期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 64, 期 9, 页码 2230-2240出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2016.2633391
关键词
Epilepsy; Gabor filters; interictal high-frequency oscillations (HFOs); intracerebral electroencephalography (iEEG); signal classification; signal detection; support vector machines (SVM)
资金
- ANR, project FORCE [ANR-13-TECS-0013]
- Agence Nationale de la Recherche (ANR) [ANR-13-TECS-0013] Funding Source: Agence Nationale de la Recherche (ANR)
Goal: Interictal high-frequency oscillations (HFOs [30-600 Hz]) have proven to be relevant biomarkers in epilepsy. In this paper, four categories of HFOs are considered: Gamma ([30-80 Hz]), high-gamma ([80-120 Hz]), ripples ([120-250 Hz]), and fast-ripples ([250-600 Hz]). A universal detector of the four types of HFOs is proposed. It has the advantages of 1) classifying HFOs, and thus, being robust to inter and intrasubject variability; 2) rejecting artefacts, thus being specific. Methods: Gabor atoms are tuned to cover the physiological bands. Gabor transform is then used to detect HFOs in intracerebral electroencephalography (iEEG) signals recorded in patients candidate to epilepsy surgery. To extract relevant features, energy ratios, along with event duration, are investigated. Discriminant ratios are optimized so as to maximize among the four types of HFOs and artefacts. A multiclass support vector machine (SVM) is used to classify detected events. Pseudoreal signals are simulated to measure the performance of the method when the ground truth is known. Results: Experiments are conducted on simulated and on human iEEG signals. The proposed method shows high performance in terms of sensitivity and false discovery rate. Conclusion: The methods have the advantages of detecting and discriminating all types of HFOs as well as avoiding false detections caused by artefacts. Significance: Experimental results show the feasibility of a robust and universal detector.
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