Tracking generalized tonic-clonic seizures with a wrist accelerometer linked to an online database

Velez, Mariel M.; Fisher, Robert S.; Bartlett, Victoria; Le, Scheherazade

doi:10.1016/j.seizure.2016.04.009

Cited by 57 publications

(65 citation statements)

References 20 publications

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“…More recent studies demonstrated detection sensitivity ranging from 90 to 92% for convulsive seizures [15, 16], but the false positive events varied between the studies. One study reported 40 false alarms in 16 out of 73 patients [15], and another study found 81 false alarms reported in 17 patients out of a sample of 30 [16].…”

Section: Resultsmentioning

confidence: 99%

Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: a mixed-methods systematic review

2018

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ObjectivesWearable 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.MethodsA 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.ResultsIn 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.ConclusionsWearables 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.Electronic supplementary materialThe online version of this article (10.1007/s00415-018-8786-y) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: a mixed-methods systematic review

2018

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“…A study of the Epi‐Care system developed by Danish Care Technology ApS demonstrated a sensitivity of 89% for detection 39 GTCS in 20 patients in the EMU with a FAR of 0.2 per 24 h 16. A recent study of the SmartWatch system being developed by SmartMonitor, reported that the device detected 12 of 13 GTCS (92.3%) in nine patients in the EMU with 81 FAs (with the FAR not detailed) 17. A previous study of the SmartWatch had shown only a 31% sensitivity for detecting GTCS (with the FAR not reported) 18.…”

Section: Discussionmentioning

confidence: 99%

Detection of generalized tonic–clonic seizures using surface electromyographic monitoring

et al. 2017

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SummaryObjectiveA prospective multicenter phase III trial was undertaken to evaluate the performance and tolerability in the epilepsy monitoring unit (EMU) of an investigational wearable surface electromyographic (sEMG) monitoring system for the detection of generalized tonic–clonic seizures (GTCSs).MethodsOne hundred ninety‐nine patients with a history of GTCSs who were admitted to the EMU in 11 level IV epilepsy centers for clinically indicated video‐electroencephalographic monitoring also received sEMG monitoring with a wearable device that was worn on the arm over the biceps muscle. All recorded sEMG data were processed at a central site using a previously developed detection algorithm. Detected GTCSs were compared to events verified by a majority of three expert reviewers.ResultsFor all subjects, the detection algorithm detected 35 of 46 (76%, 95% confidence interval [CI] = 0.61–0.87) of the GTCSs, with a positive predictive value (PPV) of 0.03 and a mean false alarm rate (FAR) of 2.52 per 24 h. For data recorded while the device was placed over the midline of the biceps muscle, the system detected 29 of 29 GTCSs (100%, 95% CI = 0.88–1.00), with a detection delay averaging 7.70 s, a PPV of 6.2%, and a mean FAR of 1.44 per 24 h. Mild to moderate adverse events were reported in 28% (55 of 199) of subjects and led to study withdrawal in 9% (17 of 199). These adverse events consisted mostly of skin irritation caused by the electrode patch that resolved without treatment. No serious adverse events were reported.SignificanceDetection of GTCSs using an sEMG monitoring device on the biceps is feasible. Proper positioning of this device is important for accuracy, and for some patients, minimizing the number of false positives may be challenging.

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“…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%

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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Tracking generalized tonic-clonic seizures with a wrist accelerometer linked to an online database

Abstract: Automatic detection and recording of GTCS to an online database is feasible and may be more informative than seizure logging in a paper diary.

Cited by 57 publications

References 20 publications

Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: a mixed-methods systematic review

Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: a mixed-methods systematic review

Detection of generalized tonic–clonic seizures using surface electromyographic monitoring

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

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