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Epilepsia. 2017 Nov;58(11):1870-1879. doi: 10.1111/epi.13899. Epub 2017 Oct 4.

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors.

Author information

1
Empatica, Milan, Italy.
2
Empatica, Cambridge, Massachusetts, U.S.A.
3
MIT Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, U.S.A.
4
Emory University Hospital Midtown, Atlanta, Georgia, U.S.A.
5
Children's Healthcare of Atlanta, Atlanta, Georgia, U.S.A.
6
Georgia Institute of Technology, Atlanta, Georgia, U.S.A.
7
Claudio Munari Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy.
8
Division of Neuropsychiatry and Behavioral Neurology, Rhode Island Hospital, Brown University, Providence, Rhode Island, U.S.A.
9
Department of Neurology, Rhode Island Hospital, Brown University, Providence, Rhode Island, U.S.A.
10
Department of Neurology, New York University Langone Medical Center, New York, New York, U.S.A.
11
Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.
12
Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, U.S.A.

Abstract

OBJECTIVE:

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.

METHODS:

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.

RESULTS:

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.

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 behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning.

KEYWORDS:

Convulsive seizures; Electrodermal activity; Epilepsy; Machine learning

PMID:
28980315
DOI:
10.1111/epi.13899
[Indexed for MEDLINE]
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