title: An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.
abstract: Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.
authors.affiliation: 1 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.|2 Suzhou Institute of Shandong University, Suzhou 215123, P. R. China.
authors.forename: Junhui
authors.initials: J
authors.lastname: Li
authors.affiliation: 1 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.|2 Suzhou Institute of Shandong University, Suzhou 215123, P. R. China.
authors.forename: Weidong
authors.initials: W
authors.lastname: Zhou
authors.affiliation: 1 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.|2 Suzhou Institute of Shandong University, Suzhou 215123, P. R. China.
authors.forename: Shasha
authors.initials: S
authors.lastname: Yuan
authors.affiliation: 1 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.|2 Suzhou Institute of Shandong University, Suzhou 215123, P. R. China.
authors.forename: Yanli
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: 1 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.|2 Suzhou Institute of Shandong University, Suzhou 215123, P. R. China.
authors.forename: Chengcheng
authors.initials: C
authors.lastname: Li
authors.affiliation: 1 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.|2 Suzhou Institute of Shandong University, Suzhou 215123, P. R. China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity
keywords: Seizure detection; dictionary learning algorithm; elastic net; kernel trick