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Real-Time Seizure Detection Using Behind-the-Ear Wearable System.
Lehnen, Jamie; Venkatesh, Pooja; Yao, Zhuoran; Aziz, Abdul; Nguyen, Phuc V P; Harvey, Jay; Alick-Lindstrom, Sasha; Doyle, Alex; Podkorytova, Irina; Perven, Ghazala; Hays, Ryan; Zepeda, Rodrigo; Das, Rohit R; Ding, Kan.
Affiliation
  • Lehnen J; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX.
  • Venkatesh P; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX.
  • Yao Z; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX.
  • Aziz A; Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX; and.
  • Nguyen PVP; Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX; and.
  • Harvey J; College of Information and Computer Science, University of Massachusets Amherst, Amherst, MA.
  • Alick-Lindstrom S; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX.
  • Doyle A; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX.
  • Podkorytova I; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX.
  • Perven G; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX.
  • Hays R; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX.
  • Zepeda R; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX.
  • Das RR; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX.
  • Ding K; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX.
J Clin Neurophysiol ; 2024 Feb 20.
Article in En | MEDLINE | ID: mdl-38376923
ABSTRACT

INTRODUCTION:

This study examines the usability and comfort of a behind-the-ear seizure detection device called brain seizure detection (BrainSD) that captures ictal electroencephalogram (EEG) data using four scalp electrodes.

METHODS:

This is a feasibility study. Thirty-two patients admitted to a level 4 Epilepsy Monitoring Unit were enrolled. The subjects wore BrainSD and the standard 21-channel video-EEG simultaneously. Epileptologists analyzed the EEG signals collected by BrainSD and validated it using video-EEG data to confirm its accuracy. A poststudy survey was completed by each participant to evaluate the comfort and usability of the device. In addition, a focus group of UT Southwestern epileptologists was held to discuss the features they would like to see in a home EEG-based seizure detection device such as BrainSD.

RESULTS:

In total, BrainSD captured 11 of the 14 seizures that occurred while the device was being worn. All 11 seizures captured on BrainSD had focal onset, with three becoming bilateral tonic-clonic and one seizure being of subclinical status. The device was worn for an average of 41 hours. The poststudy survey showed that most users found the device comfortable, easy-to-use, and stated they would be interested in using BrainSD. Epileptologists in the focus group expressed a similar interest in BrainSD.

CONCLUSIONS:

Brain seizure detection is able to detect EEG signals using four behind-the-ear electrodes. Its comfort, ease-of-use, and ability to detect numerous types of seizures make BrainSD an acceptable at-home EEG detection device from both the patient and provider perspective.

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Clin Neurophysiol Journal subject: FISIOLOGIA / NEUROLOGIA Year: 2024 Document type: Article Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Clin Neurophysiol Journal subject: FISIOLOGIA / NEUROLOGIA Year: 2024 Document type: Article Country of publication: