A Novel Hybrid Classical- Quantum Network to Detect Epileptic Seizures
Sameer, M.; Gupta, B.
Show abstract
BackgroundMachine 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. MethodIn 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. ResultThe 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 authors knowledge, this is the first study to employ quantum machine learning (QML) to detect epileptic seizures. ConclusionThus, the developed hybrid system will help neurologists to detect seizures in online mode.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction 92%
- Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence 92%
- An Inexpensive Smartphone-Based Device and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes 91%
Similar papers in this journal
Similar papers in this journal
- Evaluating three different adaptive decomposition methods for EEG signal seizure detection and classification 95%
- A Transfer Entropy-based methodology to analyze information flow under eyes-open and eyes-closed conditions with a clinical perspective 94%
- SingleChannelNet: A Model for Automatic Sleep Stage Classification with Raw Single-Channel EEG 94%
Similar papers in this journal
- A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome 94%
- A Robust Spike Sorting Method based on the Joint Optimization of Linear Discrimination Analysis and Density Peaks 94%
- NLP-based tools for localization of the Epileptogenic Zone in patients with drug-resistant focal epilepsy 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.