Detection of K-complexes in EEG waveform images using faster R-CNN and deep transfer learning.

ABSTRACT:

Background

The electroencephalography (EEG) signal carries important information about the electrical activity of the brain, which may reveal many pathologies. This information is carried in certain waveforms and events, one of which is the K-complex. It is used by neurologists to diagnose neurophysiologic and cognitive disorders as well as sleep studies. Existing detection methods largely depend on tedious, time-consuming, and error-prone manual inspection of the EEG waveform.

Methods

In this paper, a highly accurate K-complex detection system is developed. Based on multiple convolutional neural network (CNN) feature extraction backbones and EEG waveform images, a regions with faster regions with convolutional neural networks (Faster R-CNN) detector was designed, trained, a

SUBMITTER: Khasawneh N 

PROVIDER: S-EPMC9673349 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature