Epilepsy is a common neural disease among the children and in such cases, early and proper diagnosis is of paramount importance to en-sure a successful treatment. The signals of EEG in paediatrics are very unstable and noisy which complicates the process of automatic seizure detection in traditional systems. The paper presents a quantum-classical hybrid frame-work of the detection of epilepsy by EEG topographic map. EEG is processed in-to topographic representations on the scalp and de-composed in-to standard frequency bands. A light-weight convolutional neural network derives spatial features out of these maps and they are then classified by a Quantum Support Vector Machine with amplitude embedding. The framework can be used to evaluate paediatric EEGs, especially when compared to classical and quantum classifiers, which proves the potential of the framework.
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