Low-Complex TD-RBF and TD-SVM Seizures Predictors Based on EEG Energy and ECG Entropy



Low complexity classifiers are faced in this work for seizure prediction based on energy and entropy features requiring low computational costs. Time-Delay Radial Basis Function Neural Network (TD-RBFs) and Time-Delay Support Vector Machines (TD-SVMs) are used to study their potential since they have low computational complexity and a good generalization capability.

The EEG long-term energy (EEG-LTE) and ECG entropy (ECG-ENT) were computed in data from the 4th IWSP seizure contest. Up to eight data sets, corresponding to 8 recording hours, from Patient 1 were picked for this study. A qualified technician identified the focal channels and then EEG-LTE and ECG-ENT were computed. Each EEG-LTE and ECG-ENT sample is based on a window of 300 seconds, being the inter-window overlap of 295 seconds.
Once features were computed, a classifier was applied to differentiate among the possible two or four cerebral states (pre-ictal, ictal, pos-ictal and inter-ictal). A TD-RBF is a three-layer network, being the first layer a set of inputs related with the actual and past features and/or output information. A second layer formed by a set of neurons, and the last layer is a linear combiner. TD-SVM has similar structure to TD-RBF with the consideration of a threshold at the output.

Results and Discussion:
EEG-LTE based TD-RBF presented appropriate results for training in four hours and validation in one hour. Sensitivities above 60% and specificities between 48% and 95% were obtained. On the other hand, TD-SVM presented a slightly superior performance. ECG-ENT based TD-SVM were trained in seven hours and then tested in the remaining 1 hour, presenting 83% and 98% of sensitivity and specificity, respectively. These preliminary results show that further efforts should be devoted to these low-complex EEG/ECG features and non-linear classifiers since, for the used data, the obtained performance is very promising.


4th International Workshop on Seizure Prediction, June 2009

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