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Machine learning (ML) has paved the way for scientists to develop effective computer-aided diagnostic (CAD) systems. In recent years, epileptic seizure detection using Electroencephalogram (EEG) data and deep learning models has gained much attention. However, in deep learning networks, the bottleneck is a large number of learnable parameters.

In this study, a novel approach comprising a 1D-Convolutional Neural Network (CNN) model for feature extraction followed by classical-quantum hybrid layers for classification purpose has been proposed. The proposed technique has only 745 learning parameters, which is the least reported to date.

The proposed method has achieved a maximum accuracy, sensitivity, and specificity of 100% for binary classification on the Bonn EEG dataset. In addition, the noise robustness of the proposed model has also been checked. To the best of the author’s knowledge, this is the first study to employ quantum machine learning (QML) to detect epileptic seizures.

Thus, the developed hybrid system will help neurologists to detect seizures in online mode.