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AuthorBoashash B.
AuthorOuelha S.
Available date2022-05-31T19:01:34Z
Publication Date2016
Publication NameKnowledge-Based Systems
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.knosys.2016.05.027
URIhttp://hdl.handle.net/10576/31904
AbstractTime-frequency (TF) based machine learning methodologies can improve the design of classification systems for non-stationary signals. Using selected TF distributions (TFDs), TF feature extraction is performed on multi-channel recordings using channel fusion and feature fusion approaches. Following the findings of previous studies, a TF feature set is defined to include three complementary categories: signal related features, statistical features and image features. Multi-class strategies are then used to improve the classification algorithm robustness to artifacts. The optimal subset of TF features is selected using the wrapper method with sequential forward feature selection (SFFS). In addition, a new proposed measure for TF feature selection is shown to improve the sensitivity of the classifier (while slightly reducing total accuracy and specificity). As an illustration, the TF approach is applied to the design of a system for detection of seizure activity in real newborn EEG signals. Experimental results indicate that: (1) The compact kernel distribution (CKD) outperforms other TFDs in classification accuracy; (2) a feature fusion strategy gives better classification than a channel fusion strategy; e.g. using all TF features, the CKD achieves a classification accuracy of 82% with feature fusion, which is 4% more than the channel fusion approach; (3) the SFFS wrapper feature selection method improves the classification performance for all TFDs; e.g. total accuracy is improved by 4.6%; (4) the multi-class strategy improves the seizure detection accuracy in the presence of artifacts; e.g. a total accuracy of 86.61% with one vs. one multi-class approach is achieved i.e. 0.91% more than the binary classification approach. The results obtained on a large practical real data set confirm the improved performance capability of TF features for knowledge based systems
Languageen
PublisherElsevier B.V.
SubjectArtificial intelligence
Biomedical signal processing
Decision making
Extraction
Feature extraction
Knowledge based systems
Learning systems
Signal detection
Binary Classification Approach
Classification performance
Clinical decision making
Feature selection methods
Forward feature selections
High resolution
Newborn EEG seizure
Time frequency analysis
Classification (of information)
TitleAutomatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study
TypeArticle
Pagination38-50
Volume Number106
dc.accessType Abstract Only


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