. All 11 seizures captured on BrainSD had focal onset, with three becoming bilateral tonic-clonic and one seizure being of subclinical status. The device was worn for an average of 41 hours. The poststudy survey showed that most users found the device comfortable, easy-to-use, and stated they would be interested in using BrainSD. Epileptologists in the focus group expressed a similar interest in BrainSD. CONCLUSIONS Brain seizure detection is able to detect EEG signals using four behind-the-ear electrodes. Its comfort, ease-of-use, and ability to detect numerous types of seizures make BrainSD an acceptable at-home EEG detection device from both the patient and provider perspective.
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Affiliation(s)
- Jamie Lehnen
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Pooja Venkatesh
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Zhuoran Yao
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Abdul Aziz
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX; and
| | - Phuc V P Nguyen
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX; and
- College of Information and Computer Science, University of Massachusets Amherst, Amherst, MA
| | - Jay Harvey
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Sasha Alick-Lindstrom
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Alex Doyle
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Irina Podkorytova
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Ghazala Perven
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Ryan Hays
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Rodrigo Zepeda
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Rohit R Das
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Kan Ding
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
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Ulate-Campos A, Loddenkemper T. Review on the current long-term, limited lead electroencephalograms. Epilepsy Behav 2024; 150:109557. [PMID: 38070411 DOI: 10.1016/j.yebeh.2023.109557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/14/2023] [Accepted: 11/17/2023] [Indexed: 01/14/2024]
Abstract
In the last century, 10-20 lead EEG recordings became the gold standard of surface EEG recordings, and the 10-20 system provided comparability between international studies. With the emergence of advanced EEG sensors, that may be able to record and process signals in much more compact units, this additional sensor technology now opens up opportunities to revisit current ambulatory EEG recording practices and specific patient populations, and even electrodes that are embedded into the head surface. Here, we aim to provide an overview of current limited sensor long-term EEG systems. We performed a literature review using Pubmed as a database and included the relevant articles. The review identified several systems for recording long-term ambulatory EEGs. In general, EEGs recorded with these modalities can be acquired in ambulatory and home settings, achieve good sensitivity with low false detection rates, are used for automatic seizure detection as well as seizure forecasting, and are well tolerated by patients, but each of them has advantages and disadvantages. Subcutaneous, subgaleal, and subscalp electrodes are minimally invasive and provide stable signals that can record ultra--long-term EEG and are in general less noisy than scalp EEG, but they have limited spatial coverage and require anesthesia, a surgical procedure and a trained surgeon to be placed. Behind and in the ear electrodes are discrete, unobtrusive with a good sensitivity mainly for temporal seizures but might miss extratemporal seizures, recordings could be obscured by muscle artifacts and bilateral ictal patterns might be difficult to register. Finally, recording systems using electrodes in a headband can be easily and quickly placed by the patient or caregiver, but have less spatial coverage and are more prone to movement because electrodes are not attached. Overall, limited EEG recording systems offer a promising opportunity to potentially record targeted EEG with focused indications for prolonged periods, but further validation work is needed.
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Castillo Rodriguez MDLA, Brandt A, Schulze-Bonhage A. Differentiation of subclinical and clinical electrographic events in long-term electroencephalographic recordings. Epilepsia 2023; 64 Suppl 4:S47-S58. [PMID: 36008142 DOI: 10.1111/epi.17401] [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: 02/18/2022] [Revised: 08/22/2022] [Accepted: 08/22/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE With the advent of ultra-long-term recordings for monitoring of epilepsies, the interpretation of results of isolated electroencephalographic (EEG) recordings covering only selected brain regions attracts considerable interest. In this context, the question arises of whether detected ictal EEG patterns correspond to clinically manifest seizures or rather to purely electrographic events, that is, subclinical events. METHODS EEG patterns from 268 clinical seizures and 252 subclinical electrographic events from 50 patients undergoing video-EEG monitoring were analyzed. Features extracted included predominant frequency band, duration, association with rhythmic muscle artifacts, spatial extent, and propagation patterns. Classification using logistic regression was performed based on data from the whole dataset of 10-20 system EEG recordings and from a subset of two temporal electrode contacts. RESULTS Correct separation of clinically manifest and purely electrographic events based on 10-20 system EEG recordings was possible in up to 83.8% of events, depending on the combination of features included. Correct classification based on two-channel recordings was only slightly inferior, achieving 78.6% accuracy; 74.4% and 74.8%, respectively, of events could be correctly classified when using duration alone with either electrode set, although classification accuracies were lower for some subgroups of seizures, particularly focal aware seizures and epileptic arousals. SIGNIFICANCE A correct classification of subclinical versus clinical EEG events was possible in 74%-83% of events based on full EEG recordings, and in 74%-78% when considering only a subset of two electrodes, matching the channel number available from new implantable diagnostic devices. This is a promising outcome, suggesting that ultra-long-term low-channel EEG recordings may provide sufficient information for objective seizure diaries. Intraindividual optimization using high numbers of ictal events may further improve separation, provided that supervised learning with external validation is feasible.
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Affiliation(s)
| | - Armin Brandt
- Epilepsy Center, University Medical Center Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, University Medical Center Freiburg, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine, Freiburg, Germany
- European Reference Network EpiCare, Freiburg, Germany
- NeuroModulBasic, Freiburg, Germany
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Baud MO, Proix T, Gregg NM, Brinkmann BH, Nurse ES, Cook MJ, Karoly PJ. Seizure forecasting: Bifurcations in the long and winding road. Epilepsia 2023; 64 Suppl 4:S78-S98. [PMID: 35604546 PMCID: PMC9681938 DOI: 10.1111/epi.17311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/28/2022]
Abstract
To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.
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Affiliation(s)
- Maxime O Baud
- Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio- and Neuro-Engineering, Geneva, Switzerland
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan S Nurse
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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Viana PF, Attia TP, Nasseri M, Duun-Henriksen J, Biondi A, Winston JS, Martins IP, Nurse ES, Dümpelmann M, Schulze-Bonhage A, Freestone DR, Kjaer TW, Richardson MP, Brinkmann BH. Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models. Epilepsia 2023; 64 Suppl 4:S124-S133. [PMID: 35395101 PMCID: PMC9547037 DOI: 10.1111/epi.17252] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/05/2022] [Accepted: 04/05/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. METHODS We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. RESULTS Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. SIGNIFICANCE This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.
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Affiliation(s)
- Pedro F. Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Tal Pal Attia
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Mona Nasseri
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- School of Engineering, University of North Florida, Jacksonville, Florida, USA
| | | | - Andrea Biondi
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
| | - Joel S. Winston
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
| | | | - Ewan S. Nurse
- Seer Medical, Melbourne, Victoria, Australia
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Matthias Dümpelmann
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Dean R. Freestone
- Seer Medical, Melbourne, Victoria, Australia
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Troels W. Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Mark P. Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
- National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust, London, UK
| | - Benjamin H. Brinkmann
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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Attia TP, Viana PF, Nasseri M, Duun-Henriksen J, Biondi A, Winston JS, Martins IP, Nurse ES, Dümpelmann M, Worrell GA, Schulze-Bonhage A, Freestone DR, Kjaer TW, Brinkmann BH, Richardson MP. Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models. Epilepsia 2023; 64 Suppl 4:S114-S123. [PMID: 35441703 PMCID: PMC9582039 DOI: 10.1111/epi.17265] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/17/2022] [Accepted: 04/18/2022] [Indexed: 11/29/2022]
Abstract
This study describes a generalized cross-patient seizure-forecasting approach using recurrent neural networks with ultra-long-term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short-term memory (LSTM) deep-learning classifiers. Electrographic seizures were identified by a board-certified epileptologist. One-minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise-added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo-prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave-one-patient-out cross-validation method, and the area under the receiver-operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave-one-signal-out method with repeated training and testing for each classifier. Cross-patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution (p < .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross-patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two-channel ultra-long-term sqEEG.
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Affiliation(s)
- Tal Pal Attia
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
| | - Pedro F. Viana
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
- Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Mona Nasseri
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
- School of Engineering, University of North Florida, Jacksonville, Florida, USA
| | | | - Andrea Biondi
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Joel S. Winston
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Isabel P. Martins
- Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Ewan S. Nurse
- Seer Medical Pty Ltd., Melbourne, Victoria, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center, Faculty of Medicine, University Medical Center, University of Freiburg, Freiburg, Germany
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center, Faculty of Medicine, University Medical Center, University of Freiburg, Freiburg, Germany
| | - Dean R. Freestone
- Seer Medical Pty Ltd., Melbourne, Victoria, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Troels W. Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P. Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK
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Schulze‐Bonhage A, Richardson MP, Brandt A, Zabler N, Dümpelmann M, San Antonio‐Arce V. Cyclical underreporting of seizures in patient-based seizure documentation. Ann Clin Transl Neurol 2023; 10:1863-1872. [PMID: 37608738 PMCID: PMC10578895 DOI: 10.1002/acn3.51880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 07/18/2023] [Indexed: 08/24/2023] Open
Abstract
OBJECTIVE Circadian and multidien cycles of seizure occurrence are increasingly discussed as to their biological underpinnings and in the context of seizure forecasting. This study analyzes if patient reported seizures provide valid data on such cyclical occurrence. METHODS We retrospectively studied if circadian cycles derived from patient-based reporting reflect the objective seizure documentation in 2003 patients undergoing in-patient video-EEG monitoring. RESULTS Only 24.1% of more than 29000 seizures documented were accompanied by patient notifications. There was cyclical underreporting of seizures with a maximum during nighttime, leading to significant deviations in the circadian distribution of seizures. Significant cyclical deviations were found for focal epilepsies originating from both, frontal and temporal lobes, and for different seizure types (in particular, focal unaware and focal to bilateral tonic-clonic seizures). INTERPRETATION Patient seizure diaries may reflect a cyclical reporting bias rather than the true circadian seizure distributions. Cyclical underreporting of seizures derived from patient-based reports alone may lead to suboptimal treatment schemes, to an underestimation of seizure-associated risks, and may pose problems for valid seizure forecasting. This finding strongly supports the use of objective measures to monitor cyclical distributions of seizures and for studies and treatment decisions based thereon.
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Affiliation(s)
- Andreas Schulze‐Bonhage
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
- European Reference Network EpiCARE
| | - Mark P. Richardson
- Division of NeuroscienceInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - Armin Brandt
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Nicolas Zabler
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Matthias Dümpelmann
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Victoria San Antonio‐Arce
- Epilepsy CenterUniversity Medical Center, University of FreiburgFreiburgGermany
- European Reference Network EpiCARE
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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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11
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Milne-Ives M, Duun-Henriksen J, Blaabjerg L, Mclean B, Shankar R, Meinert E. At home EEG monitoring technologies for people with epilepsy and intellectual disabilities: A scoping review. Seizure 2023; 110:11-20. [PMID: 37295277 DOI: 10.1016/j.seizure.2023.05.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/06/2023] [Accepted: 05/07/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Conducting electroencephalography in people with intellectual disabilities (PwID) can be challenging, but the high proportion of PwID who experience seizures make it an essential part of their care. To reduce hospital-based monitoring, interventions are being developed to enable high-quality EEG data to be collected at home. This scoping review aims to summarise the current state of remote EEG monitoring research, potential benefits and limitations of the interventions, and inclusion of PwID in this research. METHODS The review was structured using the PRISMA extension for Scoping Reviews and the PICOS framework. Studies that evaluated a remote EEG monitoring intervention in adults with epilepsy were retrieved from the PubMed, MEDLINE, Embase, CINAHL, Web of Science, and ClinicalTrials.gov databases. A descriptive analysis provided an overview of the study and intervention characteristics, key results, strengths, and limitations. RESULTS 34,127 studies were retrieved and 23 were included. Five types of remote EEG monitoring were identified. Common benefits included producing useful results of comparable quality to inpatient monitoring and patient experience. A common limitation was the challenge of capturing all seizures with a small number of localised electrodes. No randomised controlled trials were included, few studies reported sensitivity and specificity, and only three considered PwID. CONCLUSIONS Overall, the studies demonstrated the feasibility of remote EEG interventions for out-of-hospital monitoring and their potential to improve data collection and quality of care for patients. Further research is needed on the effectiveness, benefits, and limitations of remote EEG monitoring compared to in-patient monitoring, especially for PwID.
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Affiliation(s)
- Madison Milne-Ives
- Centre for Health Technology, University of Plymouth, Plymouth, PL4 6DT, UK
| | | | | | - Brendan Mclean
- Royal Cornwall Hospitals NHS Trust, Treliske, Truro, Cornwall, TR1 3LJ, UK; Peninsula Medical School, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK; Cornwall Partnership NHS Foundation Trust, Carew House, Beacon Technology Park, Dunmere Rd, Bodmin, PL31 2QN, UK
| | - Rohit Shankar
- Peninsula Medical School, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK; Cornwall Partnership NHS Foundation Trust, Carew House, Beacon Technology Park, Dunmere Rd, Bodmin, PL31 2QN, UK
| | - Edward Meinert
- Centre for Health Technology, University of Plymouth, Plymouth, PL4 6DT, UK; Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK; Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, W6 8RP, UK.
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12
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Viana PF, Duun-Henriksen J, Winston JS, Kjaer TW, Skaarup C, Richardson MP. Atlas of ambulatory subcutaneous electroencephalography. Epileptic Disord 2023; 25:584-585. [PMID: 37149497 DOI: 10.1002/epd2.20066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/25/2023] [Accepted: 04/29/2023] [Indexed: 05/08/2023]
Affiliation(s)
- Pedro F Viana
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, UK
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | | | - Joel S Winston
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, UK
| | | | | | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, UK
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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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Lopes F, Leal A, Pinto MF, Dourado A, Schulze-Bonhage A, Dümpelmann M, Teixeira C. Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models. Sci Rep 2023; 13:5918. [PMID: 37041158 PMCID: PMC10090199 DOI: 10.1038/s41598-023-30864-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/02/2023] [Indexed: 04/13/2023] Open
Abstract
The development of seizure prediction models is often based on long-term scalp electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive, and come at a relatively low-cost. However, they suffer from major shortcomings. First, long-term EEG is usually highly contaminated with artefacts. Second, changes in the EEG signal over long intervals, known as concept drift, are often neglected. We evaluate the influence of these problems on deep neural networks using EEG time series and on shallow neural networks using widely-used EEG features. Our patient-specific prediction models were tested in 1577 hours of continuous EEG, containing 91 seizures from 41 patients with temporal lobe epilepsy who were undergoing pre-surgical monitoring. Our results showed that cleaning EEG data, using a previously developed artefact removal method based on deep convolutional neural networks, improved prediction performance. We also found that retraining the models over time reduced false predictions. Furthermore, the results show that although deep neural networks processing EEG time series are less susceptible to false alarms, they may need more data to surpass feature-based methods. These findings highlight the importance of robust data denoising and periodic adaptation of seizure prediction models.
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Affiliation(s)
- Fábio Lopes
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.
- Epilepsy Center, Department Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Adriana Leal
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Mauro F Pinto
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - António Dourado
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Department Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - César Teixeira
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
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15
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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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Löscher W, Worrell GA. Novel subscalp and intracranial devices to wirelessly record and analyze continuous EEG in unsedated, behaving dogs in their natural environments: A new paradigm in canine epilepsy research. Front Vet Sci 2022; 9:1014269. [PMID: 36337210 PMCID: PMC9631025 DOI: 10.3389/fvets.2022.1014269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 09/26/2022] [Indexed: 11/25/2022] Open
Abstract
Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily anti-seizure medications. The optimization of pharmacologic therapy is often limited by inaccurate seizure diaries and medication side effects. Electroencephalography (EEG) has long been a cornerstone of diagnosis and classification in human epilepsy, but because of several technical challenges has played a smaller clinical role in canine epilepsy. The interictal (between seizures) and ictal (seizure) EEG recorded from the epileptic mammalian brain shows characteristic electrophysiologic biomarkers that are very useful for clinical management. A fundamental engineering gap for both humans and canines with epilepsy has been the challenge of obtaining continuous long-term EEG in the patients' natural environment. We are now on the cusp of a revolution where continuous long-term EEG from behaving canines and humans will be available to guide clinicians in the diagnosis and optimal treatment of their patients. Here we review some of the devices that have recently emerged for obtaining long-term EEG in ambulatory subjects living in their natural environments.
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Affiliation(s)
- Wolfgang Löscher
- Department of Pharmacology, Toxicology, and Pharmacy, University of Veterinary Medicine, Hanover, Germany
- Center for Systems Neuroscience, Hanover, Germany
- *Correspondence: Wolfgang Löscher
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
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Esmaeili B, Vieluf S, Dworetzky BA, Reinsberger C. The Potential of Wearable Devices and Mobile Health Applications in the Evaluation and Treatment of Epilepsy. Neurol Clin 2022; 40:729-739. [DOI: 10.1016/j.ncl.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Yang Y, Truong ND, Eshraghian JK, Nikpour A, Kavehei O. Weak self-supervised learning for seizure forecasting: a feasibility study. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220374. [PMID: 35950196 PMCID: PMC9346358 DOI: 10.1098/rsos.220374] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/12/2022] [Indexed: 05/27/2023]
Abstract
This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by using self-supervised learning. Time-series data are inherently autocorrelated. By using a detection model to generate weak labels on the fly, which are concurrently used as targets to train a prediction model on a time-shifted input data stream, this autocorrelation can effectively be harnessed to reduce the burden of manual labelling. This is critical in medical patient monitoring, as it enables the development of personalized forecasting models without demanding the annotation of long sequences of physiological signal recordings. We perform a feasibility study on seizure prediction, which is identified as an ideal test case, as pre-ictal brainwaves are patient-specific, and tailoring models to individual patients is known to improve forecasting performance significantly. Our self-supervised approach is used to train individualized forecasting models for 10 patients, showing an average relative improvement in sensitivity by 14.30% and a reduction in false alarms by 19.61% in early seizure forecasting. This proof-of-concept on the feasibility of using a continuous stream of time-series neurophysiological data paves the way towards a low-power neuromorphic neuromodulation system.
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Affiliation(s)
- Yikai Yang
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Nhan Duy Truong
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Jason K. Eshraghian
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Armin Nikpour
- Faculty of Medicine and Health, Central Clinical School, The University of Sydney, Sydney, New South Wales 2006, Australia
- Comprehensive Epilepsy Service and Department of Neurology, Royal Prince Alfred Hospital, Camperdown, New South Wales 2050, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
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Haneef Z, Yang K, Sheth SA, Aloor FZ, Aazhang B, Krishnan V, Karakas C. Sub-scalp electroencephalography: A next-generation technique to study human neurophysiology. Clin Neurophysiol 2022; 141:77-87. [PMID: 35907381 DOI: 10.1016/j.clinph.2022.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/20/2022] [Accepted: 07/03/2022] [Indexed: 11/29/2022]
Abstract
Sub-scalp electroencephalography (ssEEG) is emerging as a promising technology in ultra-long-term electroencephalography (EEG) recordings. Given the diversity of devices available in this nascent field, uncertainty persists about its utility in epilepsy evaluation. This review critically dissects the many proposed utilities of ssEEG devices including (1) seizure quantification, (2) seizure characterization, (3) seizure lateralization, (4) seizure localization, (5) seizure alarms, (6) seizure forecasting, (7) biomarker discovery, (8) sleep medicine, and (9) responsive stimulation. The different ssEEG devices in development have individual design philosophies with unique strengths and limitations. There are devices offering primarily unilateral recordings (24/7 EEGTM SubQ, NeuroviewTM, Soenia® UltimateEEG™), bilateral recordings (Minder™, Epios™), and even those with responsive stimulation capability (EASEE®). We synthesize the current knowledge of these ssEEG systems. We review the (1) ssEEG devices, (2) use case scenarios, (3) challenges and (4) suggest a roadmap for ideal ssEEG designs.
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Affiliation(s)
- Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Kaiyuan Yang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Fuad Z Aloor
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Vaishnav Krishnan
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Cemal Karakas
- Division of Pediatric Neurology, Department of Neurology, University of Louisville, Louisville, KY 40202, USA; Norton Children's Neuroscience Institute, Louisville, KY 40241, USA
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Löscher W. Dogs as a Natural Animal Model of Epilepsy. Front Vet Sci 2022; 9:928009. [PMID: 35812852 PMCID: PMC9257283 DOI: 10.3389/fvets.2022.928009] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 05/18/2022] [Indexed: 12/13/2022] Open
Abstract
Epilepsy is a common neurological disease in both humans and domestic dogs, making dogs an ideal translational model of epilepsy. In both species, epilepsy is a complex brain disease characterized by an enduring predisposition to generate spontaneous recurrent epileptic seizures. Furthermore, as in humans, status epilepticus is one of the more common neurological emergencies in dogs with epilepsy. In both species, epilepsy is not a single disease but a group of disorders characterized by a broad array of clinical signs, age of onset, and underlying causes. Brain imaging suggests that the limbic system, including the hippocampus and cingulate gyrus, is often affected in canine epilepsy, which could explain the high incidence of comorbid behavioral problems such as anxiety and cognitive alterations. Resistance to antiseizure medications is a significant problem in both canine and human epilepsy, so dogs can be used to study mechanisms of drug resistance and develop novel therapeutic strategies to benefit both species. Importantly, dogs are large enough to accommodate intracranial EEG and responsive neurostimulation devices designed for humans. Studies in epileptic dogs with such devices have reported ictal and interictal events that are remarkably similar to those occurring in human epilepsy. Continuous (24/7) EEG recordings in a select group of epileptic dogs for >1 year have provided a rich dataset of unprecedented length for studying seizure periodicities and developing new methods for seizure forecasting. The data presented in this review substantiate that canine epilepsy is an excellent translational model for several facets of epilepsy research. Furthermore, several techniques of inducing seizures in laboratory dogs are discussed as related to therapeutic advances. Importantly, the development of vagus nerve stimulation as a novel therapy for drug-resistant epilepsy in people was based on a series of studies in dogs with induced seizures. Dogs with naturally occurring or induced seizures provide excellent large-animal models to bridge the translational gap between rodents and humans in the development of novel therapies. Furthermore, because the dog is not only a preclinical species for human medicine but also a potential patient and pet, research on this species serves both veterinary and human medicine.
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Affiliation(s)
- Wolfgang Löscher
- Department of Pharmacology, Toxicology and Pharmacy, University of Veterinary Medicine, Hannover, Germany
- Center for Systems Neuroscience, Hannover, Germany
- *Correspondence: Wolfgang Löscher
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21
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Biondi A, Santoro V, Viana PF, Laiou P, Pal DK, Bruno E, Richardson MP. Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review. Epilepsia 2022; 63:1041-1063. [PMID: 35271736 PMCID: PMC9311406 DOI: 10.1111/epi.17220] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/07/2022] [Accepted: 03/07/2022] [Indexed: 11/30/2022]
Abstract
In the last two decades new noninvasive mobile electroencephalography (EEG) solutions have been developed to overcome limitations of conventional clinical EEG and to improve monitoring of patients with long-term conditions. Despite the availability of mobile innovations, their adoption is still very limited. The aim of this study is to review the current state-of-the-art and highlight the main advantages of adopting noninvasive mobile EEG solutions in clinical trials and research studies of people with epilepsy or suspected seizures. Device characteristics are described, and their evaluation is presented. Two authors independently performed a literature review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO and https://clinicaltrials.gov/). Twenty-three full-text, six conference abstracts, and eight webpages were included, where a total of 14 noninvasive mobile solutions were identified. Published studies demonstrated at different levels how EEG recorded via mobile EEG can be used for visual detection of EEG abnormalities and for the application of automatic-detection algorithms with acceptable specificity and sensitivity. When the quality of the signal was compared with scalp EEG, many similarities were found in the background activities and power spectrum. Several studies indicated that the experience of patients and health care providers using mobile EEG was positive in different settings. Ongoing trials are focused mostly on improving seizure-detection accuracy and also on testing and assessing feasibility and acceptability of noninvasive devices in the hospital and at home. This review supports the potential clinical value of noninvasive mobile EEG systems and their advantages in terms of time, technical support, cost, usability, and reliability when applied to seizure detection and management. On the other hand, the limitations of the studies confirmed that future research is needed to provide more evidence regarding feasibility and acceptability in different settings, as well as the data quality and detection accuracy of new noninvasive mobile EEG solutions.
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Affiliation(s)
- Andrea Biondi
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Viviana Santoro
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Pedro F. Viana
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK,Faculty of MedicineUniversity of LisbonLisbonPortugal
| | - Petroula Laiou
- Department of Biostatistics and Health InformaticsInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Deb K. Pal
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Elisa Bruno
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Mark P. Richardson
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
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22
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Pathmanathan J, Kjaer TW, Cole AJ, Delanty N, Surges R, Duun-Henriksen J. Expert Perspective: Who May Benefit Most From the New Ultra Long-Term Subcutaneous EEG Monitoring? Front Neurol 2022; 12:817733. [PMID: 35126304 PMCID: PMC8810530 DOI: 10.3389/fneur.2021.817733] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 12/23/2021] [Indexed: 12/21/2022] Open
Abstract
Today's modalities for short-term monitoring of EEG are primarily meant for supporting clinical diagnosis of epilepsy or classifying seizures and interictal epileptiform discharges while long-term EEG adds the value of differential diagnosis investigation or pre-surgical evaluation. However, longitudinal epilepsy care relies on patient diaries, which is known to be unreliable for most patients and especially those with focal impaired awareness or nocturnal seizures. The subcutaneous ultra long-term EEG (ULT-EEG) systems alleviate those issue by enabling objective, continuous EEG monitoring for days, weeks, months, or years. Albeit a great advance in continuous EEG over extended periods, it comes with the caveat of limited spatial resolution of two channels. Therefore, the new subcutaneous EEG modality may be especially suited for a selected group of patients. We convened a panel of experienced epileptologists to consider the utility of a subcutaneous, two-channel ULT-EEG device with the goal of developing a consensus-based expert recommendation on selecting the optimal patient types for this investigative technique. The ideal patients to select for this type of monitoring would have focal impaired awareness seizures without predominant motor features and seizures with medium to high voltage patterns. As this technology matures and we learn more about its limitations and benefits we might find a wider array of use case scenarios as it is believed that the benefits for many patients are most likely to outweigh the risks and cost.
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Affiliation(s)
- Jay Pathmanathan
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Troels W. Kjaer
- Department of Neurology, Center of Neurophysiology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Andrew J. Cole
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Norman Delanty
- Department of Neurology, Beaumont Hospital, Dublin, Ireland
- FutureNeuro Research Centre, Dublin, Ireland
- School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Rainer Surges
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
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23
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Kjaer TW, Remvig LS, Helge AW, Duun-Henriksen J. The Individual Ictal Fingerprint: Combining Movement Measures With Ultra Long-Term Subcutaneous EEG in People With Epilepsy. Front Neurol 2022; 12:718329. [PMID: 35002910 PMCID: PMC8733463 DOI: 10.3389/fneur.2021.718329] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Epileptic seizures are caused by abnormal brain wave hypersynchronization leading to a range of signs and symptoms. Tools for detecting seizures in everyday life typically focus on cardiac rhythm, electrodermal activity, or movement (EMG, accelerometry); however, these modalities are not very effective for non-motor seizures. Ultra long-term subcutaneous EEG-devices can detect the electrographic changes that do not depend on clinical changes. Nonetheless, this also means that it is not possible to assess whether a seizure is clinical or subclinical based on an EEG signal alone. Therefore, we combine EEG and movement-related modalities in this work. We focus on whether it is possible to define an individual “multimodal ictal fingerprint” which can be exploited in different epilepsy management purposes. Methods: This study used ultra long-term data from an outpatient monitoring trial of persons with temporal lobe epilepsy obtained with a subcutaneous EEG recording system. Subcutaneous EEG, an EMG estimate and chest-mounted accelerometry were extracted from four persons showing more than 10 well-defined electrographic seizures each. Numerous features were computed from all three modalities. Based on these, the Gini impurity measure of a Random Forest classifier was used to select the most discriminative features for the ictal fingerprint. A total of 74 electrographic seizures were analyzed. Results: The optimal individual ictal fingerprints included features extracted from all three tested modalities: an acceleration component; the power of the estimated EMG activity; and the relative power in the delta (0.5–4 Hz), low theta (4–6 Hz), and high theta (6–8 Hz) bands of the subcutaneous EEG. Multimodal ictal fingerprints were established for all persons, clustering seizures within persons, while separating seizures across persons. Conclusion: The existence of multimodal ictal fingerprints illustrates the benefits of combining multiple modalities such as EEG, EMG, and accelerometry in future epilepsy management. Multimodal ictal fingerprints could be used by doctors to get a better understanding of the individual seizure semiology of people with epilepsy. Furthermore, the multimodal ictal fingerprint gives a better understanding of how seizures manifest simultaneously in different modalities. A knowledge that could be used to improve seizure acknowledgment when reviewing EEG without video.
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Affiliation(s)
- Troels W Kjaer
- Center of Neurophysiology, Department of Neurology, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Line S Remvig
- Epilepsy Science, UNEEG medical A/S, Alleroed, Denmark
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24
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Boada CM, Grossman SN, Grzeskowiak CL, Dumanis S, French JA. Proceedings of the 2020 Epilepsy Foundation Pipeline Conference: Emerging Drugs and Devices. Epilepsy Behav 2021; 125:108364. [PMID: 34731723 DOI: 10.1016/j.yebeh.2021.108364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 09/25/2021] [Indexed: 11/17/2022]
Abstract
From August 27-28, 2020 the Epilepsy Foundation hosted the Pipeline Conference, exploring emerging issues related to antiepileptic drug and device development. The conference featured epilepsy therapeutic companies and academic laboratories developing drugs for focal epilepsies, innovations for rare and ultra-rare diseases, and devices both in clinical trials and approved for use. In this paper, we outline the virtual presentations by the authors, including novel data from their development pipeline.
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Affiliation(s)
- Christina M Boada
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
| | - Scott N Grossman
- Department of Neurology, New York University Langone Health, New York, NY, USA
| | | | | | - Jacqueline A French
- Department of Neurology, New York University Langone Health, New York, NY, USA
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25
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Nielsen JM, Rades D, Kjaer TW. Wearable electroencephalography for ultra-long-term seizure monitoring: a systematic review and future prospects. Expert Rev Med Devices 2021; 18:57-67. [PMID: 34836477 DOI: 10.1080/17434440.2021.2012152] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION : Wearable electroencephalography (EEG) for objective seizure counting might transform the clinical management of epilepsy. Non-EEG modalities have been validated for the detection of convulsive seizures, but there is still an unmet need for the detection of non-convulsive seizures. AREAS COVERED : The main objective of this systematic review was to explore the current status on wearable surface- and subcutaneous EEG for long-term seizure monitoring in epilepsy. We included 17 studies and evaluated the progress on the field, including device specifications, intended populations, and main results on the published studies including diagnostic accuracy measures. Furthermore, we examine the hurdles for widespread clinical implementation. This systematic review and expert opinion both consults the PRISMA guidelines and reflects on the future perspectives of this emerging field. EXPERT OPINION : Wearable EEG for long-term seizure monitoring is an emerging field, with plenty of proposed devices and proof-of-concept clinical validation studies. The possible implications of these devices are immense including objective seizure counting and possibly forecasting. However, the true clinical value of the devices, including effects on patient important outcomes and clinical decision making is yet to be unveiled and large-scale clinical validation trials are called for.
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Affiliation(s)
- Jonas Munch Nielsen
- Department of Neurology, Zealand University Hospital, Region Sjælland. Vestermarksvej 11, 4000 Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
| | - Dirk Rades
- Department of Radiation Oncology, University of Lübeck, Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Troels Wesenberg Kjaer
- Department of Neurology, Zealand University Hospital, Region Sjælland. Vestermarksvej 11, 4000 Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
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26
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Schulze-Bonhage A. Seizure prediction: Time for new, multimodal and ultra-long-term approaches. Clin Neurophysiol 2021; 133:152-153. [PMID: 34802924 DOI: 10.1016/j.clinph.2021.10.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Andreas Schulze-Bonhage
- Epilepsy Center, University Medical Center, Faculty of Medicine, University of Freiburg, Germany; European Reference Network EpiCare, Europe; Bernstein Center Freiburg, University of Freiburg, Germany; NeuroModule Basic, University Medical Center, Faculty of Medicine, University of Freiburg, Germany.
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27
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Rao VR. Chronic electroencephalography in epilepsy with a responsive neurostimulation device: current status and future prospects. Expert Rev Med Devices 2021; 18:1093-1105. [PMID: 34696676 DOI: 10.1080/17434440.2021.1994388] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Implanted neurostimulation devices are gaining traction as therapeutic options for people with certain forms of drug-resistant focal epilepsy. Some of these devices enable chronic electroencephalography (cEEG), which offers views of the dynamics of brain activity in epilepsy over unprecedented time horizons. AREAS COVERED This review focuses on clinical insights and basic neuroscience discoveries enabled by analyses of cEEG from an exemplar device, the NeuroPace RNS® System. Applications of RNS cEEG covered here include counting and lateralizing seizures, quantifying medication response, characterizing spells, forecasting seizures, and exploring mechanisms of cognition. Limitations of the RNS System are discussed in the context of next-generation devices in development. EXPERT OPINION The wide temporal lens of cEEG helps capture the dynamism of epilepsy, revealing phenomena that cannot be appreciated with short duration recordings. The RNS System is a vanguard device whose diagnostic utility rivals its therapeutic benefits, but emerging minimally invasive devices, including those with subscalp recording electrodes, promise to be more applicable within a broad population of people with epilepsy. Epileptology is on the precipice of a paradigm shift in which cEEG is a standard part of diagnostic evaluations and clinical management is predicated on quantitative observations integrated over long timescales.
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Affiliation(s)
- Vikram R Rao
- Associate Professor of Clinical Neurology, Chief, Epilepsy Division, Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
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28
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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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29
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Qi G, Zhao S, Ceder AA, Guan W, Yan X. Wielding and evaluating the removal composition of common artefacts in EEG signals for driving behaviour analysis. ACCIDENT; ANALYSIS AND PREVENTION 2021; 159:106223. [PMID: 34119819 DOI: 10.1016/j.aap.2021.106223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 05/01/2021] [Accepted: 05/17/2021] [Indexed: 06/12/2023]
Abstract
Noninvasive EEG signals provide neural activity information at high resolution, of which human mental status can be properly detected. However, artefacts always exist in brain oscillatory EEG signals and thus impede the accuracy and reliability of relevant analysis, especially in real-world tasks. Moreover, the use of a mature artefact identification method cannot assure impeccable artefact separation; this leads to a trade-off between removing contaminated information and losing valuable information. This study addresses this problem by investigating a simulator-based driving behaviour analysis using a car-following scenario to correlate the EEG-based mental features with behavioural responses. The study develops an architecture for an artefact composition pool and proposes three integrated prediction models to evaluate the removal compositions of the EEG artefacts. Three errors (mean absolute, root mean square, mean absolute percentage) and R-squared index are considered for measuring the performance of the models. The results show that the best-performing composition outperformed the no-removal and all-removal cases by 11.75% and 4.28% improvements, respectively. Specifically, we investigate different common artefacts including eye blinks, horizontal eye movements, vertical eye movements, generic discontinuities and muscle artefacts. The gained knowledge on artefact removal, EEG spectral features and stimuli-response patterns can be further applied to properly manipulate real-world EEG signals and develop an effective brain-computer interface.
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Affiliation(s)
- Geqi Qi
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, PR China
| | - Shuo Zhao
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, PR China
| | - Avishai Avi Ceder
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, PR China; Faculty of Civil and Environmental Engineering and the Transportation Research Institute, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel
| | - Wei Guan
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, PR China.
| | - Xuedong Yan
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, PR China
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30
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Bruno E, Böttcher S, Viana PF, Amengual-Gual M, Joseph B, Epitashvili N, Dümpelmann M, Glasstetter M, Biondi A, Van Laerhoven K, Loddenkemper T, Richardson MP, Schulze-Bonhage A, Brinkmann BH. Wearable devices for seizure detection: Practical experiences and recommendations from the Wearables for Epilepsy And Research (WEAR) International Study Group. Epilepsia 2021; 62:2307-2321. [PMID: 34420211 DOI: 10.1111/epi.17044] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 07/20/2021] [Accepted: 08/05/2021] [Indexed: 02/06/2023]
Abstract
The Wearables for Epilepsy And Research (WEAR) International Study Group identified a set of methodology standards to guide research on wearable devices for seizure detection. We formed an international consortium of experts from clinical research, engineering, computer science, and data analytics at the beginning of 2020. The study protocols and practical experience acquired during the development of wearable research studies were discussed and analyzed during bi-weekly virtual meetings to highlight commonalities, strengths, and weaknesses, and to formulate recommendations. Seven major essential components of the experimental design were identified, and recommendations were formulated about: (1) description of study aims, (2) policies and agreements, (3) study population, (4) data collection and technical infrastructure, (5) devices, (6) reporting results, and (7) data sharing. Introducing a framework of methodology standards promotes optimal, accurate, and consistent data collection. It also guarantees that studies are generalizable and comparable, and that results can be replicated, validated, and shared.
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Affiliation(s)
- Elisa Bruno
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Sebastian Böttcher
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany.,Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Pedro F Viana
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Faculty of Medicine, University of Lisbon, Lisboa, Portugal
| | - Marta Amengual-Gual
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Boney Joseph
- Department of Neurology and Biomedical Engineering, Mayo Foundation, Rochester, Minnesota, USA
| | - Nino Epitashvili
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Martin Glasstetter
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Andrea Biondi
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Kristof Van Laerhoven
- Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mark P Richardson
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Benjamin H Brinkmann
- Department of Neurology and Biomedical Engineering, Mayo Foundation, Rochester, Minnesota, USA
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31
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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: 41] [Impact Index Per Article: 13.7] [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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32
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Karoly PJ, Freestone DR, Eden D, Stirling RE, Li L, Vianna PF, Maturana MI, D'Souza WJ, Cook MJ, Richardson MP, Brinkmann BH, Nurse ES. Epileptic Seizure Cycles: Six Common Clinical Misconceptions. Front Neurol 2021; 12:720328. [PMID: 34421812 PMCID: PMC8371239 DOI: 10.3389/fneur.2021.720328] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 07/08/2021] [Indexed: 11/19/2022] Open
Affiliation(s)
- Philippa J. Karoly
- Seer Medical, Melbourne, VIC, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | | | | | - Rachel E. Stirling
- Seer Medical, Melbourne, VIC, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Lyra Li
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Pedro F. Vianna
- School of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Matias I. Maturana
- Seer Medical, Melbourne, VIC, Australia
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, VIC, Australia
| | - Wendyl J. D'Souza
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, VIC, Australia
| | - Mark J. Cook
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, VIC, Australia
| | - Mark P. Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Ewan S. Nurse
- Seer Medical, Melbourne, VIC, Australia
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, VIC, Australia
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33
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Pal Attia T, Crepeau D, Kremen V, Nasseri M, Guragain H, Steele SW, Sladky V, Nejedly P, Mivalt F, Herron JA, Stead M, Denison T, Worrell GA, Brinkmann BH. Epilepsy Personal Assistant Device-A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation. Front Neurol 2021; 12:704170. [PMID: 34393981 PMCID: PMC8358117 DOI: 10.3389/fneur.2021.704170] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 06/21/2021] [Indexed: 12/04/2022] Open
Abstract
Epilepsy is one of the most common neurological disorders, and it affects almost 1% of the population worldwide. Many people living with epilepsy continue to have seizures despite anti-epileptic medication therapy, surgical treatments, and neuromodulation therapy. The unpredictability of seizures is one of the most disabling aspects of epilepsy. Furthermore, epilepsy is associated with sleep, cognitive, and psychiatric comorbidities, which significantly impact the quality of life. Seizure predictions could potentially be used to adjust neuromodulation therapy to prevent the onset of a seizure and empower patients to avoid sensitive activities during high-risk periods. Long-term objective data is needed to provide a clearer view of brain electrical activity and an objective measure of the efficacy of therapeutic measures for optimal epilepsy care. While neuromodulation devices offer the potential for acquiring long-term data, available devices provide very little information regarding brain activity and therapy effectiveness. Also, seizure diaries kept by patients or caregivers are subjective and have been shown to be unreliable, in particular for patients with memory-impairing seizures. This paper describes the design, architecture, and development of the Mayo Epilepsy Personal Assistant Device (EPAD). The EPAD has bi-directional connectivity to the implanted investigational Medtronic Summit RC+STM device to implement intracranial EEG and physiological monitoring, processing, and control of the overall system and wearable devices streaming physiological time-series signals. In order to mitigate risk and comply with regulatory requirements, we developed a Quality Management System (QMS) to define the development process of the EPAD system, including Risk Analysis, Verification, Validation, and protocol mitigations. Extensive verification and validation testing were performed on thirteen canines and benchtop systems. The system is now under a first-in-human trial as part of the US FDA Investigational Device Exemption given in 2018 to study modulated responsive and predictive stimulation using the Mayo EPAD system and investigational Medtronic Summit RC+STM in ten patients with non-resectable dominant or bilateral mesial temporal lobe epilepsy. The EPAD system coupled with an implanted device capable of EEG telemetry represents a next-generation solution to optimizing neuromodulation therapy.
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Affiliation(s)
- Tal Pal Attia
- 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
| | - Vaclav Kremen
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czechia
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
| | - Mona Nasseri
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Hari Guragain
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Steven W. Steele
- Division of Engineering, Mayo Clinic, 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
| | - Filip Mivalt
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Jeffrey A. Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Matt Stead
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
| | - Timothy Denison
- Engineering Sciences and Clinical Neurosciences, Oxford University, Oxford, United Kingdom
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
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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: 49] [Impact Index Per Article: 16.3] [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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35
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Viana PF, Remvig LS, Duun-Henriksen J, Glasstetter M, Dümpelmann M, Nurse ES, Martins IP, Schulze-Bonhage A, Freestone DR, Brinkmann BH, Kjaer TW, Richardson MP. Signal quality and power spectrum analysis of remote ultra long-term subcutaneous EEG. Epilepsia 2021; 62:1820-1828. [PMID: 34250608 DOI: 10.1111/epi.16969] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 06/02/2021] [Accepted: 06/03/2021] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Ultra long-term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long-term quality and consistency of the sqEEG signal, which is the objective of this study. METHODS The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG™ SubQ), and recorded from 23 to 230 days (median 42 days), with a median data capture rate of 75% (17.9 hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long-term temporal trends in signal impedance and power spectral features were investigated with subject-specific linear regression models and group-level linear mixed-effects models. RESULTS sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8-13Hz) and nocturnal peaks in the sigma range (12-16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods. SIGNIFICANCE The spectral characteristics of minimally invasive, ultra long-term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain-computer interfaces.
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Affiliation(s)
- Pedro F Viana
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | | | | | - Martin Glasstetter
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Ewan S Nurse
- Seer Medical Inc, Melbourne, Vic, Australia.,Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Vic, Australia
| | | | - Andreas Schulze-Bonhage
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Dean R Freestone
- Seer Medical Inc, Melbourne, Vic, Australia.,Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Vic, Australia
| | - Benjamin H Brinkmann
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Troels W Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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36
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Lehnertz K, Rings T, Bröhl T. Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks. FRONTIERS IN NETWORK PHYSIOLOGY 2021; 1:755016. [PMID: 36925573 PMCID: PMC10013076 DOI: 10.3389/fnetp.2021.755016] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022]
Abstract
Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.
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Affiliation(s)
- Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany.,Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
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