Seizure-specific wavelet (Seizlet) design for epileptic seizure detection using CorrEntropy ellipse features based on seizure modulus maximas patterns.

EEG signal analysis of pediatric patients plays vital role for making a decision to intervene in presurgical stages.

In this paper, an offline seizure detection algorithm based on definition of a seizure-specific wavelet (Seizlet) is presented. After designing the Seizlet, by forming cone of influence map of the EEG signal, four types of layouts are analytically designed that are called Seizure Modulus Maximas Patterns (SMMP). By mapping CorrEntropy Induced Metric (CIM) series, four structural features based on least square estimation of fitted non-tilt conic ellipse are extracted that are called CorrEntropy Ellipse Features (CEF). The parameters of the SMMP and CEF are tuned by employing a hybrid optimization algorithm based on honeybee hive optimization in combination with Las Vegas randomized algorithm and Elman recurrent classifier. Eventually, the optimal features by AdaBoost classifiers in a cascade structure are classified into the seizure and non-seizure signals.

The proposed algorithm is evaluated on 844h signals with 163 seizure events recorded from 23 patients with intractable seizure disorder and accuracy rate of 91.44% and false detection rate of 0.014 per hour are obtained by 7-channel EEG signals.

To overcome the restrictions of general kernels and wavelet coefficient-based features, we designed the Seizlet as an exclusive kernel of seizure signal for first time. Also, the Seizlet-based patterns of EEG signals have been modeled to extract the seizure.

The reported results demonstrate that our proposed Seizlet is effectiveness to extract the patterns of the epileptic seizure.