The US Food and Drug Administration (FDA) has cleared for marketing the Embrace smart watch (Empatica Inc) for seizure tracking and epilepsy management.
The Embrace smart watch uses advanced machine learning to identify convulsive seizures and sends an alert via text and phone message to caregivers.
Embrace was tested in a clinical study involving 135 patients with epilepsy who were admitted to epilepsy monitoring units for continuous monitoring with video electroencephalography while simultaneously wearing the device, which records electrodermal activity.
Researchers collected 6530 hours' worth of data over 272 days, including 40 generalized tonic-clonic seizures. Embrace's algorithm detected 100% of the seizures, confirmed by independent epilepsy experts. The device also records sleep, rest, and physical activity data.
"The FDA approval of the Embrace device to detect major convulsive seizures represents a major milestone in the care of epilepsy patients," Orrin Devinsky, director of the Comprehensive Epilepsy Center at NYU Langone in New York City, said in a news release from the company.
"Tragically, more than 3000 Americans die each year from sudden unexpected death in epilepsy (SUDEP) and the Embrace offers the potential to alarm family members and caretakers that a tonic-clonic seizure is occurring. The scientific evidence strongly supports that prompt attention during or shortly after these convulsive seizures can be life-saving in many cases," Dr Devinsky added.
Embrace was approved in Europe for seizure monitoring and alerts in April 2017.
The US Centers for Disease Control and Prevention estimates that about 3.4 million people in the United States have epilepsy, including 470,000 children.
Onorati F, Regalia G, Caborni C, Migliorini M, Bender D, Poh MZ, Frazier C, Kovitch Thropp E, Mynatt ED, Bidwell J, Mai R, LaFrance WC Jr, Blum AS, Friedman D, Loddenkemper T, Mohammadpour-Touserkani F, Reinsberger C, Tognetti S, Picard RW. Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors. Epilepsia. 2017 Nov;58(11):1870-1879.
New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors.
Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic-clonic seizures and 49 focal to bilateral tonic-clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses.
The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had <1 false alarm every 4 days, with an FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with 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 postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures.
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 behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning.