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 EEG records is time intensive, costly and subject to inconsistencies and biases. Additionally, manual scoring often omits information on SWD confidence/intensity which may be important for the investigation of mechanistic-based research questions. While there are some automated and semi-automated methods for the detection of SWDs in humans and rats, there has been minimal development of these methods for SWDs in mice. Here we develop a support vector machine (SVM)-based algorithm for the automated detection of SWDs in the gamma2R43Q mouse model of absence epilepsy. The algorithm first identifies putative SWD events using frequency- and amplitude-based peak detection. Four humans experienced at identifying SWDs scored a set of 2500 putat

SUBMITTER: Jesse Pfammatter 

PROVIDER: S-BSST208 | biostudies-other |

REPOSITORIES: biostudies-other