Epileptic seizure detection in long-term EEG records using sparse rational decomposition and local Gabor binary patterns feature extraction

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• 作者：Kaveh Samiee ; ^a ; ^{kaveh.samiee@student.tut.fi} ; P&eacute ; ter Ková ; cs¹ ; ^b ; ^{kovika@inf.elte.hu} ; Moncef Gabbouj^a ; ^{moncef.gabbouj@tut.fi}
• 关键词：Local Gabor binary patterns ; Rational functions ; Seizure onset detection ; Seizure classification ; CHB-MIT database
• 刊名：Knowledge-Based Systems
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：118
• 期：Complete
• 页码：228-240
• 全文大小：1299 K
• 卷排序：118

文摘

In this paper, we address the problem of off-line supervised detection of epileptic seizures in long-term Electroencephalography (EEG) records. A novel feature extraction method is proposed based on the sparse rational decomposition and the Local Gabor Binary Patterns (LGBP). Namely, we decompose the channels of the EEG record into 8 sparse rational components using a group of optimal coefficients. Then, a modified 1D LGBP operator is applied, which is followed by downsampling of the data. The width of the largest LGBPs is finally computed for all the 8 rational components and the 23 channels of the EEG record. Hence, we characterize seizure patterns of one-second-long EEG epochs by 23 × 8 features. The effectiveness of the proposed feature extraction method is assessed using different classifiers which are trained with 25% of early EEG records of each patient. We performed an extensive comparative study over 163 h of EEG recordings from the CHB-MIT Scalp EEG database. The experiments show that the proposed technique outperforms other dedicated techniques by achieving the overall sensitivity of 70.4% and the overall specificity of 99.1%, in the patient-specific detection of epileptic EEG epochs. Moreover, it detects onset of seizures with the overall sensitivity of 91.13% and false alarms per hour rate of 0.35, on average.