Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures

Vel, Anouk Van de; Verhaert, Kristien; Ceulemans, Berten

doi:10.1016/j.yebeh.2014.06.014

Cited by 32 publications

(38 citation statements)

References 7 publications

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“…The features were computed on 10-second epochs of 3-axis ACM, ACM magnitude, and EDA signals, with 75% overlap of epochs 22 . Three different feature sets were extracted: (I), the set employed in Poh's study (19 …”

Section: Seizure Detection: Development Of Automated Classifiers Basementioning

confidence: 99%

“…Another important limitation of this study is that analyses were done retrospectively, differently from above-mentioned studies with real-time analysis 13,15,18,19,39 . Based on the robust cross-validation approach on a large number of CSs and on our simulated real-time processing, our expectation is that performance of an algorithm embedded in a real-time system will not change significantly (as verified by preliminary tests underway).…”

Section: Limits and Future Workmentioning

confidence: 99%

“…Free watch 15,19 , Epilert 14 , Brain Sentinel 16 and EDDI alarm 17 . Except for two studies 15,18 , most algorithms have been tested on relatively small datasets, (number of seizures and recording hours), which prevents robust estimates of sensitivity and false alarm rates.…”

Section: Introductionmentioning

confidence: 99%

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Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Onorati¹,

Regalia²,

Caborni³

et al. 2017

Epilepsia

177

169

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Running title: Wearable multimodal motor seizure detectors Onorati et al. 2 Summary Objective:New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive, automated and provide false alarm rates bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. Methods:Hand-annotated video-electroencephalography seizure events were collected from 69 patients at 6 clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 hours of data, including 55 convulsive epileptic seizures (6 focal tonic-clonic seizures and 49 focal-to-bilateral-tonicclonic seizures) from 22 patients. Recordings were analyzed off-line to train and test two new machine learning classifiers and a published EDA and ACM-based classifier. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. Results:The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and false alarm rate (FAR) of 0.2 events/day.No nocturnal seizures were missed. Most patients had less than 1 false alarm every 4 days with FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures) the FAR is up to 13 times lower than the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median: 29.3 s, range: 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of post-ictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. Onorati et al. 3 Significance:The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behaviour and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning.

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Section: Seizure Detection: Development Of Automated Classifiers Basementioning

confidence: 99%

Section: Limits and Future Workmentioning

confidence: 99%

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Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Onorati¹,

Regalia²,

Caborni³

et al. 2017

Epilepsia

177

169

View full text Add to dashboard Cite

show abstract

“…The combination of radar, video and ACM may increase the ability to detect seizures. This multimodal system considers the top 10% of events per night with the most abnormal movements, regardless of the number of seizures that occurred [23]. VARIA was tested in an 8-year-old patient with focal epilepsy with secondarily generalization.…”

Section: Video Acm and Radar-induced Activity Recording (Varia)mentioning

confidence: 99%

“…There is extensive literature describing seizure detectors, but limited data on which are optimal for each seizure type [23]. This manuscript aims to address this gap in knowledge by providing a guide on how to select specific devices for individual seizure types.…”

Section: Introductionmentioning

confidence: 99%

Automated seizure detection systems and their effectiveness for each type of seizure

Ulate‐Campos

Coughlin

Gaínza‐Lein

et al. 2016

Seizure

195

166

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Epilepsy affects almost 1% of the population and most of the approximately 20-30% of patients with refractory epilepsy have one or more seizures per month. Seizure detection devices allow an objective assessment of seizure frequency and a treatment tailored to the individual patient. A rapid recognition and treatment of seizures through closed-loop systems could potentially decrease morbidity and mortality in epilepsy. However, no single detection device can detect all seizure types. Therefore, the choice of a seizure detection device should consider the patient-specific seizure semiologies. This review of the literature evaluates seizure detection devices and their effectiveness for different seizure types. Our aim is to summarize current evidence, offer suggestions on how to select the most suitable seizure detection device for each patient and provide guidance to physicians, families and researchers when choosing or designing seizure detection devices. Further, this review will guide future prospective validation studies.

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Devices to Monitor and Track Your Epilepsy

Gregg

Crepeau

2021

Epilepsy

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Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures

Cited by 32 publications

References 7 publications

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Automated seizure detection systems and their effectiveness for each type of seizure

Devices to Monitor and Track Your Epilepsy

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