An automated, machine learning–based detection algorithm for spike‐wave discharges (SWDs) in a mouse model of absence epilepsy
Abstract
Manual detection of spike‐wave discharges (SWDs) from electroencephalography (EEG) records is time intensive, costly, and subject to inconsistencies/biases. In addition, manual scoring often omits information on SWD confidence/intensity, which may be important for the investigation of mechanistic‐based research questions. Our objective is to develop an automated method for the detection of SWDs in a mouse model of absence epilepsy that is focused on the characteristics of human scoring of preselected events to establish a confidence‐based, continuous‐valued scoring.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 06, 2019
- Source ID
- 10.1002/epi4.12303
Entities
People
- Jesse A Pfammatter
- Mathew V Jones
- Rama Maganti
Organizations
- National Institutes of Health
- United States Department of Defense
- University of Wisconsin–Madison