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.

Identification and monitoring of brain activity based on stochastic relevance analysis of short–time EEG rhythms

“…Therefore, we use five cepstral coefficients associated with δ , θ , α , and β rhythms, extracted as dynamic features(which were also used for EEG analysis in [29, 30]).…”

Automated Epileptic Seizure Detection Model using WPT, CFS and KNN based Multiclass TSVM

“…In this experimental work, the method detected 100% of Sensitivity, considering 6 numbers of patients. In [6], authors implemented a seizure identification technique for extracting various discrete domain features, where 25% of data considered as the training data for 21 numbers of patients. It was experimented on 18 numbers of channels.…”

Long-term epileptic EEG classification via 2D mapping and textural features

“…The results show that a CNN is able to achieve a sensitivity of 71% with zero false alarm, on 15 patients of Freiburg EEG dataset, in the prediction of seizures about 60 min before seizures onset in average. A generic and automated patient specific epilepsy detection system was proposed in Kiranyaz et al (2014) which used a large set of features (time, frequency, time-frequency and wavelet) to train an ensemble of binary classifiers. On CHB-MIT dataset (Goldberger et al, 2000), the system achieved 89% for sensitivity and 93% for specificity, trained on the first 25% record of each patient.…”

“…To cope with this problem and to suppress false alarms, most of the proposed epilepsy detection techniques in the literature randomly pick a large portion of EEG record as the training dataset which is infeasible in practice (Greene et al, 2008;Mirowski, Madhavan, LeCun, & Kuzniecky, 2009;Shoeb, Kharbouch, Soegaard, Schachter, & Guttag, 2011;Yan et al, 2015). In a recent study Kiranyaz, Ince, Zabihi, and Ince (2014), an automated epilepsy detection system was proposed utilizing a high dimensional feature set containing a large number of low-level features. The detection was performed by a network http://dx.doi.org/10.1016/j.eswa.2015.05.002 0957-4174/Ó 2015 Elsevier Ltd. All rights reserved.…”