EEG signals analysis for epileptic seizures detection using polynomial transforms, linear discriminant analysis and support vector machines.
In: Biomedical Signal Processing & Control, Jg. 62 (2020-09-01), S. N.PAG
academicJournal
Zugriff:
• A new alternative approach of EEG rhythms decomposition based Jacobi polynomial transforms (JPTs) is implemented as a one-step process. • EEG signals are explored in terms of spectral coefficients and typical statistical, entropy and energy features are extracted in time and frequency domains. • Informative and discriminative low dimension feature vectors which are easily viewed and interpreted are computed using the linear discriminant analysis (LDA). • Support vector machines (SVM) are used as classifiers for the epileptic seizures detection task. • Twelve experiments closely related to clinical applications are examined and promising results are achieved with maximum accuracies between 96.25–100 %. Electroencephalogram (EEG) signals are useful in understanding the human brain diseases like epilepsy which is characterized by an enduring predisposition to generate epileptic seizures and by neurologic, cognitive, psychological and social consequences of these conditions. That is why this paper proposed a novel full processing chain of EEG signals analysis for epileptic seizures detection that applied a new alternative approach for EEG rhythms decomposition. EEG signals are decomposed into their different background rhythms using discrete Jacobi polynomial transforms (JPTs) and a 28-dimension feature vector is extracted in frequency and time domains. Due to the high dimension and redundancy of these features, the linear discriminant analysis (LDA) is applied for dimensionality reduction and informative and discriminative low dimension feature vectors are computed and feed as inputs of the support vector machines (SVM) classifiers. The validation of this processing chain is done experimentally using an online available database which consists of five hundred EEG signals. Analysis demonstrated that the EEG rhythms decomposition using JPTs can be implemented as a single-step process and provides more abundant information for class discrimination. In addition, twelve experiments closely related to clinical applications are examined and promising results are achieved with maximum accuracies between 96.25–100 %. Overall, it is found that the proposed processing chain will be useful in providing an accurate and objective scheme for automatic EEG rhythms decomposition and epileptic seizures detection that can be integrated into implantable devices intended to predict the onset of seizures and trigger a focal treatment to block the seizures progression. [ABSTRACT FROM AUTHOR]
Copyright of Biomedical Signal Processing & Control is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
EEG signals analysis for epileptic seizures detection using polynomial transforms, linear discriminant analysis and support vector machines.
|
---|---|
Autor/in / Beteiligte Person: | Djoufack Nkengfack, Laurent Chanel ; Tchiotsop, Daniel ; Atangana, Romain ; Louis-Door, Valérie ; Wolf, Didier |
Zeitschrift: | Biomedical Signal Processing & Control, Jg. 62 (2020-09-01), S. N.PAG |
Veröffentlichung: | 2020 |
Medientyp: | academicJournal |
ISSN: | 1746-8094 (print) |
DOI: | 10.1016/j.bspc.2020.102141 |
Schlagwort: |
|
Sonstiges: |
|