title: Automated patient-specific classification of long-term Electroencephalography.
abstract: This paper presents a novel systematic approach for patient-specific classification of long-term Electroencephalography (EEG). The goal is to extract the seizure sections with a high accuracy to ease the Neurologist's burden of inspecting such long-term EEG data. We aim to achieve this using the minimum feedback from the Neurologist. To accomplish this, we use the majority of the state-of-the-art features proposed in this domain for evolving a collective network of binary classifiers (CNBC) using multi-dimensional particle swarm optimization (MD PSO). Multiple CNBCs are then used to form a CNBC ensemble (CNBC-E), which aggregates epileptic seizure frames from the classification map of each CNBC in order to maximize the sensitivity rate. Finally, a morphological filter forms the final epileptic segments while filtering out the outliers in the form of classification noise. The proposed system is fully generic, which does not require any a priori information about the patient such as the list of relevant EEG channels. The results of the classification experiments, which are performed over the benchmark CHB-MIT scalp long-term EEG database show that the proposed system can achieve all the aforementioned objectives and exhibits a significantly superior performance compared to several other state-of-the-art methods. Using a limited training dataset that is formed by less than 2 min of seizure and 24 min of non-seizure data on the average taken from the early 25% section of the EEG record of each patient, the proposed system establishes an average sensitivity rate above 89% along with an average specificity rate above 93% over the test set.
authors.affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
authors.forename: Serkan
authors.initials: S
authors.lastname: Kiranyaz
authors.affiliation: Department of Electrical and Electronics Engineering, Izmir University of Economics, Turkey.
authors.forename: Turker
authors.initials: T
authors.lastname: Ince
authors.affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
authors.forename: Morteza
authors.initials: M
authors.lastname: Zabihi
authors.affiliation: Department of Pediatric Oncology, Dokuz Eylul University, Izmir, Turkey.
authors.forename: Dilek
authors.initials: D
authors.lastname: Ince
mesh_terms: D001331:Automation; D004569:Electroencephalography / Q000145:classification* / Q000379:methods; D006801:Humans
keywords: EEG classification; Evolutionary classifiers; Morphological filtering; Seizure event detection