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Rai P, Knight A, Hiillos M, Kertész C, Morales E, Terney D, Larsen SA, Østerkjerhuus T, Peltola J, Beniczky S. Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence. Front Neuroinform 2024; 18:1324981. [PMID: 38558825 PMCID: PMC10978750 DOI: 10.3389/fninf.2024.1324981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Automated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment. Methods In this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0-80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances of five categories of motor epileptic seizures (tonic-clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 s were assessed independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (>10 s) motor seizures (out of 1,114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity (n = 81 subjects with seizures) and false detection rate (FDR) (n = all 230 subjects). Results At optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/h (CI): tonic-clonic- 95.2% (82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7% (73.5, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures- 78% (65.4, 90.4%); 4.81 (4.50, 5.14), and PNES- 97.7% (97.7, 100%); 1.73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/h. Discussion These results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.
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Affiliation(s)
| | - Andrew Knight
- Neuro Event Labs, Tampere, Finland
- Department of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | | | | | - Daniella Terney
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
| | - Sidsel Armand Larsen
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
| | - Tim Østerkjerhuus
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Jukka Peltola
- Department of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Neurology, Tampere University Hospital, Tampere, Finland
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Gupta N, Kasula V, Sanmugananthan P, Panico N, Dubin AH, Sykes DAW, D'Amico RS. SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies. World Neurosurg X 2024; 21:100247. [PMID: 38033718 PMCID: PMC10682285 DOI: 10.1016/j.wnsx.2023.100247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/24/2023] [Indexed: 12/02/2023] Open
Abstract
Background/objective Recent technological advances have allowed for the development of smart wearable devices (SmartWear) which can be used to monitor various aspects of patient healthcare. These devices provide clinicians with continuous biometric data collection for patients in both inpatient and outpatient settings. Although these devices have been widely used in fields such as cardiology and orthopedics, their use in the field of neurosurgery and neurology remains in its infancy. Methods A comprehensive literature search for the current and future applications of SmartWear devices in the above conditions was conducted, focusing on outpatient monitoring. Findings Through the integration of sensors which measure parameters such as physical activity, hemodynamic variables, and electrical conductivity - these devices have been applied to patient populations such as those at risk for stroke, suffering from epilepsy, with neurodegenerative disease, with spinal cord injury and/or recovering from neurosurgical procedures. Further, these devices are being tested in various clinical trials and there is a demonstrated interest in the development of new technologies. Conclusion This review provides an in-depth evaluation of the use of SmartWear in selected neurological diseases and neurosurgical applications. It is clear that these devices have demonstrated efficacy in a variety of neurological and neurosurgical applications, however challenges such as data privacy and management must be addressed.
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Affiliation(s)
- Nithin Gupta
- Campbell University School of Osteopathic Medicine, Lillington, NC, USA
| | - Varun Kasula
- Campbell University School of Osteopathic Medicine, Lillington, NC, USA
| | | | | | - Aimee H. Dubin
- Campbell University School of Osteopathic Medicine, Lillington, NC, USA
| | - David AW. Sykes
- Department of Neurosurgery, Duke University Medical School, Durham, NC, USA
| | - Randy S. D'Amico
- Lenox Hill Hospital, Department of Neurosurgery, New York, NY, USA
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Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [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] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
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Xia Y, Lai W, Li S, Wen Z, Chen L. Differentiation of epilepsy and psychogenic nonepileptic events based on body fluid characteristics. Epilepsia Open 2023; 8:959-968. [PMID: 37329211 PMCID: PMC10472377 DOI: 10.1002/epi4.12775] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 05/24/2023] [Indexed: 06/18/2023] Open
Abstract
OBJECTIVE Differential diagnosis between epileptic seizures and psychogenic nonepileptic events (PNEEs) is a worldwide problem for neurologists. The present study aims to identify important characteristics from body fluid tests and develop diagnostic models based on them. METHODS This is a register-based observational study in patients with a diagnosis of epilepsy or PNEEs at West China Hospital of Sichuan University. Data from body fluid tests between 2009 and 2019 were used as a training set. We constructed models with a random forest approach in eight training subsets divided by sex and categories of tests, including electrolyte, blood cell, metabolism, and urine tests. Then, we collected data prospectively from patients between 2020 and 2022 to validate our models and calculated the relative importance of characteristics in robust models. Selected characteristics were finally analyzed with multiple logistic regression to establish nomograms. RESULTS A total of 388 patients, including 218 with epilepsy and 170 with PNEEs, were studied. The AUROCs of random forest models of electrolyte and urine tests in the validation phase achieved 80.0% and 79.0%, respectively. Carbon dioxide combining power, anion gap, potassium, calcium, and chlorine in electrolyte tests and specific gravity, pH, and conductivity in urine tests were selected for the logistic regression analysis. C (ROC) of the electrolyte and urine diagnostic nomograms achieved 0.79 and 0.85, respectively. SIGNIFICANCE The application of routine indicators of serum and urine may help in the more accurate identification of epileptic and PNEEs.
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Affiliation(s)
- Yilin Xia
- Department of Neurology, West China HospitalSichuan UniversityChengduChina
| | - Wanlin Lai
- Department of Neurology, West China HospitalSichuan UniversityChengduChina
| | - Shihai Li
- College of ChemistrySichuan UniversityChengduChina
| | - Zhining Wen
- College of ChemistrySichuan UniversityChengduChina
| | - Lei Chen
- Department of Neurology, West China HospitalSichuan UniversityChengduChina
- Pazhou LabGuangzhouChina
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Varley D, Sweetman J, Brabyn S, Lagos D, van der Feltz-Cornelis C. The clinical management of functional neurological disorder: A scoping review of the literature. J Psychosom Res 2023; 165:111121. [PMID: 36549074 DOI: 10.1016/j.jpsychores.2022.111121] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 12/13/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To date, there have been no reviews bringing together evidence on the clinical management of functional neurological disorder (FND) and patients', caregivers', and healthcare workers' experiences. This review provides an overview of the literature focused on the clinical management of FND. METHODS Four databases were searched, and a consultation exercise was conducted to retrieve relevant records dated from September 2010 to September 2020. Articles documenting diagnostic methods, treatments or interventions, or the experiences and perspectives of patients and healthcare workers in the clinical management of FND were included. RESULTS In total, 2756 records were retrieved, with 162 included in this review. The diagnostic methods reported predominantly included positive clinical signs, v-EEG and EEG. Psychological treatments and medication were the most reported treatments. Mixed findings of the effectiveness of CBT were found. Haloperidol, physiotherapy and scripted diagnosis were found to be effective in reducing FND symptoms. Several facilitators and barriers for patients accessing treatment for FND were reported. CONCLUSION The literature describing the clinical management for FND has increased considerably in recent times. A wide variety of diagnostic tools and treatments and interventions were found, with more focus being placed on tests that confirm a diagnosis than 'rule-out' tests. The main treatment type found in this review was medication. This review revealed that there is a lack of high-quality evidence and reflects the need for official clinical guidelines for FND, providing healthcare workers and patients the support needed to navigate the process to diagnose and manage FND.
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Affiliation(s)
- Danielle Varley
- Department of Health Sciences, University of York, York YO10 5DD, UK.
| | - Jennifer Sweetman
- Department of Health Sciences, University of York, York YO10 5DD, UK
| | - Sally Brabyn
- Department of Health Sciences, University of York, York YO10 5DD, UK
| | - Dimitris Lagos
- Hull York Medical School, University of York, York YO10 5DD, UK
| | - Christina van der Feltz-Cornelis
- Department of Health Sciences, University of York, York YO10 5DD, UK; Hull York Medical School, University of York, York YO10 5DD, UK; York Biomedical Research Institute, University of York, York YO10 5DD, UK; Institute of Health Informatics, University College London, London NW1 2DA, UK
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Xiong W, Kameneva T, Lambert E, Cook MJ, Richardson MP, Nurse ES. Forecasting psychogenic non-epileptic seizure likelihood from ambulatory EEG and ECG. J Neural Eng 2022; 19. [PMID: 36270501 DOI: 10.1088/1741-2552/ac9c97] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 10/21/2022] [Indexed: 12/24/2022]
Abstract
Objective.Critical slowing features (variance and autocorrelation) of long-term continuous electroencephalography (EEG) and electrocardiography (ECG) data have previously been used to forecast epileptic seizure onset. This study tested the feasibility of forecasting non-epileptic seizures using the same methods. In doing so, we examined if long-term cycles of brain and cardiac activity are present in clinical physiological recordings of psychogenic non-epileptic seizures (PNES).Approach.Retrospectively accessed ambulatory EEG and ECG data from 15 patients with non-epileptic seizures and no background of epilepsy were used for developing the forecasting system. The median period of recordings was 161 h, with a median of 7 non-epileptic seizures per patient. The phases of different cycles (5 min, 1 h, 6 h, 12 h, 24 h) of EEG and RR interval (RRI) critical slowing features were investigated. Forecasters were generated using combinations of the variance and autocorrelation of both EEG and the RRI of the ECG at each of the aforementioned cycle lengths. Optimal forecasters were selected as those with the highest area under the receiver-operator curve (AUC).Main results.It was found that PNES events occurred in the rising phases of EEG feature cycles of 12 and 24 h in duration at a rate significantly above chance. We demonstrated that the proposed forecasters achieved performance significantly better than chance in 8/15 of patients, and the mean AUC of the best forecaster across patients was 0.79.Significance.To our knowledge, this is the first study to retrospectively forecast non-epileptic seizures using both EEG and ECG data. The significance of EEG in the forecasting models suggests that cyclic EEG features of non-epileptic seizures exist. This study opens the potential of seizure forecasting beyond epilepsy, into other disorders of episodic loss of consciousness or dissociation.
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Affiliation(s)
- Wenjuan Xiong
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
| | - Tatiana Kameneva
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia.,Iverson Health Innovation Institute, Swinburne University of Technology, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Elisabeth Lambert
- Iverson Health Innovation Institute, Swinburne University of Technology, Melbourne, Australia.,School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
| | - Mark J Cook
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Ewan S Nurse
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia.,Seer Medical, Melbourne, Australia
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Seizure-related differences in biosignal 24-h modulation patterns. Sci Rep 2022; 12:15070. [PMID: 36064877 PMCID: PMC9445076 DOI: 10.1038/s41598-022-18271-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 08/08/2022] [Indexed: 11/08/2022] Open
Abstract
A seizure likelihood biomarker could improve seizure monitoring and facilitate adjustment of treatments based on seizure risk. Here, we tested differences in patient-specific 24-h-modulation patterns of electrodermal activity (EDA), peripheral body temperature (TEMP), and heart rate (HR) between patients with and without seizures. We enrolled patients who underwent continuous video-EEG monitoring at Boston Children's Hospital to wear a biosensor. We divided patients into two groups: those with no seizures and those with at least one seizure during the recording period. We assessed the 24-h modulation level and amplitude of EDA, TEMP, and HR. We performed machine learning including physiological and clinical variables. Subsequently, we determined classifier performance by cross-validated machine learning. Patients with seizures (n = 49) had lower EDA levels (p = 0.031), EDA amplitudes (p = 0.045), and trended toward lower HR levels (p = 0.060) compared to patients without seizures (n = 68). Averaged cross-validated classification accuracy was 69% (AUC-ROC: 0.75). Our results show the potential to monitor and forecast risk for epileptic seizures based on changes in 24-h patterns in wearable recordings in combination with clinical variables. Such biomarkers might be applicable to inform care, such as treatment or seizure injury risk during specific periods, scheduling diagnostic tests, such as admission to the epilepsy monitoring unit, and potentially other neurological and chronic conditions.
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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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Kok XH, Imtiaz SA, Rodriguez-Villegas E. Assessing the Feasibility of Acoustic Based Seizure Detection. IEEE Trans Biomed Eng 2022; 69:2379-2389. [PMID: 35061585 DOI: 10.1109/tbme.2022.3144634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Long-term monitoring of epilepsy patients outside of hospital settings is impractical due to the complexity and costs associated with electroencephalogram (EEG) systems. Alternative sensing modalities that can acquire, and automatically interpret signals through easy-to-use wearable devices, are needed to help with at-home management of the disease. In this paper, a novel machine learning algorithm is presented for detecting epileptic seizures using acoustic physiological signals acquired from the neck using a wearable device. METHODS Acoustic signals from an existing database, were processed, to extract their Mel-frequency Cepstral Coefficients (MFCCs) which were used to train RUSBoost classifiers to identify ictal and non-ictal acoustic segments. A postprocessing stage was then applied to the segment classification results to identify seizures episodes. RESULTS Tested on 667 hours of acoustic data acquired from 15 patients with at least one seizure, the algorithm achieved a detection sensitivity of 88.1% (95% CI: 79%-97%) from a total of 36 seizures, out of which 24 had no motor manifestations, with a FPR of 0.83/h, and a median detection latency of -42 s. CONCLUSION The results demonstrated for the first time the ability to identify seizures using acoustic internal body signals acquired on the neck. SIGNIFICANCE The results of this paper validate the feasibility of using internal physiological sounds for seizure detection, which could potentially be of use for the development of novel, wearable, very simple to use, long term monitoring, or seizure detection systems; circumventing the practical limitations of EEG monitoring outside hospital settings, or systems based on sensing modalities that work on convulsive seizures only.
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Naganur V, Sivathamboo S, Chen Z, Kusmakar S, Antonic-Baker A, O'Brien TJ, Kwan P. Automated seizure detection with non-invasive wearable devices: A systematic review and meta-analysis. Epilepsia 2022; 63:1930-1941. [PMID: 35545836 PMCID: PMC9545631 DOI: 10.1111/epi.17297] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 05/08/2022] [Accepted: 05/09/2022] [Indexed: 11/29/2022]
Abstract
Objective This study was undertaken to review the reported performance of noninvasive wearable devices in detecting epileptic seizures and psychogenic nonepileptic seizures (PNES). Methods We conducted a systematic review and meta‐analysis of studies reported up to November 15, 2021. We included studies that used video‐electroencephalographic (EEG) monitoring as the gold standard to determine the sensitivity and false alarm rate (FAR) of noninvasive wearables for automated seizure detection. Results Twenty‐eight studies met the criteria for the systematic review, of which 23 were eligible for meta‐analysis. These studies (1269 patients in total, median recording time = 52.9 h per patient) investigated devices for tonic–clonic seizures using wrist‐worn and/or ankle‐worn devices to measure three‐dimensional accelerometry (15 studies), and/or wearable surface devices to measure electromyography (eight studies). The mean sensitivity for detecting tonic–clonic seizures was .91 (95% confidence interval [CI] = .85–.96, I2 = 83.8%); sensitivity was similar between the wrist‐worn (.93) and surface devices (.90). The overall FAR was 2.1/24 h (95% CI = 1.7–2.6, I2 = 99.7%); FAR was higher in wrist‐worn (2.5/24 h) than in wearable surface devices (.96/24 h). Three of the 23 studies also detected PNES; the mean sensitivity and FAR from these studies were 62.9% and .79/24 h, respectively. Four studies detected both focal and tonic–clonic seizures, and one study detected focal seizures only; the sensitivities ranged from 31.1% to 93.1% in these studies. Significance Reported noninvasive wearable devices had high sensitivity but relatively high FARs in detecting tonic–clonic seizures during limited recording time in a video‐EEG setting. Future studies should focus on reducing FAR, detection of other seizure types and PNES, and longer recording in the community.
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Affiliation(s)
- Vaidehi Naganur
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Medicine, The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Shobi Sivathamboo
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Medicine, The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Zhibin Chen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Medicine, The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Chronic Disease and Ageing, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Shitanshu Kusmakar
- Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, Australia
| | - Ana Antonic-Baker
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Medicine, The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Medicine, The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
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Hirashima J, Saito M, Kuriyama T, Akamatsu T, Yokomori M. Detection of Generalized Tonic–Clonic Seizures in Dogs With a Seizure Detection System Established Using Acceleration Data and the Mahalanobis Distance: A Preliminary Study. Front Vet Sci 2022; 9:848604. [PMID: 35573398 PMCID: PMC9097225 DOI: 10.3389/fvets.2022.848604] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/18/2022] [Indexed: 11/13/2022] Open
Abstract
Caregivers of dogs with epilepsy experience severe stress due to unpredictable seizures. Hence, they feel the need for a better management strategy. A seizure detection system (SDS), which can identify seizures and provide notifications to caregivers immediately, is required to address this issue. The current study aimed to establish a wearable automatic SDS using acceleration data and the Mahalanobis distance and to preliminarily investigate its feasibility among dogs. A generalized tonic–clonic seizure (GTCS) was targeted because it is the most common type of seizure and can have serious consequences (i.e., status epilepticus). This study comprised three phases. First, the reference datasets of epileptic and non-epileptic activities were established using acceleration data of GTCSs in 3 dogs and daily activities in 27 dogs. Second, the GTCS-detecting algorithm was created using the reference datasets and was validated using other acceleration data of GTCSs in 4 epileptic dogs and daily activities in 27 dogs. Third, a feasibility test of the SDS prototype was performed in three dogs with epilepsy. The algorithm was effective in identifying all acceleration data of GTCSs as seizures and all acceleration data of daily activities as non-seizure activities. Dogs with epilepsy were monitored with the prototype for 48–72 h, and three GTCSs were identified. The prototype detected all GTCSs accurately. A false positive finding was not obtained unless the accelerometer was displaced. Hence, a method that can detect epileptic seizures, particularly GTCSs, was established. Nevertheless, further large-scale studies must be conducted before the method can be commercialized.
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Kerr WT, Zhang X, Hill CE, Janio EA, Chau AM, Braesch CT, Le JM, Hori JM, Patel AB, Allas CH, Karimi AH, Dubey I, Sreenivasan SS, Gallardo NL, Bauirjan J, Hwang ES, Davis EC, D'Ambrosio SR, Al Banna M, Cho AY, Dewar SR, Engel J, Feusner JD, Stern JM. Factors associated with delay to video-EEG in dissociative seizures. Seizure 2021; 86:155-160. [PMID: 33621828 DOI: 10.1016/j.seizure.2021.02.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/23/2021] [Accepted: 02/12/2021] [Indexed: 01/31/2023] Open
Abstract
PURPOSE While certain clinical factors suggest a diagnosis of dissociative seizures (DS), otherwise known as functional or psychogenic nonepileptic seizures (PNES), ictal video-electroencephalography monitoring (VEM) is the gold standard for diagnosis. Diagnostic delays were associated with worse quality of life and more seizures, even after treatment. To understand why diagnoses were delayed, we evaluated which factors were associated with delay to VEM. METHODS Using data from 341 consecutive patients with VEM-documented dissociative seizures, we used multivariate log-normal regression with recursive feature elimination (RFE) and multiple imputation of some missing data to evaluate which of 76 clinical factors were associated with time from first dissociative seizure to VEM. RESULTS The mean delay to VEM was 8.4 years (median 3 years, IQR 1-10 years). In the RFE multivariate model, the factors associated with longer delay to VEM included more past antiseizure medications (0.19 log-years/medication, standard error (SE) 0.05), more medications for other medical conditions (0.06 log-years/medication, SE 0.03), history of physical abuse (0.75 log-years, SE 0.27), and more seizure types (0.36 log-years/type, SE 0.11). Factors associated with shorter delay included active employment or student status (-1.05 log-years, SE 0.21) and higher seizure frequency (0.14 log-years/log[seizure/month], SE 0.06). CONCLUSIONS Patients with greater medical and seizure complexity had longer delays. Delays in multiple domains of healthcare can be common for victims of physical abuse. Unemployed and non-student patients may have had more barriers to access VEM. These results support earlier referral of complex cases to a comprehensive epilepsy center.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States.
| | - Xingruo Zhang
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Chloe E Hill
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States
| | - Emily A Janio
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Andrea M Chau
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Chelsea T Braesch
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Justine M Le
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Jessica M Hori
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Akash B Patel
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Corinne H Allas
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Amir H Karimi
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Ishita Dubey
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Siddhika S Sreenivasan
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Norma L Gallardo
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Janar Bauirjan
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Eric S Hwang
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Emily C Davis
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Shannon R D'Ambrosio
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Mona Al Banna
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Andrew Y Cho
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Sandra R Dewar
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Jerome Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States; Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States; Brain Research Institute, University of California Los Angeles, Los Angeles, CA, United States
| | - Jamie D Feusner
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - John M Stern
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
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13
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Kerr WT, Zhang X, Janio EA, Karimi AH, Allas CH, Dubey I, Sreenivasan SS, Bauirjan J, D'Ambrosio SR, Al Banna M, Cho AY, Engel J, Cohen MS, Feusner JD, Stern JM. Reliability of additional reported seizure manifestations to identify dissociative seizures. Epilepsy Behav 2021; 115:107696. [PMID: 33388672 PMCID: PMC7882023 DOI: 10.1016/j.yebeh.2020.107696] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 11/21/2020] [Accepted: 12/03/2020] [Indexed: 12/19/2022]
Abstract
PURPOSE Descriptions of seizure manifestations (SM), or semiology, can help localize the symptomatogenic zone and subsequently included brain regions involved in epileptic seizures, as well as identify patients with dissociative seizures (DS). Patients and witnesses are not trained observers, so these descriptions may vary from expert review of seizure video recordings of seizures. To better understand how reported factors can help identify patients with DS or epileptic seizures (ES), we evaluated the associations between more than 30 SMs and diagnosis using standardized interviews. METHODS Based on patient- and observer-reported data from 490 patients with diagnoses documented by video-electoencephalography, we compared the rate of each SM in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic seizure-like events (PSLE), mixed DS and ES, and inconclusive testing. RESULTS In addition to SMs that we described in a prior manuscript, the following were associated with DS: light triggers, emotional stress trigger, pre-ictal and post-ictal headache, post-ictal muscle soreness, and ictal sensory symptoms. The following were associated with ES: triggered by missing medication, aura of déjà vu, and leftward eye deviation. There were numerous manifestations separately associated with mixed ES and DS. CONCLUSIONS Reported SM can help identify patients with DS, but no manifestation is pathognomonic for either ES or DS. Patients with mixed ES and DS reported factors divergent from both ES-alone and DS-alone.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA.
| | - Xingruo Zhang
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Emily A Janio
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Amir H Karimi
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Corinne H Allas
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Ishita Dubey
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | | | - Janar Bauirjan
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Shannon R D'Ambrosio
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Mona Al Banna
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Andrew Y Cho
- Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jerome Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Brain Research Institute, University of California Los Angeles, Los Angeles, CA, USA; Departments of Radiology, Psychology, Biomedical Physics, and Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Mark S Cohen
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; California NanoSystems Institute, University of California Los Angeles, Los Angeles, CA, USA
| | - Jamie D Feusner
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - John M Stern
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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14
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Objective score from initial interview identifies patients with probable dissociative seizures. Epilepsy Behav 2020; 113:107525. [PMID: 33197798 PMCID: PMC7736162 DOI: 10.1016/j.yebeh.2020.107525] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/21/2020] [Accepted: 09/21/2020] [Indexed: 11/22/2022]
Abstract
OBJECTIVE To develop a Dissociative Seizures Likelihood Score (DSLS), which is a comprehensive, evidence-based tool using information available during the first outpatient visit to identify patients with "probable" dissociative seizures (DS) to allow early triage to more extensive diagnostic assessment. METHODS Based on data from 1616 patients with video-electroencephalography (vEEG) confirmed diagnoses, we compared the clinical history from a single neurology interview of patients in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic nonepileptic seizure-like events (PSLE), mixed DS plus ES, and inconclusive monitoring. We used data-driven methods to determine the diagnostic utility of 76 features from retrospective chart review and applied this model to prospective interviews. RESULTS The DSLS using recursive feature elimination (RFE) correctly identified 77% (95% confidence interval (CI), 74-80%) of prospective patients with either ES or DS, with a sensitivity of 74% and specificity of 84%. This accuracy was not significantly inferior than neurologists' impression (84%, 95% CI: 80-88%) and the kappa between neurologists' and the DSLS was 21% (95% CI: 1-41%). Only 3% of patients with DS were missed by both the fellows and our score (95% CI 0-11%). SIGNIFICANCE The evidence-based DSLS establishes one method to reliably identify some patients with probable DS using clinical history. The DSLS supports and does not replace clinical decision making. While not all patients with DS can be identified by clinical history alone, these methods combined with clinical judgement could be used to identify patients who warrant further diagnostic assessment at a comprehensive epilepsy center.
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15
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Beghi M, Cornaggia I, Diotti S, Erba G, Harder G, Magaudda A, Laganà A, Vitale C, Cornaggia CM. The semantics of epileptic and psychogenic nonepileptic seizures and their differential diagnosis. Epilepsy Behav 2020; 111:107250. [PMID: 32603809 DOI: 10.1016/j.yebeh.2020.107250] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/07/2020] [Accepted: 06/08/2020] [Indexed: 11/18/2022]
Affiliation(s)
| | | | - Silvia Diotti
- School of Medicine and Surgery, University of Milano Bicocca, Milano, Italy
| | - Giuseppe Erba
- Department of Neurology, University of Rochester, USA
| | - Gaia Harder
- School of Medicine and Surgery, University of Milano Bicocca, Milano, Italy
| | - Adriana Magaudda
- Epilepsy Centre, Neurological Clinic, University of Messina, Italy
| | - Angelina Laganà
- Epilepsy Centre, Neurological Clinic, University of Messina, Italy
| | - Chiara Vitale
- Epilepsy Centre, Neurological Clinic, University of Messina, Italy
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16
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Bernard-Willis Y, Oliveira ED, Lakhan SE. An Overview of Digital Health in the Transition of Pediatric to Adult Epilepsy Care. JOURNAL OF PEDIATRIC EPILEPSY 2020. [DOI: 10.1055/s-0040-1716825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
AbstractChildren with epilepsy often have impairments in cognitive and behavioral functioning which may hinder socio-occupational well-being as they reach adulthood. Adolescents with epilepsy have the added worry of health problems while starting the transition from family-centered pediatric care into largely autonomous adult care. If this transition is not appropriately planned and resourced, it may result in medical mistrust, nonadherence, and worsening biopsychosocial health as an adult. In recent years, there has been increased availability of digital health solutions that may be used during this stark change in care and treating teams. The digital health landscape includes a wide variety of technologies meant to address challenges faced by patients, caregivers, medical professionals, and health care systems. These technologies include mobile health products and wearable devices (e.g., seizure monitors and trackers, smartphone passive data collection), digital therapeutics (e.g., cognitive/behavioral health management; digital speech–language therapy), telehealth services (e.g., teleneurology visits), and health information technology (e.g., electronic medical records with patient portals). Such digital health solutions may empower patients in their journey toward optimal brain health during the vulnerable period of pediatric to adult care transition. Further research is needed to validate and measure their impact on clinical outcomes, health economics, and quality of life.
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Affiliation(s)
| | - Emily De Oliveira
- Department of Speech-Language Pathology, Spaulding Rehabilitation Hospital, Boston, Massachusetts, United States
| | - Shaheen E Lakhan
- Department of Biosciences, Global Neuroscience Initiative Foundation, Boston, Massachusetts, United States
- College of Science, Virginia Tech, Blacksburg, Virginia, United States
- Division of Neurology, Cambridge Health Alliance, Cambridge, Massachusetts, United States
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17
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Beniczky S, Arbune AA, Jeppesen J, Ryvlin P. Biomarkers of seizure severity derived from wearable devices. Epilepsia 2020; 61 Suppl 1:S61-S66. [PMID: 32519759 DOI: 10.1111/epi.16492] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 03/05/2020] [Accepted: 03/05/2020] [Indexed: 11/28/2022]
Abstract
Besides triggering alarms, wearable seizure detection devices record a variety of biosignals that represent biomarkers of seizure severity. There is a need for automated seizure characterization, to identify high-risk seizures. Wearable devices can automatically identify seizure types with the highest associated morbidity and mortality (generalized tonic-clonic seizures), quantify their duration and frequency, and provide data on postictal position and immobility, autonomic changes derived from electrocardiography/heart rate variability, electrodermal activity, respiration, and oxygen saturation. In this review, we summarize how these biosignals reflect seizure severity, and how they can be monitored in the ambulatory outpatient setting using wearable devices. Multimodal recording of these biosignals will provide valuable information for individual risk assessment, as well as insights into the mechanisms and prevention of sudden unexpected death in epilepsy.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Anca A Arbune
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Neurosciences, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Jesper Jeppesen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Vaud University Hospital Center, Lausanne, Switzerland
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18
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Ahmadi N, Pei Y, Carrette E, Aldenkamp AP, Pechenizkiy M. EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features. Brain Inform 2020; 7:6. [PMID: 32472244 PMCID: PMC7260313 DOI: 10.1186/s40708-020-00107-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 05/16/2020] [Indexed: 12/12/2022] Open
Abstract
Epilepsy and psychogenic non-epileptic seizures (PNES) often show over-lap in symptoms, especially at an early disease stage. During a PNES, the electrical activity of the brain remains normal but in case of an epileptic seizure the brain will show epileptiform discharges on the electroencephalogram (EEG). In many cases an accurate diagnosis can only be achieved after a long-term video monitoring combined with EEG recording which is quite expensive and time-consuming. In this paper using short-term EEG data, the classification of epilepsy and PNES subjects is analyzed based on signal, functional network and EEG microstate features. Our results showed that the beta-band is the most useful EEG frequency sub-band as it performs best for classifying subjects. Also the results depicted that when the coverage feature of the EEG microstate analysis is calculated in beta-band, the classification shows fairly high accuracy and precision. Hence, the beta-band and the coverage are the most important features for classification of epilepsy and PNES patients.
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Affiliation(s)
- Negar Ahmadi
- Department of Mathematics and Computer Science, Eindhoven University of Technology, TU/e, P.O.Box: 513, 5600MB, Eindhoven, NL, The Netherlands.
| | - Yulong Pei
- Department of Mathematics and Computer Science, Eindhoven University of Technology, TU/e, P.O.Box: 513, 5600MB, Eindhoven, NL, The Netherlands
| | | | - Albert P Aldenkamp
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Mykola Pechenizkiy
- Department of Mathematics and Computer Science, Eindhoven University of Technology, TU/e, P.O.Box: 513, 5600MB, Eindhoven, NL, The Netherlands
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19
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Beghi M, Cornaggia CM, Beghi E. A brief update on psychogenic non-epileptic seizures: a challenge to overcome. JOURNAL OF EPILEPTOLOGY 2020. [DOI: 10.21307/jepil-2020-003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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20
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Schulze-Bonhage A, Böttcher S, Glasstetter M, Epitashvili N, Bruno E, Richardson M, V Laerhoven K, Dümpelmann M. [Mobile seizure monitoring in epilepsy patients]. DER NERVENARZT 2019; 90:1221-1231. [PMID: 31673723 DOI: 10.1007/s00115-019-00822-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Wearables are receiving much attention from both epilepsy patients and treating physicians, for monitoring of seizure frequency and warning of seizures. They are also of interest for the detection of seizure-associated risks of patients, for differential diagnosis of rare seizure types and prediction of seizure-prone periods. Accelerometry, electromyography (EMG), heart rate and further autonomic parameters are recorded to capture clinical seizure manifestations. Currently, a clinical use to document nocturnal motor seizures is feasible. In this review the available devices, data on the performance in the documentation of seizures, current options for clinical use and developments in data analysis are presented and critically discussed.
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Affiliation(s)
- A Schulze-Bonhage
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland.
| | - S Böttcher
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
| | - M Glasstetter
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
| | - N Epitashvili
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
| | - E Bruno
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College, London, Großbritannien
| | - M Richardson
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College, London, Großbritannien
| | - K V Laerhoven
- Department Elektrotechnik und Informatik, Universität Siegen, Siegen, Deutschland
| | - M Dümpelmann
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
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