Automatic identification of epileptic seizures from EEG signals using linear programming boosting. (November 2016)

• Record Type: Journal Article
• Authors: Hassan, Ahnaf Rashik; Subasi, Abdulhamit
• Is Part Of: Computer methods and programs in biomedicine. Volume 136(2016)
• Page Start: 65
• Page End: 77
• Publication Date: 2016-11
• Subjects: EEG -- Epileptic seizure detection -- Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) -- Linear programming boosting (LPBoost) classification; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.08.013
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A single lead EEG based automated epilepsy seizure screening method is proposed. A novel signal processing technique, namely CEEMDAN, is employed. We introduce LPBoost to classify epileptic seizures for the first time. Efficacy of the method is confirmed by statistical and graphical analyses. The performance of the proposed scheme, compared to the existing ones, is promising. Abstract: Background and objective: Computerized epileptic seizure detection is essential for expediting epilepsy diagnosis and research and for assisting medical professionals. Moreover, the implementation of an epilepsy monitoring device that has low power and is portable requires a reliable and successful seizure detection scheme. In this work, the problem of automated epilepsy seizure detection using singe-channel EEG signals has been addressed. Methods: At first, segments of EEG signals are decomposed using a newly proposed signal processing scheme, namely complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Six spectral moments are extracted from the CEEMDAN mode functions and train and test matrices are formed afterward. These matrices are fed into the classifier to identify epileptic seizures from EEG signal segments. In this work, we implement an ensemble learning based machine learning algorithm, namely linear programming boosting (LPBoost) to perform classification. Results: The efficacy of spectral features in the CEEMDAN domain is validated by graphical and statistical analyses. The performance of CEEMDAN is compared to those of its predecessors to further inspect its suitability. The effectiveness and the appropriateness of LPBoost are demonstrated as opposed to the commonly used classification models. Resubstitution and 10 fold cross-validation error analyses confirm the superior algorithm performance of the proposed scheme. The algorithmic performance of our epilepsy seizure identification scheme is also evaluated against state-of-the-art works in the literature. Experimental outcomes manifest that the proposed seizure detection scheme performs better than the existing works in terms of accuracy, sensitivity, specificity, and Cohen's Kappa coefficient. Conclusion: It can be anticipated that owing to its use of only one channel of EEG signal, the proposed method will be suitable for device implementation, eliminate the onus of clinicians for analyzing a large bulk of data manually, and expedite epilepsy diagnosis.