This paper presents new time-frequency features for seizure detection in newborn EEG signals. These features are obtained by translating some relevant time features or frequency features to the joint time-frequency domain. A calibration procedure is then used for verification. The relevant translated features are ranked and selected according to maximal-relevance and minimal-redundancy criteria. The selected features improve the performance of newborn EEG seizure detection and classification systems by up to 4% for 100 real newborn EEG segments. © 2012 Springer-Verlag.
CITATION STYLE
Boashash, B., & Boubchir, L. (2012). On the selection of time-frequency features for improving the detection and classification of newborn EEG seizure signals and other abnormalities. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7666 LNCS, pp. 634–643). https://doi.org/10.1007/978-3-642-34478-7_77
Mendeley helps you to discover research relevant for your work.