Movement‐based seizure detection

Arends, Johan

doi:10.1111/epi.14053

Cited by 19 publications

(13 citation statements)

References 24 publications

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“…To the authors' knowledge, there are no equivalent devices at the same stage of development. Several other seizure-detection devices have appeared within the last decade, some employing EEG 2,10,17,21,22 and some employing other technologies, such as EMG/accelerometry, 23,24 heart rate variability, 25 electrodermal activity, or a combination of these. 26,27 Describing the influence of an ultra-long-term EEG monitoring device on the mobility of the wearer is a key aspect of its usability.…”

Section: Discussionmentioning

confidence: 99%

Ultra‐long‐term subcutaneous home monitoring of epilepsy—490 days of EEG from nine patients

et al. 2019

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ObjectiveTo explore the feasibility of home monitoring of epilepsy patients with a novel subcutaneous electroencephalography (EEG) device, including clinical implications, safety, and compliance via the first real‐life test.MethodsWe implanted a beta‐version of the 24/7 EEG SubQ (UNEEG Medical A/S, Denmark) subcutaneously in nine participants with temporal lobe epilepsy. Data on seizures, adverse events, compliance in using the device, and use of antiepileptic drugs (AEDs) were collected. EEG was recorded for up to 3 months, and all EEG data were reviewed visually to identify electrographic seizures. These were descriptively compared to seizure counts and AED changes reported in diaries from the same period.ResultsFour hundred ninety days of EEG and 338 electrographic seizures were collected. Eight participants completed at least 9 weeks of home monitoring, while one cancelled participation after 4 weeks due to postimplantation soreness. In total, 13 cases of device‐related adverse events were registered, none of them serious. Recordings obtained from the device covered 73% of the time, on average (range 45%‐91%). Descriptively, electrographic seizure counts were substantially different from diary seizure counts. We uncovered several cases of underreporting and revealed important information on AED response. Electrographic seizure counts revealed circadian distributions of seizures not visible from seizure diaries.SignificanceThe study shows that home monitoring for up to 3 months with a subcutaneous EEG device is feasible and well tolerated. No serious adverse device‐related events were reported. An objective seizure count can be derived, which often differs substantially from self‐reported seizure counts. Larger clinical trials quantifying the benefits of objective seizure counting should be a priority for future research as well as development of algorithms for automated review of data.

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Section: Discussionmentioning

confidence: 99%

Ultra‐long‐term subcutaneous home monitoring of epilepsy—490 days of EEG from nine patients

et al. 2019

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“…Our PubMed search identified 4 prospective studies on clinical validation of accelerometry‐based, wearable devices using real‐time detection of TCS . Sensitivity and FAR of these devices were inferior or similar to the EMG‐based ones (see the detailed review on accelerometry‐based seizure detection devices in this supplement of Epilepsia ). Compared with EMG‐based detection, detection latencies of these devices are longer, since alarms are triggered during the vibratory or the clonic phase.…”

Section: Emg‐based Automated Detection Of Convulsive Seizuresmentioning

confidence: 99%

Detection of convulsive seizures using surface electromyography

2018

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Bilateral (generalized) tonic-clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low- and high-frequency signal components. Algorithms targeting increase in high-frequency EMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large-scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%-100%). The rate of false alarms (0.7-2.5/24 h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG-based seizure detection.

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“…We focused our review on automatic seizure detection based on scalp electroencephalography (scalp-EEG), electrocardiography (ECG) and surface electromyography (sEMG) because these modalities have been studied most extensively in the literature. For seizure detection based on other modalities the reader is referred to some recent excellent reviews ( 12 , 16 , 40 ).…”

Section: Measurement Parameters For Automatic Seizure Detectionmentioning

confidence: 99%

Automatic Computer-Based Detection of Epileptic Seizures

2018

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Automatic computer-based seizure detection and warning devices are important for objective seizure documentation, for SUDEP prevention, to avoid seizure related injuries and social embarrassments as a consequence of seizures, and to develop on demand epilepsy therapies. Automatic seizure detection systems can be based on direct analysis of epileptiform discharges on scalp-EEG or intracranial EEG, on the detection of motor manifestations of epileptic seizures using surface electromyography (sEMG), accelerometry (ACM), video detection systems and mattress sensors and finally on the assessment of changes of physiologic parameters accompanying epileptic seizures measured by electrocardiography (ECG), respiratory monitors, pulse oximetry, surface temperature sensors, and electrodermal activity. Here we review automatic seizure detection based on scalp-EEG, ECG, and sEMG. Different seizure types affect preferentially different measurement parameters. While EEG changes accompany all types of seizures, sEMG and ACM are suitable mainly for detection of seizures with major motor manifestations. Therefore, seizure detection can be optimized by multimodal systems combining several measurement parameters. While most systems provide sensitivities over 70%, specificity expressed as false alarm rates still needs to be improved. Patients' acceptance and comfort of a specific device are of critical importance for its long-term application in a meaningful clinical way.

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Movement‐based seizure detection

Cited by 19 publications

References 24 publications

Ultra‐long‐term subcutaneous home monitoring of epilepsy—490 days of EEG from nine patients

Ultra‐long‐term subcutaneous home monitoring of epilepsy—490 days of EEG from nine patients

Detection of convulsive seizures using surface electromyography

Automatic Computer-Based Detection of Epileptic Seizures

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