Principles of time–frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection

作者：

Highlights：

• We propose (t,f) based features for detecting change in nonstationary signals.

• We use the features to detect seizures and artifacts in newborn EEGs.

• The features result in an improved performance in detecting seizures and artifacts.

• Performance of (t,f) features depends on the type of time–frequency distribution.

摘要

Highlights•We propose (t,f) based features for detecting change in nonstationary signals.•We use the features to detect seizures and artifacts in newborn EEGs.•The features result in an improved performance in detecting seizures and artifacts.•Performance of (t,f) features depends on the type of time–frequency distribution.

论文关键词：Time–frequency feature extraction,Abnormality detection,Seizure,Newborn EEG artifacts,ROC analysis

论文评审过程：Received 30 January 2014, Revised 7 August 2014, Accepted 18 August 2014, Available online 27 August 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.08.016