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A method for detecting high-frequency oscillations using semi-supervised k-means and mean shift clustering
- Source :
- Neurocomputing. 350:102-107
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- This paper proposes a method to detect the high-frequency oscillations (HFOs) in epileptic seizure onset zones (SOZs) localization using semi-supervised k-means and mean shift algorithm. Wavelet entropy (WE) and teager energy operator (TEO) are adopted to distinguish HFOs from normal electroencephalogram (EEG). Labeled data are used to initialize the clustering center of semi-supervised k-means algorithm, and unlabeled data are employed to obtain physiological and suspected pathological HFOs. For the suspected pathological HFOs, the mean shift algorithm is used for clustering, and the results are analyzed by the spectral center algorithm to locate SOZs. By comparing the EEG data of five patients with the results of the other three methods, it can be seen that the method proposed in this paper has good sensitivity and specificity, which is helpful for accurate localization before clinical epilepsy surgery.
- Subjects :
- 0209 industrial biotechnology
medicine.diagnostic_test
Computer science
business.industry
Cognitive Neuroscience
k-means clustering
Pattern recognition
02 engineering and technology
Electroencephalography
Computer Science Applications
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
medicine
020201 artificial intelligence & image processing
Epilepsy surgery
Epileptic seizure
Artificial intelligence
Mean-shift
Sensitivity (control systems)
medicine.symptom
Cluster analysis
business
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 350
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........67712501f3b7925a77bc829f08aa1e20