SmartWatch by SmartMonitor: Assessment of Seizure Detection Efficacy for Various Seizure Types in Children, a Large Prospective Single-Center Study

Patterson, Amy L.; Mudigoudar, Basanagoud; Fulton, Stephen; McGregor, Amy; Poppel, Kate Van; Wheless, Margaret C.; Brooks, Lai; Wheless, James W.

doi:10.1016/j.pediatrneurol.2015.07.002

Cited by 65 publications

(59 citation statements)

References 13 publications

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“…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. No AEs have been reported with use of wrist accelerometry devices 16, 17, 18.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…No AEs have been reported with use of wrist accelerometry devices 16, 17, 18. In addition, the low power consumption of accelerometry monitoring and Bluetooth transmission of captured data allows for battery life of up to 5 days 18. These devices, which show promising results from pilot studies, are not yet FDA approved.…”

Section: Discussionmentioning

confidence: 99%

Detection of generalized tonic–clonic seizures using surface electromyographic monitoring

et al. 2017

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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%

“…Existing automated seizure detectors 11 measure motion to detect seizures with a motor manifestation. Algorithms based on wrist acceleration (ACM) 13,14,15 or electromyogram (EMG) 16,17 have been commercialized into the SmartWatch 13,18 , Epi-Care…”

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

173

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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Multimodal, automated detection of nocturnal motor seizures at home: Is a reliable seizure detector feasible?

et al. 2017

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SummaryObjectiveAutomated seizure detection and alarming could improve quality of life and potentially prevent sudden, unexpected death in patients with severe epilepsy. As currently available systems focus on tonic–clonic seizures, we want to detect a broader range of seizure types, including tonic, hypermotor, and clusters of seizures.MethodsIn this multicenter, prospective cohort study, the nonelectroencephalographic (non‐EEG) signals heart rate and accelerometry were measured during the night in patients undergoing a diagnostic video‐EEG examination. Based on clinical video‐EEG data, seizures were classified and categorized as clinically urgent or not. Seizures included for analysis were tonic, tonic–clonic, hypermotor, and clusters of short myoclonic/tonic seizures. Features reflecting physiological changes in heart rate and movement were extracted. Detection algorithms were developed based on stepwise fulfillment of conditions during increases in either feature. A training set was used for development of algorithms, and an independent test set was used for assessing performance.ResultsNinety‐five patients were included, but due to sensor failures, data from only 43 (of whom 23 patients had 86 seizures, representing 402 h of data) could be used for analysis. The algorithms yield acceptable sensitivities, especially for clinically urgent seizures (sensitivity = 71–87%), but produce high false alarm rates (2.3–5.7 per night, positive predictive value = 25–43%). There was a large variation in the number of false alarms per patient.SignificanceIt seems feasible to develop a detector with high sensitivity, but false alarm rates are too high for use in clinical practice. For further optimization, personalization of algorithms may be necessary.

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SmartWatch by SmartMonitor: Assessment of Seizure Detection Efficacy for Various Seizure Types in Children, a Large Prospective Single-Center Study

Cited by 65 publications

References 13 publications

Detection of generalized tonic–clonic seizures using surface electromyographic monitoring

Detection of generalized tonic–clonic seizures using surface electromyographic monitoring

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Multimodal, automated detection of nocturnal motor seizures at home: Is a reliable seizure detector feasible?

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