Automated patient-specific classification of long-term Electroencephalography

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Highlights

  • Novel EEG classification system for patient-specific classification of long-term EEG.

  • It aims to maximize the sensitivity rate with the minimum human feedback.

  • CNBC-E strives for accurate detection of all seizure frames in a generic way.

  • The adopted “Divide and Conquer” ability can truly take benefit of a large feature collection.

  • The average sensitivity and specificity rates achieved are 89.01% and 94.71%, respectively.

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.

Graphical abstract

The illustration of the proposed EEG classification system (top). The illustration of the evolution process of a CNBC (bottom).

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Keywords

EEG classification
Seizure event detection
Evolutionary classifiers
Morphological filtering

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