期刊
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
卷 29, 期 4, 页码 -出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065718500053
关键词
Seizure detection; EEG; kernel function mapping; dictionary pair learning
资金
- Key Program of the Natural Science Foundation of Shandong Province [ZR2013FZ002]
- Development Program of Science and Technology of Shandong [2014GSF118171]
- Key Research and Development Program of Shandong Province [2017GGX10113]
- Fundamental Research Funds of Shandong University [2014QY008]
Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. First, for the EEG data, wavelet filtering and differential filtering are applied, and the kernel function is performed to make the signal linearly separable. In DPL, the synthesis dictionary and analysis dictionary are learned jointly from original training samples with alternating minimization method, and sparse coefficients are obtained by using of linear projection instead of costly l(0)-norm or l(1)-norm optimization. At last, the reconstructed residuals associated with seizure and nonseizure sub-dictionary pairs are calculated as the decision values, and the postprocessing is performed for improving the recognition rate and reducing the false detection rate of the system. A total of 530 h from 20 patients with 81 seizures were used to evaluate the system. Our proposed method has achieved an average segment-based sensitivity of 93.39%, specificity of 98.51%, and event-based sensitivity of 96.36% with false detection rate of 0.236/h.
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