Performance of ECG-based seizure detection algorithms strongly depends on training and test conditions.

ABSTRACT:

Objective

To identify non-EEG-based signals and algorithms for detection of motor and non-motor seizures in people lying in bed during video-EEG (VEEG) monitoring and to test whether these algorithms work in freely moving people during mobile EEG recordings.

Methods

Data of three groups of adult people with epilepsy (PwE) were analyzed. Group 1 underwent VEEG with additional devices (accelerometry, ECG, electrodermal activity); group 2 underwent VEEG; and group 3 underwent mobile EEG recordings both including one-lead ECG. All seizure types were analyzed. Feature extraction and machine-learning techniques were applied to develop seizure detection algorithms. Performance was expressed as sensitivity, precision, F₁ score, and false positives per 24 hours.

Results

SUBMITTER: Jahanbekam A 

PROVIDER: S-EPMC8408591 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature