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Vakilna YS, Li X, Hampson JS, Huang Y, Mosher JC, Dabaghian Y, Luo X, Talavera B, Pati S, Masel T, Hays R, Szabo CA, Zhang GQ, Lhatoo SD. Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study. Epilepsia 2024; 65:2054-2068. [PMID: 38738972 PMCID: PMC11251850 DOI: 10.1111/epi.17974] [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: 12/13/2023] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 05/14/2024]
Abstract
OBJECTIVE The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.
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Affiliation(s)
- Yash Shashank Vakilna
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Xiaojin Li
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Jaison S. Hampson
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Yan Huang
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - John C. Mosher
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Yuri Dabaghian
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Xi Luo
- The University of Texas Health Science Center at Houston, Department of Biostatistics and Data Science, Houston, Texas, USA
| | - Blanca Talavera
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Sandipan Pati
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Todd Masel
- The University of Texas Medical Branch, Department of Neurology, Galveston, Texas, USA
| | - Ryan Hays
- The University of Texas Southwestern Medical Center, Department of Neurology, Dallas, Texas, USA
| | - Charles Akos Szabo
- The University of Texas Health Science Center at San Antonio, Department of Neurology, Texas, USA
| | - Guo-Qiang Zhang
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Samden D. Lhatoo
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
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Automated temporal lobe epilepsy and psychogenic nonepileptic seizure patient discrimination from multichannel EEG recordings using DWT based analysis. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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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: 29] [Impact Index Per Article: 14.5] [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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Datta S, Karmakar CK, Yan B, Palaniswami M. Analyzing Distance Measures for Upper Limb Activity Measurement in Hemiparetic Stroke Patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3735-3738. [PMID: 33018813 DOI: 10.1109/embc44109.2020.9175758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Stroke survivors are often characterized by upper limb hemiparesis due to which activities in one of the hands is significantly restricted. Manual evaluation of the progression of hemiparesis in acute stroke patients involves 24x7 medical supervision, which is prone to inter-rater variability, is labor-intensive and consequently expensive in public hospitals. In this paper, we investigate the use of wrist-worn accelerometers for automated identification of upper limb hemiparesis in acute stroke. We propose a set of spontaneous and instructed movements in order to estimate two-hand activity correlation using accelerometry data. We use this information to determine the weak hand and further investigate an Activity Based Distance (ABD) measure to quantify this correlation. We compare ABD with standard time-series distance measures such as Lp norms and Dynamic Time Warping (DTW) for hemiparetic severity estimation. We study these distance measures with respect to the National Institutes of Health Stroke Scale (NIHSS), the clinical gold standard to determine hemiparetic severity, and demonstrate their suitability for developing a wearable based automated hemiparesis detection and monitoring system.Clinical relevance-This study presents a novel experimental paradigm for identifying upper limb hemiparesis in acute stroke patients using measures of two-hand activity correlation.
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George ST, Subathra M, Sairamya N, Susmitha L, Joel Premkumar M. Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.02.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Datta S, Karmakar CK, Rao AS, Yan B, Palaniswami M. Automated Scoring of Hemiparesis in Acute Stroke From Measures of Upper Limb Co-Ordination Using Wearable Accelerometry. IEEE Trans Neural Syst Rehabil Eng 2020; 28:805-816. [DOI: 10.1109/tnsre.2020.2972285] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Simblett SK, Biondi A, Bruno E, Ballard D, Stoneman A, Lees S, Richardson MP, Wykes T. Patients' experience of wearing multimodal sensor devices intended to detect epileptic seizures: A qualitative analysis. Epilepsy Behav 2020; 102:106717. [PMID: 31785481 DOI: 10.1016/j.yebeh.2019.106717] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 11/13/2019] [Accepted: 11/13/2019] [Indexed: 01/10/2023]
Abstract
BACKGROUND The health management of patients with epilepsy could be improved by wearing devices that reliably detect when epileptic seizures happen. For the devices to be widely adopted, they must be acceptable and easy to use for patients, and their views are very important. Previous studies have collected feedback from patients on hypothetical devices, but very few have examined experience of wearing actual devices. PURPOSE This study assessed the first-hand experiences of people with epilepsy using wearable devices, continuously over a period of time. The aim was to understand how acceptable and easy they were to use, and whether it is reasonable to expect that people will use them. MATERIALS AND METHODS Adults with a diagnosis of epilepsy admitted routinely to a hospital epilepsy monitoring unit were asked to wear one, or more, wearable biosensor devices, tested for seizure detection. The devices are designed to continuously monitor and record signals from the body (biosignals). Participants completed semistructured interviews about their experiences of wearing the device(s). A systematic thematic analysis extracted themes from the interviews, focusing on acceptability and usability. Feedback was organized into (1) participants' experiences of the devices, any support they required and reasons for stopping wearing them; (2) their thoughts about using this technology outside a hospital setting. RESULTS Twenty-one people with epilepsy wore one, or more, wearable devices for an average of 112.81 (SD = 71.83) hours. Participants found the devices convenient, and had no problem wearing them in hospital or sharing the data collected from them with the researchers and medical professionals. However, the presence of wires, bulky size, discomfort, and need for support, moderated experience. Participants' thoughts about wearing them in everyday life were strongly influenced by how visible and perceived accuracy. Willingness to use a smartphone app to complete questionnaires depended on the frequency, number of questions, and support. CONCLUSIONS Overall, this work provides evidence about the feasibility and acceptability of using wearable devices to monitor seizure activity in people with epilepsy. Key barriers and facilitators to use while in hospital and hypothetical use in everyday life were identified and will be helpful for guiding future implementation.
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Affiliation(s)
- Sara Katherine Simblett
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, United Kingdom.
| | - Andrea Biondi
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), London, United Kingdom
| | - Elisa Bruno
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), London, United Kingdom
| | - Dominic Ballard
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), London, United Kingdom
| | - Amanda Stoneman
- Epilepsy Action (British Epilepsy Association), New Anstey House, Leeds, United Kingdom; RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom
| | - Simon Lees
- RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom
| | - Mark P Richardson
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), London, United Kingdom; NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust, King's College London, London, United Kingdom
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, United Kingdom; NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust, King's College London, London, United Kingdom
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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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Bigelow MD, Kouzani AZ. Neural stimulation systems for the control of refractory epilepsy: a review. J Neuroeng Rehabil 2019; 16:126. [PMID: 31665058 PMCID: PMC6820988 DOI: 10.1186/s12984-019-0605-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 10/10/2019] [Indexed: 12/18/2022] Open
Abstract
Epilepsy affects nearly 1% of the world's population. A third of epilepsy patients suffer from a kind of epilepsy that cannot be controlled by current medications. For those where surgery is not an option, neurostimulation may be the only alternative to bring relief, improve quality of life, and avoid secondary injury to these patients. Until recently, open loop neurostimulation was the only alternative for these patients. However, for those whose epilepsy is applicable, the medical approval of the responsive neural stimulation and the closed loop vagal nerve stimulation systems have been a step forward in the battle against uncontrolled epilepsy. Nonetheless, improvements can be made to the existing systems and alternative systems can be developed to further improve the quality of life of sufferers of the debilitating condition. In this paper, we first present a brief overview of epilepsy as a disease. Next, we look at the current state of biomarker research in respect to sensing and predicting epileptic seizures. Then, we present the current state of open loop neural stimulation systems. We follow this by investigating the currently approved, and some of the recent experimental, closed loop systems documented in the literature. Finally, we provide discussions on the current state of neural stimulation systems for controlling epilepsy, and directions for future studies.
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Affiliation(s)
- Matthew D Bigelow
- School of Engineering, Deakin University, Geelong, Victoria, 3216, Australia
| | - Abbas Z Kouzani
- School of Engineering, Deakin University, Geelong, Victoria, 3216, Australia.
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Naganur VD, Kusmakar S, Chen Z, Palaniswami MS, Kwan P, O'Brien TJ. The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non-epileptic seizures. Epilepsia Open 2019; 4:309-317. [PMID: 31168498 PMCID: PMC6546070 DOI: 10.1002/epi4.12327] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 04/14/2019] [Accepted: 04/22/2019] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE Accurate differentiation between epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) can be challenging based on history alone. Inpatient video EEG monitoring (VEM) is often needed for a definitive diagnosis. However, VEM is highly resource intensive, is of limited availability, and cannot be undertaken over long periods. Previous research has shown that time-frequency analysis of accelerometer data could be utilized to differentiate between ES and PNES. Using a seizure detection and classification algorithm, we sought to examine the diagnostic utility of an automated analysis with an ambulatory accelerometer. METHODS A wrist-worn device was used to collect accelerometer data from patients during VEM admission, for diagnostic evaluation of convulsive seizures. An automated process, that involved the use of K-means clustering and support vector machines, was used to detect and classify each seizure as ES or PNES. The results were compared with VEM diagnoses determined by epileptologists blinded to the accelerometer data. RESULTS Twenty-four convulsive seizures, consisting of at least 20 seconds of sustained continuous activity, recorded from 11 patients during inpatient VEM (13 PNES from five patients and 11 ES from six patients) were included for analysis. The automated system detected all convulsive seizures (ES, PNES) from >661 hours of recording with 67 false alarms (2.4 per 24 hours). The sensitivity and specificity for classifying ES from PNES were 72.7% and 100%, respectively. The positive and negative predictive values for classifying PNES were 81.3% and 100%, respectively. There was no significant difference between the classification results obtained from the automation process and the VEM diagnoses. SIGNIFICANCE This automated system can potentially provide a wearable out-of-hospital seizure diagnostic monitoring system.
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Affiliation(s)
- Vaidehi D. Naganur
- Departments of Neurology and MedicineThe Melbourne Brain Centre, The Royal Melbourne HospitalParkvilleVictoriaAustralia
| | - Shitanshu Kusmakar
- Department of Electrical EngineeringThe University of MelbourneParkvilleVictoriaAustralia
| | - Zhibin Chen
- Department of Electrical EngineeringThe University of MelbourneParkvilleVictoriaAustralia
| | | | - Patrick Kwan
- Departments of Neurology and MedicineThe Melbourne Brain Centre, The Royal Melbourne HospitalParkvilleVictoriaAustralia
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
| | - Terence J. O'Brien
- Departments of Neurology and MedicineThe Melbourne Brain Centre, The Royal Melbourne HospitalParkvilleVictoriaAustralia
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
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Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device. IEEE Trans Biomed Eng 2019; 66:421-432. [DOI: 10.1109/tbme.2018.2845865] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Ahmedt-Aristizabal D, Denman S, Nguyen K, Sridharan S, Dionisio S, Fookes C. Understanding Patients' Behavior: Vision-Based Analysis of Seizure Disorders. IEEE J Biomed Health Inform 2019; 23:2583-2591. [PMID: 30714935 DOI: 10.1109/jbhi.2019.2895855] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A substantial proportion of patients with functional neurological disorders (FND) are being incorrectly diagnosed with epilepsy because their semiology resembles that of epileptic seizures (ES). Misdiagnosis may lead to unnecessary treatment and its associated complications. Diagnostic errors often result from an overreliance on specific clinical features. Furthermore, the lack of electrophysiological changes in patients with FND can also be seen in some forms of epilepsy, making diagnosis extremely challenging. Therefore, understanding semiology is an essential step for differentiating between ES and FND. Existing sensor-based and marker-based systems require physical contact with the body and are vulnerable to clinical situations such as patient positions, illumination changes, and motion discontinuities. Computer vision and deep learning are advancing to overcome these limitations encountered in the assessment of diseases and patient monitoring; however, they have not been investigated for seizure disorder scenarios. Here, we propose and compare two marker-free deep learning models, a landmark-based and a region-based model, both of which are capable of distinguishing between seizures from video recordings. We quantify semiology by using either a fusion of reference points and flow fields, or through the complete analysis of the body. Average leave-one-subject-out cross-validation accuracies for the landmark-based and region-based approaches of 68.1% and 79.6% in our dataset collected from 35 patients, reveal the benefit of video analytics to support automated identification of semiology in the challenging conditions of a hospital setting.
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Zhao X, Lhatoo SD. Seizure detection: do current devices work? And when can they be useful? Curr Neurol Neurosci Rep 2018; 18:40. [PMID: 29796939 DOI: 10.1007/s11910-018-0849-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW The unpredictability and apparent randomness of epileptic seizures is one of the most vexing aspects of epilepsy. Methods or devices capable of detecting seizures may help prevent injury or even death and significantly improve quality of life. Here, we summarize and evaluate currently available, unimodal, or polymodal detection systems for epileptic seizures, mainly in the ambulatory setting. RECENT FINDINGS There are two broad categories of detection devices: EEG-based and non-EEG-based systems. Wireless wearable EEG devices are now available both in research and commercial arenas. Neuro-stimulation devices are currently evolving and initial experiences of these show potential promise. As for non-EEG devices, different detecting systems show different sensitivity according to the different patient and seizure types. Regardless, when used in combination, these modalities may complement each other to increase positive predictive value. Although some devices with high sensitivity are promising, practical widespread use of such detection systems is still some way away. More research and experience are needed to evaluate the most efficient and integrated systems, to allow for better approaches to detection and prediction of seizures. The concept of closed-loop systems and prompt intervention may substantially improve quality of life for patients and carers.
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Affiliation(s)
- Xiuhe Zhao
- Epilepsy Center, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA.,Neurology Department, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
| | - Samden D Lhatoo
- Epilepsy Center, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA. .,NIH/NINDS Center for SUDEP Research, Boston, MA, USA.
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Johansson D, Malmgren K, Alt Murphy M. Wearable sensors for clinical applications in epilepsy, Parkinson's disease, and stroke: a mixed-methods systematic review. J Neurol 2018; 265:1740-1752. [PMID: 29427026 PMCID: PMC6060770 DOI: 10.1007/s00415-018-8786-y] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 02/01/2018] [Accepted: 02/02/2018] [Indexed: 01/25/2023]
Abstract
OBJECTIVES Wearable technology is increasingly used to monitor neurological disorders. The purpose of this systematic review was to synthesize knowledge from quantitative and qualitative clinical researches using wearable sensors in epilepsy, Parkinson's disease (PD), and stroke. METHODS A systematic literature search was conducted in PubMed and Scopus spanning from 1995 to January 2017. A synthesis of the main findings, reported adherence to wearables and missing data from quantitative studies, is provided. Clinimetric properties of measures derived from wearables in laboratory, free activities in hospital, and free-living environment were also evaluated. Qualitative thematic synthesis was conducted to explore user experiences and acceptance of wearables. RESULTS In total, 56 studies (50 reporting quantitative and 6 reporting qualitative data) were included for data extraction and synthesis. Among studies reporting quantitative data, 5 were in epilepsy, 21 PD, and 24 studies in stroke. In epilepsy, wearables are used to detect and differentiate seizures in hospital settings. In PD, the focus is on quantification of cardinal motor symptoms and medication-evoked adverse symptoms in both laboratory and free-living environment. In stroke upper extremity activity, walking and physical activity have been studied in laboratory and during free activities. Three analytic themes emerged from thematic synthesis of studies reporting qualitative data: acceptable integration in daily life, lack of confidence in technology, and the need to consider individualization. CONCLUSIONS Wearables may provide information of clinical features of interest in epilepsy, PD and stroke, but knowledge regarding the clinical utility for supporting clinical decision making remains to be established.
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Affiliation(s)
- Dongni Johansson
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
| | - Kristina Malmgren
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Margit Alt Murphy
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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15
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Biogeography based hybrid scheme for automatic detection of epileptic seizures from EEG signatures. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.12.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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16
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Sarma AK, Khandker N, Kurczewski L, Brophy GM. Medical management of epileptic seizures: challenges and solutions. Neuropsychiatr Dis Treat 2016; 12:467-85. [PMID: 26966367 PMCID: PMC4771397 DOI: 10.2147/ndt.s80586] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Epilepsy is one of the most common neurologic illnesses. This condition afflicts 2.9 million adults and children in the US, leading to an economic impact amounting to $15.5 billion. Despite the significant burden epilepsy places on the population, it is not very well understood. As this understanding continues to evolve, it is important for clinicians to stay up to date with the latest advances to provide the best care for patients. In the last 20 years, the US Food and Drug Administration has approved 15 new antiepileptic drugs (AEDs), with many more currently in development. Other advances have been achieved in terms of diagnostic modalities like electroencephalography technology, treatment devices like vagal nerve and deep-brain stimulators, novel alternate routes of drug administration, and improvement in surgical techniques. Specific patient populations, such as the pregnant, elderly, those with HIV/AIDS, and those with psychiatric illness, present their own unique challenges, with AED side effects, drug interactions, and medical-psychiatric comorbidities adding to the conundrum. The purpose of this article is to review the latest literature guiding the management of acute epileptic seizures, focusing on the current challenges across different practice settings, and it discusses studies in various patient populations, including the pregnant, geriatric, those with HIV/AIDS, comatose, psychiatric, and "pseudoseizure" patients, and offers possible evidence-based solutions or the expert opinion of the authors. Also included is information on newer AEDs, routes of administration, and significant AED-related drug-interaction tables. This review has tried to address only some of these issues that any practitioner who deals with the acute management of seizures may encounter. The document also highlights the numerous avenues for new research that would help practitioners optimize epilepsy management.
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Affiliation(s)
- Anand K Sarma
- Department of Neurology, Virginia Commonwealth University, Richmond, VA, USA
| | - Nabil Khandker
- Department of Neurology, Virginia Commonwealth University, Richmond, VA, USA
| | - Lisa Kurczewski
- Departments of Pharmacotherapy & Outcomes Science and Neurosurgery, Virginia Commonwealth University, Richmond, VA, USA
| | - Gretchen M Brophy
- Departments of Pharmacotherapy & Outcomes Science and Neurosurgery, Virginia Commonwealth University, Richmond, VA, USA
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